Advances in Magnetic Resonance in Food Science (Special Publications)

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Advances in Magnetic Resonance in Food Science

Advances in Magnetic Resonance in Food Science

Edited by

P. S. Belton Institute of Food Science, Norwich, UK B. P. Hills Institute of Food Science, Norwich, UK G. A. Webb University of Surrey, Guildford, UK

RSmC ROYALSOCIETY OF CHEMISTRY

The proceedings of the Fourth International Conference on Applications of Magnetic Resonance in Food Science, held on 7-9 September 1998 in Norwich, UK

Special Publication No. 23 1 ISBN 0-85404-724-7 A catalogue record for this book is available from the British Library 0 The Royal Society of Chemistry 1999

All rights reserved Apart from any fair dealing for the purpose of research or private study, or criticism or review as permitted under the terms of the UK Copyright, Designs and Patents Act, 1988, this publication may not be reproduced, stored or transmitted, in any form or by any means, without the prior permission in writing of The Royal Society of Chemistry, or in the case of reprographic reproduction only in accordance with the terms of the licences issued by the Copyright Licensing Agency in the UK, or in accordance with the terms of the licences issued by the appropriate Reproduction Rights Organization outside the UK. Enquiries concerning reproduction outside the terms stated here should be sent to The Royal Society of Chemistry at the address printed on this page.

Published by The Royal Society of Chemistry, Thomas Graham House, Science Park, Milton Road, Cambridge CB4 OWF, UK For further information see our web site at www.rsc.org Printed and bound by MPG Books Ltd, Bodmin, Cornwall, UK.

Preface

The fourth International Conference on Applications of Magnetic Resonance in Food Science was held in Norwich, UK, between 7th and 9th September 1998. The meeting attracted 120 scientists from 20 countries and was thud a truly international occasion. The success of the Conference is reflected in the high quality of the oral and poster presentations which it attracted. This volume contains the material given in the oral presentations; the science covered in the fifty posters is additional. The Conference comprised major and minor oral contributions divided among five symposia which, taken together, ably demonstrate the protean nature of magnetic resonance techniques in dealing with problems arising in many areas of food science. The order of the chapters in this volume shows a parallelism to that in which the lectures were given at the Conference. Symposium A covered Magnetic Resonance in Food: The Developing Scene; the first four chapters relate to this Symposium. Symposium B dealt with Water, Ions and Small Molecules in Food; Chapters 5 to 10 relate to this material. The following eight chapters belong to the largest of the Symposia, Symposium C, which was devoted to Functional Constituents of Food. Chapters 19 to 21 are from Symposium D, which dealt with Signal Treatment and Analysis in Magnetic Resonance. The topics presented in Symposium E, relating to Applications of Magnetic Resonance to Food Processing and Engineering, are covered in the final five chapters of this volume. The Editors wish to express their gratitude to the authors for the prompt submission of their camera-ready copy manuscripts and to the production staff at the Royal Society of Chemistry for their kind co-operation in the genesis of this volume.

Contents

Magnetic Resonance in Food: The Developing Scene From Solid-Liquid Ratios to Real Time Tomography - The Development of NMR in Food Applications P.J. Lillford and S. Ablett

3

Time Domain NMR Studies under Controlled Shear Conditions S. Ablett, A. Darke and D. Martin

16

Internal Structure Characterization of Soft Cheeses by MRI F. Mariette, G. Collowet, P. Marchal and J.M. Franconi

24

Protein Aggregation Studies Using PFG NMR Diffusion Measurements W.S. Price, F. Tsuchiya and Y. Arata

35

Water, Ions and Small Molecules in Food A Multistate Theory of Water Relations in Biopolymer Systems B.P. Hills, C.E. Manning and J.Godward

45

Molecular Mobility of a System: Waxy Maize, Glycerol and Water Studied by NMR D.C.P. Jardim, J.R. Mitchell, W. Derbyshire, J.M.V. Blanshard and J.A.G Ar2as

63

Water Dynamics in Gelatine. Relaxation and Diffusion Analysis L. Foucat, A. Traore' and J.P. Renou

73

Probing the Physical and Sensory Properties of Food Systems Using NMR Spectroscopy S.J. Schmidt

79

'H Relaxation of Hydrated Carbohydrate Systems J.M. V. Blanshard, W. Derbyshire, W. MacNaughtan, S. Ablett, D. Martin and M.J. h a r d

95

Thermodynamics of Relaxation Phenomena in Freeze-dried Wheat Starch Gel S. Poliszko, D.M. Napierala, R. Rezlar and G. Hojjkann

105

Functional Constituents of Food NMR of Food Biopolymers P.S. Belton

115

...

Advances in Magnetic Resonance in Food Science

Vlll

Solid State I3C NMR Studies of Wheat High Molecular Weight Subunits A.M. Gil, E. Alberti, A. Nait6, K. Okuda, H. Sait6, A S . Tatham and S.Gilbert

126

The Application of Electron Spin Resonance Spectroscopy to the Detection and Transfer of Free Radicals in Protein-Lipid Systems N.K. Howell and S. Saeed

135

Editing the Information in Solid State Carbon-13 NMR Spectra of Food R.H. Newman

144

Cross-polarisation Kinetics and the Determination of Proton Mobility in Hydrated Plant Cell Walls M.C. Jarvis. M A . Ha and R.J. Vietor

158

Proton Relaxation in Plant Cell Walls and Model Systems H. Tang and P.S. Belton

166

Probing Molecular Motions of Low Moisture Starch Gels by Carbon-1 3 NMR Y. Vodovotz and P. Chinachoti

185

Applications of ESR Imaging in Food Science D.G. Gillies

193

Signal Treatment and Analysis in Magnetic Resonance Analysis of Time Domain NMR and Other Signals D.N. Rutledge, A S . Barros, M.C. Vackier, S. Baumberger and C. Lapierre

203

Comparative Chemometric Analysis of Transverse Low-field 'H NMR Relaxation Data I.E. Bechmann, H.T. Pedersen, L. N@rgaardand S.B. Engelsen

217

Quality Evaluation of Atlantic Halibut (Hippoglossus hippoglossus L) during Ice Storage Using 'H NMR Spectroscopy B. Sitter, J. Krane, I.S. Gribbestad, L. J@rgensenand M. Aursand

226

Applications of Magnetic Resonance to Food Processing and Engineering Magnetic Resonance Temperature Mapping A.G. Webb and J. B. Litchfeld

24 1

Study and Modelisation of Starch Gelatinisation in Potatoes with Magnetic Resonance Imaging C.A. Toussaint, F. Lungevin, J.-P. Pain and A. Goullieux

256

Contents

ix

Online Magnetic Resonance Imaging for Detection of Spoilage in Finished Packages T.W. Schenz, B. Dauber, C. Nicholls, C. Gardner, V.A. Scott, S.P.Roberts and M.J. Hennesy

264

Magnetic Resonance Mapping of Solid Fat Content of Adipose Tissues in Meat

272

A. Davenel, P . Marchal, A. Riaublanc and G. Gandemer

Time Domain 'HNMR: Its Relevance to the Processing and Storage of Starch Systems 280 I.A. Farhat, J.M. V. Blanshard and J.R. Mitchell Subject Index

289

Magnetic Resonance in Food: The Developing Scene

From Solid-Liquid Ratios to Real Time Tomography The Development of NMR in Food Applications P. J. Lillford and S. Ablett UNILEVER RESEARCH COLWORTH, COLWORTH HOUSE, SHARNBROOK, BEDFORD MK44 lLQ, UK

1 INTRODUCTION Most food materials are of complex chemical composition, heterogeneous structure, and reactive. i.e. just like the biological materials from which most of them arise. Food technology is the developing skill which allows all these variables to be understood, controlled and manipulated to produce nutritious, attractive, entertaining and, most of all, safe food. To understand and control anyhng requires that firstly measurements must be made, and it is easy to write down the information that is required to build a predictive model of the behaviour of any food product or process, (and processes not only involve the fabrication and assembly of products, but also their degradation during storage, use and consumption). We need to know: What are the molecules present, and how many of each? Where are they within the product? Are they stationary or moving, and if so at what speed? What are they reacting with? - and though it is reasonable to construct models to simplify the study of each of these questions, it would be ideal if we could have a single instrument, to measure all of them, in real time and on the immediate subject of interest. Fortunately, the food technologist is not alone in asking these questions. It is also what all medical researchers would like to know, and they have the added problem that their subjects of interest are not cheap or easily and frequently manufactured and dissected. So a non-invasive route to all of these measurements is also advantageous to all of us. Fortunately, Physical Science gave us the route to the solution of these problems over 50 years ago by the discover of nuclear magnetism, and the demonstration of nuclear magnetic resonance (NMR). The solution to all our measurement problems are encapsulated in two basic equations. Viz.

4

Advances in Magnetic Resonance in Food Science

M = ( N y 2h21(I+ l)B) l3kT

(1)

and

-T2= c

(

32+

52

+

1+W2Z2

1

1 + 422 02r2

The first tells us that if we can measure the net magnetisation of nuclei, they will be labelled by their specific magnetic moment (y), and we can count how many (N). Also, by controlling the external field (B), we can expose internal field shifts giving molecular information, or even gross position in space; and if all these were constant we could even measure temperature (T). The second tells us that the decay of magnetisation gives direct information on the molecular correlation times (T) and therefore movement of the nucleus and the molecule in which it is contained. All our measurement problems are over. It’s just a question of doing it! The real rate of progress in solving our measurement problems has been in the hands of electronic engineers,physicist and latterly computer scientists, who have identified how to extract the parameters and latterly how to reassemble complex data sets to provide the molecular and structural information we need. We still don’t have machines that can answer all our questions but before we complain we should now review what we have done with what they have already provided. I will approach this on an approximately chronological basis, but like most scientific developments, progress is rarely linear but transfers developmentsfrom one field to another on an entrepreneurial basis. 2 THE CONTINUOUS WAVE ERA

The early instrumentsused relatively low magnetic fields corresponding to I 60 MH, nuclear fiequencies for protons and used continuous wave radio fiequencies for excitation. There were two parallel developments, firstly in search of information on molecular structure - “high resolution” which operated well on solution spectra of mobile molecules. Liquid foods (and drinks) were open to study, and many of us, as undergraduates, first learnt the principles of chemical shifts and spin coupling from the spectra of alcohol and sugar solutions. The first protein spectra was published in 1957’. Certainly, by the 1960s Unilever was examining high resolution spectra of tea, only to find that molecular complexity rather than intrinsic resolution limited the interpretationof the spectra. Even 30 years later, with advanced in high field and pulsed Fourier Transform instruments, it is still not easy (see Figure 1).

Magnetic Resonance in Food: The Developing Scene

,

~

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7.8 7.6

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5

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,

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~

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Figure 1 Proton NMR Spectrum of a Black Tea Extract: Expansion showingpolyphenol Signals Broad band instruments were capable of identifying relaxation time differences of solids and liquids by analysis of lineshapes, with immediate impact on food formulation in the areas of fats and oils, and water. Most edible fats exist as mixtures of liquid and crystalline forms at body temperature so that the proportions of each can be measured directly from N M R lineshape (Figure 2).

t

Signal Intensity

I

Field-

Figure 2 Continuous Wave NMR Signalfiom a Sample Containing a Mixture of Solid and Liquid Phases.

.

~

6

Advances in Magnetic Resonance in Food Science

Table 1 A Comparison of the analytical D20contents of crisps with the concentration of

D20detected by NMR (-1969) Total D 2 0 content

D,O concentration detected i.e. bound water concentration

Solid-like water concentration

19.8 10.1 6.5 5.8

16.1 0.9 0.6 0.5

3.7 9.2 5.9 5.3

A simple method, allowing the quantification of amounts of solid and liquid fats as a function of blending source and temperature has been of enormous commercial value. It probably pays my salary. Observations of water in dried foods showed not dissimilar spectra. Some of the water appeared to be solid at temperatures well above its freezing point. Table 1 shows early results of deuteron studies in potato crisps. The ‘liquid’ water appears somewhere between 5 and 10% w:w, which is the same point where crispness is lost. It appeared that N M R could now measure textural properties and the search for ‘bound water’ was afoot. But continuous wave experiments were slow, signal to noise was awful and we needed to speed things up.

3 PULSED N M R STUDIES It is amazing to think that as late as 1968, Emst was still doubtful that pulsed nuclear excitation followed by Fourier Transform of the resulting decay signal would become a regular way of conducting NMR. His doubts related to the difficulty in collecting sufficient detailed data to produce high resolution spectra. But for ‘broad line’ practitioners there was no such problem. The collection of decay rates provided direct access to relaxation times (T, and T,) and was much faster and could be averaged. The CW instruments were superseded by pulse machines with simple averagers, and as many pulse sequences as one could afford or build oneself. Solid-liquid ratios became cheap and almost on-line, and the study of water in foods and model systems began in earnest. 3.1 “Water binding”

As early as 1954’, it was reported that the line width of water was increased by the presence of biological material (deoxyribonucleic acid). Broad line studies also showed that food gels and biological tissues had anomalously broad water peaks, and the simplistic explanation that all the water was more solid-like, bound or less structurally mobile was briefly advanced. This did not last long after the realisation that rapid exchange according to the Zimmermann-Britten model was the probable reason. i.e.

Magnetic Resonunce in Food: The Developing Scene

1

1 1 =x, -+(l-xB)-

T20bs

T2B

(3)

TZF

where XBrelates to the proportion of water “bound “ to substrates and TZobs, T,, and T,, relate to observed, bound and free water relaxation time. Furthermore, an estimate of XB could be envisaged by connecting with the observation that N M R saw a ftaction of water with reduced mobility, but not frozen, below the fleezing point. Solutes clearly affect the mobility of some water, if not all of it3. Real foods showed the same behaviour and firtherrnore the processes of rigor mortis and cooking were reflected in the water proton relaxation behaviour4.As data collection improved, so did the number of apparent water relaxation times, from 2 to 3 and up to 5, where the number of fittable parameters exceeded the reasonable number of domains where water could be thought to reside. An alternative approach, transforming the decay curve to a relaxation time spectrum was proposed’, where the origin of the complex decay can be related to the spatial heterogeneity of the sample over scales of lo’s of microns (Figure 3). Transverse proton relaxation in a 4.8%agarose gel:

!h

b. Freezethaw damaged gel

a. Homogeneous gel

i s”

40

Time (msec)

200

40

120

Time ( m e )

Schematic representation of the structure of agarose gel: a. Homogeneous gel

b. Freezethaw damaged gel

Figure 3 Spatial Heterogeneity and Dzfision Lengths

8

Advances in Magnetic Resonance in Food Science

The NMR machine can now be used as structure measurement tool, capable of measuring the effect of processes relevant to food processing. The approach has been investigated and developed extensively by Brian Hills et a16 and will be referred to in a later lecture. The relaxation time spectrum has also been correlated with the sensory impression of juiciness in the mouth. Not surprisingly, the water least influenced by the architecture of the food is released the most quickly. 3.2 Water droplets Many foods e.g. margarine, low fat spreads, dressing, are oil continuous emulsions, containing disperse droplets of aqueous solution. The stability, both microbiological and physical, and even the mouthfeel are dependent on the droplet size distribution. The insertion of controlled field gradients pulses within a normal echo train causes the refocussed signal to be dominated by the diffusion of water rather than its intrinsic relaxation time (Figure 4). If diffusions is restricted (by the boundaries of a dro let) then P anomalous diffusion is observed fkom which the droplet sizes can be estimated .

6

c)

z

b = exp{-y

2 2

6

AG

2

G D

(A-6/3)}

Y = Magnetogyric Ratio G= Magnetic Field Gradient D= Diffusion Coefficient

Figure 4 Pulse sequencefor determination of self diffusion 3.3 Diffusion in Foods

The interest in diffusion rates is not limited to emulsions. Essentially, the self diffusion coefficient of any small molecule in a matrix of any other is vital to the reaction rates of deteriorative processes, flavour retention, and migration between components. All

Magnetic Resonance in Food: The Developing Scene

9

of these can be measured by the pulsed field gradient method. Figure (5) shows a recent and surprising result, that the water within a “glassy” polysaccharide still exhibits considerable diffusive motion, and is quite independent of the glassing of the polymer itsel?.

D (m2/sec) 10-10 L

* * --

lo-”

0

10

20

30

40

50

60

70

80

90

I

100

Temperature (C)

Figure 5 SelfDiffusion Coeficient of the Water in 81 % Pullulan 4 FOURIER TRANSFORMED HIGH RESOLUTION SPECTRA

Emst’s early worries were unfounded. As computer power developed, and NMR spectroscopists found clever ways of enhancing signal to noise, FT-NMR became the dominant experiment and allowed amazing results to be delivered. As early as 1970, Stahl and McNaught showed that with a little hydrolysis to reduce viscosity, NMR could be the most effective method of analysing starches and chemically modified food ingredientsg. Access to natural abundance CI3 spectra made even further possibilities available, such as this detailed assignments of hyaluronate polymer resonance. Chemical shift and line width changes can be quantitatively measured as association or conformational changes take place. Wiithrich reported the first N M R structural determination of a protein in solution in 1985”. All of these advances now allow structural studies of macromolecule unfolding to be measured under conditions relevant to commercial processing. Developments in the medical field showed that 31Pspectra could be obtained in whole cells and organs”. This was seized upon by the food industry to monitor pre to post rigor changes in most muscle tissue, and the location and turnover of phosphates added to enhance water retention in processing and freezing. All these high resolution phenomena rely on sufficient molecular mobility to allow resonances to be observed. But many food systems contain real solids, crystalline

10

Advances in Magnetic Resonance in Food Science

materials or polymers associated to form effectively solid state motional restriction - but help was at hand. Since 1958, Andrew and co-workers had been “cheating” by high speed spinning of solid samples at the magic angleI2. As soon as commercial spectrometers became available, the food industry was on board, producing high resolution spectra of crystalline polymorphs of triglycerides, which had previously been lost in the broad line signalsI3 (Figure 6)

170.0

140.0

110 0

80.0

50.0

20.0

0.0

PPM

Figure 6 CP/MAS Spectra from Tripalmitin

But whilst this cadre of workers were scheming to increase resolution, others were playing about with the magnetic field.

5 NMRIMAGING In 1973, Paul Lauterbur published an image of 2 glass tubes by sweeping the external magnetic fieldI4. For those of us doing NMR, it was such an annoyingly clever experiment that we wished we had thought of it first, and several of us repeated it, just to prove that we could! I was lucky enough to be working with Nottingham at the time of the enormous advances made by Mansfield, Andrew et a1 and seeing the first image cross section of Waldo Hinshaw’s finger, and then his wrist, and then other bits of people as the magnets got bigger. Foods found their way into the magnets as fast as we could borrow time on the machines, and the first image of food was shown in 1980 at the Royal S~ciety’~. The publication was not ours, but the apple pie was (Figure 7).

Magnetic Resonance in Food: The Developing Scene

Figure 7 NMR image of Apple Pie

Early work simply showed that images were possible, and that changes such as water migration within composite structures could be measured as a function of time or temperature (Figure 8).

Figure 8 NMR images of Egg Custard - aft.. baking

11

12

Advances in Magnetic Resonance in Food Science

The images rapidly improved in quality but what did they mean and how could they be used? The relative intensity of signals was derived from a combination of proton density and relaxation time. Provided one could examine the specimen by visual means, the results were interpretable, but this largely defeats the objective of non invasive testing. Fortunately, the problem was recognised and shared with medical imaging, and food science gave something back to medicine via construction of calibration samples of varying proton density and relaxation time’6. Nowadays, image sequences weighted for density, relaxation time and even diffusion rates are reasonably well understood. After the heady days of image collection, questions began concerning the real value of imaging in foods. Taking photographs of structures is not enough. The technique must provide information not otherwise obtainable, or it would be seen as an expensive luxury. We must answer the basic questions posed in the Introduction to this paper. The ability of NMR to measure dynamic processes in 3-dimensions provides one valuable and novel piece of information. Early experiments used proton density change as the indicator of water flow. Such experiments have been extended to the macroscopic visualisation of whole products (Figure 9), and at the microscopic level of wheat grains (Figure lo), allowing mathematical modellin of heat and mass transfer processes to be matched to the actual flows mapped by NMR The capability of spatial labelling by imaging has allowed the development of “velocimetry” by NMR18. This is particularly important for the direct measurement of flow in Non-Newtonian liquids frequently encountered in food materials, which is important in the measurement of constitutive behaviour (rheology) (Figure 11). And the direct imaging of complex flows in complex geometries (Figure 12)

5’.

Figure 9 Rehydration of Dried Pasta

Magnetic Resonance in Food: The Developing Scene

13

Figure 10 Corrected NMR images of moisture contentfor grains boiled in distilled water.

10

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14

Advances in Magnetic Resonance in Food Science

Figure 12 Axial Velocity Maps This means that the developing capabilities of Computational Fluid Dynamics can be challenged by experimental measurement of actual flows in the same complex environments. This should provide some healthy competition between modellers and measurers for a few years. 6 THE WAY FORWARD

So where do we go from here. Firstly, in “conventional” spectroscopy, we need further developments to obtain molecular information of both the complex mobile components but also the solid-like or real solid elements in heterogeneous foods. Fortunately, the developments in magic angle spinning (MAS) allows reduction of both the motional and susceptibility induced broadening, so that high resolution spectra are becoming available either by the simple addition of MAS, or the combined techniques of spinning and cross polarisation (CPMAS). In imaging, there is still the requirement for even higher spatial resolution and shorter timescales. We still cannot achieve the quality of answers to questions originally set in this paper. Even with the current capabilities there are new opportunities only recently

15

Magnetic Resonance in Food: The Developing Scene

identified. Who would have believed that N M R could measure the brain’s response to stimuli likely to be induced by the appearance, smell and taste of food? The developments in imaging food manufacturing processes suggests that the eating and digestion of food is equally amenable to real time study, with real foods inside whole people. We will continue to depend upon the skills of instrument designers and the financial input from medical research. But we must continue to “steal with honour” from all available sources. NMR still has a lot more to offer. ACKNOWLEDGEMENTS The chronology of N M R developments has been described in much greater details elsewhere. Interested readers are recommended to study the excellent chapters in Progress in Nuclear Magnetic Spectroscopy, 28,(1995). Thanks to all the N M R specialists who have taken the brave step of examining foods. Not all have been referred to here, but they know who they are.

References 1. 2. 3. 4.

5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.

z,

M. Saunders, A. Wishnia and J.C. Kirkwood, J. Amer. Chem. SOC.,1957, 3289. B. Jacobsen, W.A. Anderson and J.T. Amold, Nature, 1954,173,772. I.D. Duff, PhD. Thesis, University of Nottingham, Oct. 1973. for example: P.S. Belton, R.R. Jackson and K.J. Packer, Biochim. Biophys. Acta.,1972,286, 16. C.F. Hazlewood et al. Biophys. J., 1974,14,583. P.J. Lillford et al. ACS Symposium Series, 1980, 127. A.H. Clark and P.J. Lillford, J. Mag. Res., 1980,3,42. B.P. Hills, S.F. Takacs and P.S. Belton, Food Chem., 1990,37,2,95. K.J. Packer and C. Rees, J. Colloid Int, Science, 1972,4,2,206. and J.C. van den Enden et al, 1.Colloidfnt. Science, 1990,140. 1, 105. S. Ablett, A.H. Darke, M.J. Izzard and P.J. Lillford, in “The Glassy State in Foods” ed. J.M. Blanshard, P.J. Lillford, Nottingham Press, 1993, 189. H. Stahl and R.P. McNaught, Cereal Chem., 1970, flz, 345. M.P. Williamson, T. Have1 and K. Wuthrich, J. Mol. Biol., 1985,182, 195. D.I. Hoult et al., Nature, 1974,252,285. E.R. Andrew et al., Nature, 1958,182, 1659. S.M. Bociek et al., J.A.O.C.S., 1985,62,8, 1261. P.C. Lauterbur, Nature, 1973,242, 190. S.F.J. Cox et al., Phil. Trans. Roy. SOC.,1980,289. 1037. P.M. Walker et al, Phys. Med. Biol., 1989,2, 1,s. A.G.F. Stapley et al., Int. J. ofFood Sci. and Technol. 1997,2,355-375 For example: I.T. Callaghan and Y. Xia, J. Mag. Res., 1 9 9 1 , a , 326. B. Newling et al., Chem. Eng. Sciences, 1997,2, 13,2059.

Time Domain NMR Studies under Controlled Shear Conditions Steve Ablett, Arthur Darke and Dave Martin UNILEVER RESEARCH COLWORTH, COLWORTH HOUSE, SHARNBROOK, BEDFORD MK44 ILQ, UK

1 INTRODUCTION

Time domain NMR is now a well established techtuque within many Food Science laboratories for the characterisation of food microstructure. These range from simple routine measurements of the solids content of fat blends’, through to the more complex analysis of water relaxation decays to probe the microstructure present within foods293. Many food processes involve the application of shear to generate the required final product structure, and time domain NMR has proven to be a powerhl technique for characterising the changes that have taken place. However, these N M R measurements have to be performed off-line, which means it is only possible to characterise the initial and final structure produced. The ability to make these types of NMR measurements directly under shear conditions would be beneficial because it would allow the dynamics of the microstructural changes to be directly monitored. Shear cell devices have previously been constructed for N M R spectrometers, but these have primarily been used to study the flow patterns of rather than for bulk NMR measurements to characterise any shear induced microstructural changes. In addition, these measurements have tended to have been undertaken on specialised MRI equipment which most food scientists would not have ready access to. In this paper we describe the design of a shear cell, a simple addition to a standard commercial benchtop spectrometer, which allows the dynamics of shear induced structural changes in fluid samples to be directly investigated. The application of this device is illustrated by demonstrating how it can be used to study the effect of shear on the crystallisation kinetics of edible fats, and on the gelation properties of what are referred to as biopolymer ‘fluid gels”.

2. DESCRIPTION OF THE SHEAR CELL

A concentric cylindrical couette type device has been designed and constructed for use with a Resonance Instruments MARAN benchtop spectrometer. It has been designed to operate inside a standard NMR tube which required no major modifications to the spectrometer, allowing the cell to be easily installed without compromising the performance of the spectrometer when it is required for other applications. The cell has

17

Magnetic Resonance in Food: The Developing Scene

been constructed using a standard 9 inch long 18 mm O.D. precision glass NMR tube (Wilmad code no.18-PP-9), together a 13.55 mm O.D. central cylinder made of a plastic material (PEEK), giving a sample gap of 1.5 mm. The bottom of the N M R tube is fitted with a glass and ceramic bearing to keep the inner cylinder central. The design of the shear cell is shown schematically in figure 1, and a photograph of the actual cell components is shown in figure 2. The inner cylinder is driven by a standard laboratory motor (Jake & Kunkel type RW 20 DZM), which together with a 1 O :l step down gear box permits rotation speeds of 5 rpm to 400 rpm to be readily achieved, allowing shear rates in the range -3 sec-' to 230 sec-' to be applied to the fluids. The shear cell is clamped to the top of the magnet box, and a frame has been constructed around the magnet box to hold the motor firmly in place relative to the shear cell (figure 3). Temperature control is within the range -20°C to 1OO"C, with a typical sample volume of 5 ml.

u

R

MotorDrive

Rotating Inner Cylinder

Nh4R Tube (18mm OD) Sample (1.5mm gap)

C- Magnet Pole Piece

Bottom Bearing

Figure 1

Schematic of the design of the shear cell.

Advances in Magnetic Resonance in Food Science

Figure 2 Photograph of the components of the shear cell showing the standard MMR tube Jitted with the bottom bearing, the inner plastic cylinder, and the top bush to attach the cell to the magnet.

Figure 3 Photograph showing the attachment of the motor and the shear cell to the spectrometer magnet.

Magnetic Resonance in Food: The Developing Scene

19

3. RESULTS AND DISCUSSION 3.1 Characteristics of the Shear Cell

The characteristics of this device were evaluated using proton relaxation studies of both pure water (T2-2sec) and water doped with gadolinium chloride (T2-5Omsec) to establish if the cell itself, or the shear induced flow of the sample, caused any artefact to the recorded NMR signal. These two test samples were chosen since they were considered to represent the typical range of relaxation times to be studied by this method. 3.1.1 Effect of the Shear Cell on the NMR Signal. The central cylinder is machined fiom PEEK which gives a strong proton signal with a very short relaxation time which completely decays within -70 ps. The close proximity of the motor causes small fluctuations in the magnetic field homogeneity which also affects the FID. This means the device is not suitable for recording FID's, i.e. for studying samples with very short relaxation times. However, no background signal from the shear cell was detected with the Carr Purcell Meiboom Gill (CPMG) pulse sequence', and the operation of the motor did not have any detectable effect on this signal. 3.1.2. Effect of Applied Shear on the CPMG signal. CPMG pulse sequences are routinely used in time domain NMR studies to eliminate the effect of molecular diffusion on the NMR signal. The amplitude of an echo in the CPMG pulse sequence is given by-

A(echo at time t) oc exp[-(t/Tz) - 0.33y2G2Dz3] where G is the magnetic field gradient due to magnet inhomogeneity D is the diffusion coefficient z is the 90"- 180' pulse spacing (i.e. echo spacing =22). The value of z is chosen to be sufficiently short such that the second term in equation 1 is negligible, aflowing the value of T2 to be determined directly fiom the CPMG decay. However with this shear cell, in addition to normal diffusion processes there is also forced fluid flow taking place. This could increase the magnitude of the second term in equation 1 such that it is no longer negligible. The effect of changing the 5 spacing was evaluated for both the pure water and the doped water over the range of z values typically used (50 KS < r < 250 ps). It was found that the application of shear had no significant effect on either the measured amplitude or the relaxation time for both samples studied over the range of z spacings investigated. The effect of the rotation speed of the central cylinder was also investigated. No significant effect on the recorded CPMG signal was observed for both the pure water and the doped water samples over the range of rotation speeds (0 to 100 rpm) investigated. It was concluded that the second term in equation 1 remains negligible despite the increased motion caused by the application of shear, and therefore the application of shear introduces no measurable artefacts to the recorded CPMG decay.

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3.2 SolidILiquid Fat Measurements One of the widest applications of bench top NMR spectrometers is the determination of the solid/liquid ratio of fats'. The N M R signal of a fat typically contains two components, a very rapid decay from the crystalline component and a much slower decay from the liquid component. Two different approaches have been developed for the routine analysis of the solidhquid ratio of fats'. The so called 'direct method' calculates the solids content directly from the FID signal. The amplitude of the signal relating to both the solid and liquid components is measured close to the start of the FID, and the amplitude of just the liquid component is recorded some 60 ps later when it is assumed the signal from the solid component will have completely relaxed. The solids content is then calculated directly from these two amplitude values. In contrast to this approach, the 'indirect method' only measures the amplitude of the liquid component of the NMR signal at the temperature of interest. This amplitude is then compared to the increased signal amplitude recorded at a higher temperature where it is known that the fat will be totally liquid. A reference sample of oil which remains totally liquid over the whole temperature range of interest also needs to be measured to compensate for the temperature dependence of the NMR signal amplitude. The solids content of the fat is then calculated using the following equation %Solids = 100[ ~-(R~&I)(S,I/S,O)]

(2)

where t 1 is the measurement temperature tO is the temperature at which the sample is 100% liquid R is the signal amplitude of the reference oil S is the signal amplitude of the sample. Both of the two above methods are widely used throughout the Food Industry to evaluate the solids content of fats. They are primarily used to construct phase diagrams for the fats which are subsequently used to help with product formulations. However, it is well known that fats can readily supercool, in which case, supplementary information on the rate of crystallisation is also likely to be required. Fortunately, the speed of the NMR measurement is sufficient short (i.e. a few seconds) that in most cases, this non equilibrium solids content can be determined. However, many food products are processed under shear conditions which could potentially moditjl the crystallisation rate of fats. For this situation, the measurement of the solids content while shear is directly applied to the fat is ideally required. It has already been shown that this device is not suitable for studying FID's so the direct method cannot be used, However the indirect method can be used to study the crystallisation behaviour of a fat, by measuring the initial amplitude of the liquid component using the CPMG pulse sequence. A fat sample (hardened palm kernel oil) was initially heated to 50°C in the shear cell to ensure it was completely melted, and then slowly cooled from 30°C to 15°C at a rate of l"C/minute. The CPMG signal was recorded every minute (i.e. at each 1°C step), initially for the sample under static conditions (i.e. 0 rpm), and again with the sample under shear conditions (i.e. 400 rpm). This measurement protocol was then repeated with pure sunflower oil to allow the corresponding reference oil signal amplitudes to be acquired. The NMR signals were fitted to a single exponential function to allow the initial amplitude of the liquid component present in the sample to be determined. The solids contents were

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calculated using equation 2, and the results are shown in figure 4. It can be seen that the

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25

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15

Temperature IC

Figure 4 Solid fat content of hardened palm kernel oil as a firnction of decreasing temperature (I Qminute) with (crosses) and without (open circles) applied shear. 3.3 Biopolymer Fluid Gels

Biopolymers have been used for many years by the Food Industry for their gelling properties which impart textural improvements to food products. More recently it has been shown that novel properties can be achieved with these materials when shear is applied during the gelation process’. This produces what are referred to as ‘fluid gels’. The action of shear during gelation causes very small gel particles to be formed whose dimensions are typically on the micron scale. This results in the final material having no long range network structure, and so produces a pourable colloidal type liquid instead of a solid like gel which would have normally been produced under static conditions. Agarose has been extensively studied as a model for biopolymer gelation, and it has been established that the gelation mechanism is via the formation of a three dimensional network of aggregated double helices. Previous NMR studiesg on agarose have shown the water relaxation time (Tz) to be particularly sensitive to the degree of aggregation present and is a powefil tool for probing the dynamics of the network formation during gelation. The application of NMR to the study of biopolymer fluid gel formation is severely limited because it is not possible to study this process directly within a conventional NMR probe. Previously measurements have had to be restricted to the initial and final structure formed, but the development of this shear cell provides the potential for directly studying the complete dynamic process of fluid gel formation. The NMR signal from 3% agarose was recorded using the CPMG pulse sequence as a function of temperature from 70°C to 28°C at a cooling rate of 1°C/minute and then held at 28°C for a further 30 minutes. Both static and shear conditions (i.e. 100rpm) were

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investigated, and the results are shown in figure 5 . This shows the kinetics of structure formation within the fluid gel to be the same as those of the normal quiescently formed gel. However, the final Tzvalue of the water within the fluid gel is significantly longer than the corresponding value Erom the quiescent gel. This suggests some microstructural modification within the fluid gel particles compared that that of the quiescent gel, although the origin of these structural difference is unclear at present. It should be noted that similar results to these have also been observed using another cylindrical couette type shear cell inside a MRI spectrometer"

loooo 1000

I------

. v)

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Figure 5 Tz relaxation time of the water in 3% agarose as a function of temperature with (crosses) and without (open circles) applied shear. The solution was cooled (I Wminute) from 70 97 to 28 T and held at this temperaturefor afirther 30 minutes. 4. CONCLUSIONS

A shear cell has been designed and constructed for use with a low cost commercial bench top Nh4R spectrometer. It operates inside a standard NMR tube, allowing easy installation without compromising the overall performance of the spectrometer when it is required for other applications. It has been shown that no detectable artefacts are introduced into the NMR signal using the CPMG pulse sequence, either from the shear device itself or from the flow of the sample which results from the applied shear. The potential of this type of shear device to the Food Industry has been demonstrated by showing how it can be used to follow shear induced effects to both the crystallisation behaviour of fats and the dynamics of biopolymer fluid gel formation.

5 , ACKNOWLEDGEMENTS

The authors wish to thank Mr C. Marriott of the Engineering Support Group (Unilever Research Colworth) for the design and construction of the shear device.

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References

K. Van Putte and J. Van den Enden, J. Amer. Oil Chem. Soc., 1974,51,316. P. J. Lillford, A. H. Clark and D. V. Jones, ‘Water in Polymers’, Washington DC, 1980, ACS Symp. Series No. 127, p177. 3. P. S. Belton and R. G. Ratcliffe, Prog. NMR Spectrosc., 1985, 17,24. 4. P.T. Callaghan and Y. Xia, J. Mug. Rex, 1991, 91, 326. 5. R. L. Powell, J. E. Maneval, J. D. Seymour, K. L. McCarthyandM. J. McCarthy, J. Rheol., 1994, 38, 1465. 6. S. J. Gibbs, D. Xing, T. A. Carpenter, L. D. Hall, S . Ablett, I. D. Evans, W. Frith and D. E. Haycock, J. Rheol., 1994,38, 1757. 7. I. T. Norton, T. Foster and C. R. T. Brown, ‘9th International Conference on Gums and Stabilisers for the Food Industry’, Wrexham, 1997. 8. S. Meiboom and D. Gill, Rev. Sci. Inst., 1958, 29, 688. 9. S. Ablett, P. J. Lillford, S . M. A. Baghdadi and W. D. Derbyshire, J. Colloid Interface Sci., 1978, 67, 355. 10. A. L. Hanlon, PhD Thesis, University of Cambridge, 1998. 1. 2.

Internal Structure Characterization of Soft Cheeses by MRI F. Mariette,' G. Collewet,' P. Marchal' and J. M. Franconi2

'

CEMAGREF, TECHNOLOGY DIVISION, 17 AVENUE DE CUCILLE, CS 64427,35044 RENNES, FRANCE * SIEMENS SA, 3 9 4 7 BOULEVARD ORNANO, 93527 SAINT DENIS, FRANCE

1 INTRODUCTION Structure is a relevant characteristic of food products. It is often related to texture and flavour but also stability during storage. Consequently the structure is very important for those involved in the chain of production, fiom farmer to consumer'. The structure of soft cheeses can range from hard to soft, almost semi-liquid as the Camembert cheese. Moreover the structure can be highly heterogeneous inside a single product. All these aspects increase the difficulty to develop instrumental techniques for structure characterisation. If numerous works have been published for micro structure description, relatively few works have been done for macro structure description. To achieve this goal, Magnetic Resonance imaging (MRI) technique presents numerous advantages. The object can be studied in a non-invasive and nondestructive way. Structural information can be obtained such as air or liquid pockets, and cracks. Moreover information can be obtained on chemical composition and molecular dynamic. Since the last decade the applications of the MRI technique in the field of food science have been increasing. Numerous review papers have been written2933.Applied on dairy food MRI has been evaluated to study hard cheese openings formation and di~crimination~.~.~, dairy gels shrinkage' and internal fat and moisture measurements*. The objective of our work was to discriminate soft cheeses texture from their structure measured by MRI. Numerous MRI sequences were defined and the MR images obtained were analysed.

2 MATERIAL AND METHODS 2.1 Cheese Collection The soft cheese collection was provided by Bongrain company. The cheeses were selected in order to investigate a wide structure and texture range variation. Six different cheese technologies were studied : a) one ultrafiltrated (UF) technology b) one thennophilic (THERMO) and c) four mesophilic (MESO I, MESO 11, MESO 111, MESO IV). For each technology 6 cheeses from different batches were analysed. Two mesophilic technologies (MESO I and MESO 11) were characterized by an heterogeneous structure formation. To take this effect into account, a kinetic procedure was proposed. This kinetic procedure consists in making two

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measurements, one when the cheeses were young and one at the end of life. Consequently the cheese collection was composed of 48 cheeses.

2.2 MRI Data Sets MR images were acquired using a 0.2 T MR scanner (Open System, Siemens) with a head coil. All the images were acquired in the plane parallel to the longest cheese surface using a two-dimensional (2D) Fourier transform technique. Three different pulse sequences were used : a) a standard spin-echo (SE) sequence to produce proton-density (PD) , T2-weighted (T2) and TI-weighted (TI) images, b) a gradient echo (FLASH) sequence to produce magnetic susceptibility-weighted images and c) a multi-echo (CPMG) sequence to calculate T2 parametric images. Three sets of images were obtained from the SE pulse sequence , proton density weighted images (TE =17 ms and TR = 1500 ms), Tz weighted images (TE = 80 ms and TR = 1500 ms) and TI weighted images (TE = 15 ms and TR = 3000 ms). The parameters of the FLASH sequences were TE = 23 ms, TR = I500 ms. Three slices were acquired per cheese, with a field of view (FOV) = 180 * 180 mm, slice thickness = 4 mm and a 256 x 256 pixel matrix. The multi-echo sequence parameters were TE=17 ms, 24 equidistant echoes, TR= 1500 ms, slice thickness = 4 mm and a 128 x 128 pixel matrix. One T2 parametric images was acquired per cheese. For each sequence the gain and FFT scale parameter were kept constant. The T2 relaxation times were calculated according to the Marquartd algorithm using a monoexponential fitting model. All the MRI measurement were performed at 16°C. 2.3 Image Analysis

Three different kinds of information were extracted from the MR images: T2 value distribution, opening characteristics and information on image heterogeneity 2.3.1 Tz Distribution. The T2 map analysis was achieved from the T2 histogram representing the T2 value from 1 to 100 ms and the amount of pixel at a specific TZvalues. The histogram frequency is normalised by the total pixel number to prevent any distortion of the histogram from the cheese MR image size. 2.3.2 Opening Characteristics. The openings were segmented in each proton density image using an adaptive thresholding (see an example in Figure 1). For each opening the following characteristics were computed : the surface S, the perimeter P (computed with the Crofton formulae), the compacity C = P2/4*ll*Swhich is equal to 1 for a perfect circle and decreases with the complexity of the shape, the distance D to the nearest opening neighbour. Seven characteristics were extracted from the distribution histograms of S, P, C and D (minimum, maximum, mean, standard deviation, variance, skewness and kurtosis values). Finally, for each cheese the ratio R of the total openings surface to the cheese surface and the number N of openings per surface unit were computed. The three slices of each cheese were collected.

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Figure 1 From lefr to right :original proton density and segmented openings 2.3.3 Textural Analysis. In order to quantify the image heterogeneity, textural features were computed. The image texture is the visual information characterized by pixels grey levels properties and their spatial relationships to each other. Texture classification for image segmentation or products characterisation is used in many applications fields such as medical imagery, remote sensing and products inspection or classification8~93'0~''~'2. Within the several approaches to compute textural feature^'^"^, the one proposed by Haralick" was found to be the most powerful to describe texture in generalI6. This method gives 13 parameters extracted from a statistical study of grey level variations between one pixel and its neighbour at distance d in direction 6 (Table 1). Among these parameters some are easily linked with images features such as the angular second moment (or energy) which measures the homogeneity in the image, the contrast and the inverse different moment are related to the local variations in the image, the entropy measures the randomness of the grey levels, the correlation corresponds to the grey level linear dependencies in the image. We computed the 13 parameters with d=l in all directions. These parameters were computed on proton density, TZ weighted and magnetic susce tibility weighted images. The images were first filtered with a Nagao type . filter to improve signal to noise ratio. More, 7 parameters (minimum, maximum, mean, standard deviation, variance, skewness and kurtosis values) were extracted from the grey-level histograms.

I7

Sum Variance Correlation Variance

Entro Difference Variance Difference Entro

no9 nol 1

Sum average

Table 1 Textural parameters

Principal component analysis (PCA) and discriminant 2.3.4 Data Analysis. analysis (DA) included in the Stalab software (SPL infoware, Paris) were used. The data were subjected to PCA to identify data structure and to achieve dimensionality reduction. The PCA method expresses the variable vector as a linear combination of a set of orthogonal (uncorrelated) vectors called principal components (PC). The first principal component is chosen to account for the largest possible fraction of the total variance. Each successive principal component is then chosen to account for the largest possible fraction of the remaining variance. The correlation of the variables, or of the samples and the principal components were assessed and are helpful to

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identify data structure. Then a discriminant analysis (DC) was performed on principal component to separate the groups in multidimensional feature space. The misclassification rate was estimated with the cross validation leave-one-out method.

3 RESULTS

Figure 2

Proton density weighted images. From top to bottom, left to right : UF, THERMO,young MESO I, old MESO I, young MESO I1 and old MESO 11, MESO Ill, MESO N.

Figure 2 shows examples of proton density weighted images of the 8 types of cheese.These cheeses present significant differences in many points. young MESO I and young MESO I1 have different grey level in the centre and near the rind while the other cheeses are more uniform. The number of openings is quite different from UF with no openings at all to MESO IV with a lots of openings and THERMO with few openings. MESO 111 and MESO IV present very large openings contrary to old MESO I or old MESO I1 for example. Figure 3 shows the effect of MR parameters on the young MESO I MR images contrast. Note the great effect of TE on the contrast between the non mature and more mature area while the FLASH sequence exalted the openings because of the magnetic susceptibility effects between curd and openings.

Figure 3 From left to right :MR images from proton density, T2 weighted and FLASH sequence on a young MESO I cheese.

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3.1 T2 Histogram Analysis An example of T2 histograms is given in Figures 4 and 5. According to the cheese technology the T2 distribution varied from a narrow distribution for homogeneous cheese such as the ultrafiltrate one to a bi-modal distribution for the heterogeneous cheese such as young MESO I.

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Figure 4

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T2 histogram of UF cheese Figure 5 T2 histogram ofyoung MESO MR image I MR image

The mean T2 value variation was between 30 to 65 ms. In order to identify data structure between T2 histogram a PCA was performed. The results showed that the total data variation was distributed among many principal components. For example the first two principal components PC 1 and PC 2 explained only 45.44 % of the total variation. These resuIts could be explained by two concomitant factors. The first factor was related to the non linearity of the data. A change of Tz mean value or a change of the histogram dispersion was converted through the PCA method into a plane. The second factor resulted from the intra-variation of a T2 histogram from a single cheese compared to the total data variation. Despite these effects main tendency could be pointed out. To identify relationships between the principal components and the T2 histogram data, both the correlation coefficient and the eigenvectors were calculated and plotted as a function of the T2 values (Figure 6).

Figure 6 Correlation coeficient (-) and eigenvectors (a) of the three first principal components The most important variables in PC 1 were the small T2 values around 20 ms and the high T2 values around 70 ms. The small TZ value presented a negative eigenvector, while the high T2 values showed a positive eigenvector. So PC 1 opposed the cheese characterised by small T2 to those with high T2 value. The second principal component, PC 2 which represented 11 % of the total variation was mainly correlated (r2 = 0.45) to the T2 value centred at 50 ms. In the same way the third principal component, PC 3 explained 9 % of the total variation and was positively correlated to the T2 value centred at 40 ms and negatively correlated to the T2 value

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centred at 55 ms. Compare to PC 1 which explained large T2 variation between cheese T2 histogram, PC 2 and PC 3 explained small T2 changes. The representation of each cheese T2 histogram on the factorial 3D map is given in Figure 7.

pcl

I

6 FCl

Figure 7 Principal components analysis Figure 8 Discriminant analysis of T2 of T2 histograms histogram after variable number reduction by PCA From the three first PC it should be possible to distinguish some cheese groups associated to the T2 distribution. For example the both MESO I ,young and old, the young MESO 11, the UF and the T H E M 0 cheeses. While MESO I11 and old MESO I1 cheese T2 histograms seemed more confhsed with the others. Note also the large individual dispersion which should be related to the difficulty to keep a constant cheese chemical composition for all the batches. However the PCA subspace representation showed that a good discrimination between cheese could be achieved. A discriminant analysis was carried out. Because of the large number of T2 variable, 100 T2 variables, compare to the 48 cheese MR images, the discriminate analysis was performed from the reduced variables obtained by the PCA method. This dimension reduction increases the discriminant analyse reliability . So the first ten principal components were selected. Learning sets and test sets composed of one cheese T2 histogram per cheese were built and the discriminant analysis was applied several times. From this method the misclassification rate estimated from the learning sets was very low, around 10 %. The Figure 8 represents the factorial map composed by the two first discriminant components, DC 1 and DC 2. Some cheeses were well separated such as UF, MESO 11, MESO IV and THERMO. While, MESO I and MESO I11 were disperse and were not well discriminated from the previous ones. The misclassification rate estimated from the test sets increased to 33 %. This effect was consistent with the PCA results obtained. The T2 values of the cheeses were very sensitive to the batch chosen. The difficulty to obtain a homogeneous cheese population resulted from chemical composition and ripening effects. These two effects will induce large internal structure modifications which have a great effect on the relaxometric behaviour of the cheese protons dynamic. Therefore the discrimination is very efficient on the learning sets but become less efficient on test sets.

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3.2 Opening Data Analysis Compared to hard cheeses such as Emmental which are characterised by large and regular openings, soft cheese openings could offer a wide distribution of size and shape. Figure 9 shows the frequency distribution of the size and compacity of openings for one old MESO I1 cheese and for one MESO 111 cheese (the compactity is given only for the openings with a surface greater than 4 pixels i.e 1.96 mm', value under which this measure is not reliable). The old MESO I1 cheese has many small openings with regular shape (high value for compacity) while the surfaces for the MESO 111are more widely spread as well as the compacities. MESO 111 Compacitiesdistribution 0.3 7

MESO I1 old Compacitiesdistribution

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1 2 3 4 5 6 7 8 91011 Compacity * 10

MESO 111 surfaces distribution 0.4 I

MESO II old surfaces distribution

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1 3 5 7 9 11 13 15 17 1921 surfacen (mm'mm)

1 3 5 7 9 11 13 15 17 1921 surfacel2 (mmlmm)

Figure 9 Compacities and surfaces distributionfor old MESO II and MESO III cheese openings The opening data analysis was performed on the calculated parameters (mean, max, min, standard deviation, variance, kurtosis and skewness values) from the different histograms (opening surface, opening perimeter, opening shape and opening distance). In order to prevent any distortion of the PCA all the UF cheeses were taken off from the data base because no openings were observed into these cheeses. The two first principal components PC 1 and PC 2 explained 54 % of the total variation. These two components were correlated with the perimeter standard deviation ? =0.94,the distance standard deviation =0.78 and the surface standard deviation r2 =0.73 variables. This implies that 54 % of the total variation of the total data base are attributed to the distribution of the opening characteristics. The representation of the cheeses openings parameters on the 2D factorial map built from PC 1 and PC 2 were very disperse (Figure 10). No data organisation as a function of cheese structure was observed.

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Figure 10 Principal components analysis of opening parameters However some cheese samples were well correlated to this 2D factorial map. For example, some young MESO I and 11, MESO IV were correlated to PC 1 (?>0.6) and were characterized by large openinf size irregularities. Old MESO and mainly THERMO cheeses were correlated (r >0.7) with PC 2 which explained the distance standard deviation. The specific distance standard deviation of the THERMO openings could be explained by the low opening number observed into these cheeses. The lower the number of openings, the more the distance between them becomes sensitive to the opening position and so the standard deviation increases. On the other hand a cheese characterised by a large opening density should have distance parameters insensitive to the opening position. For the other variables no clear features have been observed. A discriminant analysis was performed from the first six principal components which explained 91 % of the total data variation. The misclassification rate obtained from the learning sets was 24 %. This value increased to 36 YOfor the test sets. These results indicate that the opening characteristic could not be considered as discriminating variable for the cheese structure. 3.3 Texture Data Analysis

A PCA was performed and the correlation and the eigenvectors for the first principal components as a function of the texture variables are given in Figure 11. PC 1 which explained 54 % of the total variability, was highly correlated with the textural features. The other components PC 2 and PC 3 were correlated with the grey level histogram parameters such as skewness (?=0.8 l), Kurtosis (?=0.62), minimum ( ?=0.75) of the T2 weighted grey values and mean proton density grey values (?=O .63).

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1 2

3 4 5 6 7 8 9 1011 1213 Textural features

1 oPD weighted

HT2 weighted .mag

susc weighted

Figure 11 Correlation between texture variables and PC 1 for each MR image contrast. The numbers refer to Table I

image

1 Figure 12 Correlation parameter extracted @om the Co-occurence matrice of the PD and magnetic susceptibility image

Among all the textural features, some of them had correlation coefficient independent of the MR contrast, for example the angular moment or energy (l), the contrast (2) and the entropy (9). These textural features were strongly correlated with each other suggesting that the information provided by these parameters was independent of the MR contrast. On the other hand correlation coefficient of the textural correlation (3), variance (4) and sum average ( 6 ) textural parameters were dependent on the MR contrast. The major effect was observed between the correlation textural parameter calculated from the proton density and the magnetic susceptibility for which we observed the larger difference. The effect was explained by the UF cheeses (Figure 12). For this technology the linear grey level dependencies were higher for the magnetic susceptibility contrast images compare to proton density images. This observation was related to the signal to noise ratio which was higher for magnetic susceptibility contrast images. Indeed the UF images were highly homogeneous because they were free of openings. Consequently the grey level dependencies were strongly dependent to the signal to noise ratio. More the signal to noise ratio increase more the correlation textural parameters increase.

Figure 13 Principal components analysis of textural features

Figure 14 Discriminant analysis of texturalfeatures after variable number reduction by PCA

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The study of the correlation between the cheese textural features and the principal components showed that PC 1 ordered the cheese according to texture regularity. PC 1 set the energy and the inverse different moment against the sum variance and the entropy. More the texture is regular ( high value of energy) more the cheese will be negatively positioned on the this principal component. On the map composed by PC 1 and PC 2 (Figure 13), the UF cheeses were highly distinguished from the other cheeses. In opposite the MESO IV had the most irregular texture. The other cheese technology were distributed along the first axes. The young MESO I were correlated with PC 2 because of a high T2 kurtosis value which is sensitive to the flattening of the grey level histogram. The third principal component separated The old MESO I and the THERMO cheeses from the young MESO I1 according to their mean grey level values obtained on the proton density images. In the same way more minor components, such as the fifth one which opposed the old MESO I from the others had to be take into account. To perform the discriminant analysis, the ten first principal components were retained (Figure 14). They explained 96 % of the total data variability. From the learning and test set the rate of misclassification were nearly null. 100% of the cheeses were well classified from the learning set and a rate of 92 % was obtained from the test set. Those results compared to the discriminant analysis from T2 histogram or opening parameters were very good, knowing that the discriminant analysis could also separate the cheese as a function of their ripening age. 4 DISCUSSION AND CONCLUSION The MRI data presented in this paper could be divided into three groups : micro scale data, macro scale and localised data, macro scale and global data. The micro-scale data group is constituted by relaxometric data. Indeed MRI T2 values which are of course sensitive to water and fat amount, are also sensitive to protein structure through protein-water interactions. During ripening the proteolyse modifies the gel network structure and the microbiological activities change the pH and ionic strength gradient. These two phenomena act on the water relaxation through diffusive and proton exchange evolutions. Consequently the T2 cheese distribution are function of the cheese composition, the cheese structure and the ripening stage. Thus the difficulty to control those effects from batch to batch explained the misclassification rate of the discriminant analysis. This misclassification rate should be reduced by increasing the sample number. The micro scale and localised data are constituted by the opening information. The open texture in cheese is the result of gas production from microbiological activities on the one hand and from mechanical curd handling practices during the cheese manufacturing process on the other. The opening number and shape are out of control from batch to batch. Except for UF cheese which were free of openings the other cheese presented too much intra group variation of the opening parameters compared to the inter group variation. The sensitivity of the computed parameters and the small sample number contributed to increase the misclassification rate of discrimination. Textural features could be considered as macro scale and global data because of the statistical approach retained. The co-occurrence matrix method appeared to be

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very powerful. In fact this method integrates much information provided by the MR images. The opening and all the curd heterogeneity were took into account. But the effect of small image variation changes were ignored compared to the opening analysis. Moreover because we used a multi contrast strategy, the T2 effect were also analysed from the Tz weighted images. Thus the co-occurrence method properties provide the lowest rate of misclassification. This work was financed by a share cost contract no 96-1056 DG12 within the FAIR program TIROS of the EC. We thank J.M. Soulie and P. Fortier from Bongrain company and P. Marty Mahe from Cemagref for helpful discussions and advices.

References 1. P. Walstra and P. Peleg, 'Rheological and Fracture Properties of Cheese', Bull. Int. Dairy Fed. 1991,268,3 2. S.J. Schmidt and H. M. Lai, 'Water Relationships in Food', E. H. Levine and L. Slade, Plenum press, New York, 1991,405 3. M.J. Mc Carthy, 'Magnetic Resonance Imaging in Foods' Chapman & Hall, New York, 1994 4. M. Rosemberg, M.J. Mc Carthy, R. Kauten, Food Struct. , 1991, 10, 185 5. M. Rosemberg, M.J. Mc Carthy, R. Kauten, J. Dairy Sci. , 1992,75,2081. 6. M. Ozilgen and R. Kauten, Process Biochem. , 1994,29, 373. 7. R. Ruan, K. Chang, P. L. Chen, R. G. Fulcher, E. D. Bastian, J. Dairy Sci., 1998, 80, 9 8. S Kim, M.J. McCarthy and P.Chen, J. Magnetic Resonance Analysis, 1996, 2 (4), 281. 9. K.J Khiani, S.M. Yamany and A.A. Farag, Proceedings from the ANNIE96, St.Louis, November 1996. 10. B.B.B. Khoo, M.P.C. McQueen and W.J. Sandle, J. Biorned. Eng. 1991,13, November, 489 1 1. J.A. Throop, D.J. Aneshansley and B.L. Upchurch, Proceedings of the ASAE International Winter Meeting, Atlanta, December 1994. ASAE Paper 946580. 12. S.A. Shearer and R.G. Holmes, Transactions of the ASAE, 1990,33 (6), 2037 13. J.S. Weszka, C.R. Dyer and A. Rosenfeld, IEEE Transactions on Sytems, Man and Cybernetics, 1976, SMC-6 ( 4 ), 269. 14. P.P. Ohanian and R.C. Dubes, Pattern Recognition, 1992,25 (8), 819 15. R.M. Haralick, K. Shanmugam and I. Dinstein, IEEE Transactions on sytems, Man and Cybernetics, 1973, SMC-3 ( 6), 610. 16. R.W. Conners and C.A. Harlow, IEEE Transactions on Pattern Analysis and Intelligence, 1980, 2 (3), 204 Machine 17. M. Nagao and T.Matsuyama, Computer Graphics Image Processing, 1979, 9, 394

Protein Aggregation Studies Using PFG NMR Diffusion Measurements William S. Price, Fumihiko Tsuchiya and Yoji Arata WATER RESEARCH INSTITUTE, SENGEN 2-1-6, TSUKUBA, IBARAKI 305, JAPAN

1 INTRODUCTION The propensity of lysozyme to aggregate is well-known. The aggregation and crystallisation behaviours of lysozyme are closely linked as it has been reported that the critical nucleus most likely consists of four monomers and the growth unit is probably not the monomer but is more likely to be the octamer. The aggregation process has a complex dependence on pH, temperature and the protein and salt concentrations. This complex behaviour results from intermolecular forces since proteins are both colloids and polymers.’ Lysozyme has an isoelectric point of pH 11 and thus at most pHs has a net positive charge. In low ionic strength solutions, lysozyme interacts mainly through a combination of electrostatic repulsion and attractive dispersion forces.2 Addition of salt lowers the electrostatic barriers thereby achieving supersaturation of the protein at lower protein concentrations. In the present study the solution and aggregation properties of lysozyme at different pH, temperature, protein and salt (i.e., ionic strength) concentrations were studied using the self-diffusion coefficient, D, obtained from pulsed field gradient (PFG) NMR measurement^.^" D has a very direct correlation with molecular weight. However, care must be taken in relating the measured diffusion coefficients with protein aggregation and in this article we discuss the data interpretation in some detail. Some of the experimental results are compared to those obtained from crystal growth rate data by Li et al?’

2 PFG MEASUREMENTS OF AGGREGATION The basic scheme for studying protein aggregation using PFG NMR diffusion measurements is depicted in Figure 1 and the individual steps are outlined in the following subsections. 2.1 Protein Shape and Diffusion

To a first approximation we can take monomeric lysozyme to be spherical, in which case, D can be related to the Stokes radius, Ro, and the solvent viscosity, 7,via the Stokes-Einstein relation,

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Advances in Magnetic Resonance in Food Science

Ensemble Averaging , ~

Figure 1 Schematic description of the steps involved in studying protein aggregation using PFG NMR diffusion measurements. A cogent model for the aggregate distribution must be used in calculating the apparent diffusion coefjcient.

It is important to realise that Eq. (1) only applies to a dilute solution such that the aggregating species diffuse independently of each other. The Stokes radius, which is the effective hydrodynamic radius, is generally larger than the radius derived from the molecular structure itself. The concept of a hydration shell is often invoked to account for this discrepancy. Although the difference may also be related to the rugosity of the protein surface. In reality lysozyme is not exactly spherical, nevertheless taking it to be spherical for the point of view of modelling the diffusion data is a reasonable approximation. The next problem is how to model the hydrodynamic characteristics of the different oligomeric states. The simplest way is to approximate the monomer-monomer contact as hard-sphere contact,8x9in which case the ratio of the diffusion coefficient of an i-mer, D,, to that of the monomer, D,=Ican be modelled by

Magnetic Resonance in Food: The Developing Scene

37

and the values of Fifor various geometries are given by Teller et a1.' Whilst there is only one possible geometry for dimer formation, many possibilities exist for higher oligomers. Consequently, we have simplistically taken all oligomers to be hydrodynamically spherical, thus the friction coefficient (i.e., the denominator of Eq. (1)) increases according to the inverse cube root of the molecular weight. In fact the friction coefficients calculated from Eq. ( 2 ) for reasonable geometrical possibilities for the oligomeric shapes are all quite close to that obtained for a sphere of equivalent volume. 2.2 Crowding Effects on Diffusion

The previous section considered the hydrodynamics of lysozyme at infinite dilution (i.e., no interaction between the diffusing aggregating species). In reality, aggregating lysozyme samples are crowded systems and consideration must be given to the effect of such crowding on the diffusion coefficient, irrespective of any aggregation process since both processes lead to a reduction in the measured diffusion coefficient. Crowding is a complicated many-body problem and at present only approximate means of estimating the effects of crowding on the diffusion coefficient exist. A simple model to account for the effects of crowding based on scaled particle theory is given by"

D= ~~exp(-%c)

(3)

where

In Eq. (3) DO is the true (uncrowded) difhsion coefficient, v p is the volume fraction (mllg) of the protein and Ar is the step size and R is the radius of the diffusing particle. From the Smoluchowski equation ArlR = 213. This model is based on all of the protein present existing in the monomeric state. Consequently as the degree of aggregation increases, this simple model overestimates the reduction in diffusion."

2.3 PFG NMR Measurements of Diffusion A detailed description of the PFG NMR diffusion experiment can be found el~ewhere.~,'For a single diffusing species the equation relating the echo signal attenuation to the experimental variables is given by

In( E ) = -y 2 g 2D J 2( A - 6/3)

(5)

where y is the gyromagnetic ratio, g is the magnitude of the gradient, 6 is the width of the gradient pulses and A is the separation between the leading edges of the gradient pulses. Importantly, A defines the timescale over which the diffusion is measured. In a polydisperse system the attenuation curve is more complicated than that described by Eq. (5). If the species are in slow exchange, E should be multi-exponential. If the exchange rate is intermediate the attenuation curve is further complicated. While in the fast exchange rate the attenuation will again be single exponential (i.e., similar to Eq.

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Advances in Magnetic Resonance in Food Science

(5)) but with D representing a mass averaged diffusion coefficient (see Eq. (7)). However, all of the experimental data was well described by a single apparent difhsion coefficient even under the conditions most likely to produce aggregation. Thus, it is probably not unreasonable to assume that the exchange is slow on the time-scale of the PFG difhsion experiment. Since an aggregating protein solution is crowded it is likely that some ensemble averaging process occurs leading to a narrower distribution of diffusion coefficients. This averaging probably results from collisions and crowding since the mean free displacement is greater than the average spacing between the lysozyme molecules. We allow for this by taking the cumulant expansion (to 2"d order), ln(E) = -b(D)w + -b2 ((D): -(D2)w)7 2 where b = -y2g262(A- 6/3) and the mass averaged diffusion coefficient is defined by

where M, is the molar mass and ni is the number of the i-th aggregating species. In fact for the values of b used in the present work we can safely omit the quadratic term in Eq. (6) and thereby recover the single exponential form of Eq. (5). Equation (7) can be used to simulate the observed diffusion coefficient given a model for the oligomer distribution and an estimate of the Di.

3 RESULTS AND DISCUSSION We used 'H PFG NMR measurements to obtain the difhsion coefficients of three Iysozyme concentrations, 1S,2.8 and 10 mM at various temperatures. The experiments were performed using a Bruker DMX 500 NMR spectrometer equipped with a triple axis gradient probe (only the z-axis gradient was used). The 1.5 mM lysozyme samples were studied at pH 3, 5 and 8 and in the presence of 0, 0.15 and 0.5 M NaC1. The 1.5 mM lysozyme samples were used to study the onset of aggregation and only at the highest pH and salt concentrations were the solutions saturated at this low protein concentration. Measurements were also conducted on a lysozyme sample containing 2.8 mM lysozyme at pH 4.6 in the presence of 0.15 M salt which results in a saturated sample. These sample conditions were chosen so as to correspond with those used by Li et aL6.' who used crystal growth measurements to study the aggregation process. Finally a sample containing 10 mM lysozyme in the presence of 0.15 M salt at various pH values was used to study lysozyme in a supersaturated solution. Theoretical calculations of the lysozyme monomer diffusion coefficient were performed using the three dimensional structure of lysozyme" assuming that the backbone atoms were of equal size (0= 5.0 A) with the program DIFFCI2 which is based on the bead model appro~imation.~

Magnetic Resonance in Food: The Developing Scene

39

3.1 The Onset of Aggregation (1.5 mM Lysozyme) The results of the diffusion measurements for the 1.5 mM lysozyme samples are shown in Figure 2. In the absence of salt there is no obvious pH-dependence consistent with there being no aggregation. The theoretical predictions overestimate the monomer diffusion coefficient. However, if the effect of crowding on the theoretical diffusion coefficient is considered, the agreement with experiment becomes better. In the presence of 0.15 M NaCl the situation is rather different and there is a clear pH-dependence of the measured diffusion coefficients. The lower pH samples have higher apparent diffusion coefficients consistent with monomeric lysozyme being the dominant form. At low pH in the absence of salt, the lysozyme molecules are repulsive towards one another and thus they effectively exclude other molecules from diffusing in their neighbourhood (i.e., the self-obstruction effect is increased due to electrostatic interactions), as the ionic strength increases this effect is decreased and thus the difhsion coefficient increases (i.e., compare the results for the 0 and 0.15 M NaCl samples). In 0.5 M NaCl aggregation is possible and D decreases. Interestingly at the highest salt concentration and lowest temperature (i.e., 0.5 M and 283 K) the diffusion becomes less pH sensitive. A likely reason is that at this temperature the conditions are such that aggregation is now relatively similar at all three of the measured pH values.

... ... ........ ..+' ...

a:. . ..

i-....'-. . ..

E?.A

"+:I.

... ..

_ .. . ..' _ . .

....'.. .

'+ :....._...

3.20

3.25

3.30

3.35

3.40

3.45

3.50

0.15

M

3.55

1 O O O R (K-')

Figure 2 Diffusion of 1.5 mM lysozyme at pH 3.0 (square), 5.0 (circle) and 8.0 (triangle) centre) and 0.5 (solid centre) M versus temperature containing.0 (open centre), 0.15 NaC1. The 0.15 and 0.5 Mdata have been offset in the temperature axis (i.e., away from the vertical lines). The theoretically derived monomer diffusion coefficients are also shown (dotted line) and corrected for the effects of crowding (dotted line with plus symbols). ('-I

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3.2 Diffusion in Saturated Solution (2.8 mM Lysozyme)

The results of the diffusion measurements for the 2.8 mM lysozyme sample are shown in Figure 3. Due to the higher protein concentration the crowding effects are more significant. After accounting for the effects of crowding the experimental difhsion coefficients for 2.8 mM lysozyme at pH 4.6 and 0.15 M NaCl indicate that there is some degree of aggregation although the average molecular weight is clearly less than that of a dimer. The crystal growth data of Li et a16,’ appear to underestimate the degree of association at high temperatures but overestimate it at low temperatures.

...

Figure 3 Diffusion of lysozyme at p H 4.6for a sample containing 2.8 mM lysozyme and 0.15 M NaCl (closed squares). Since this is very similar to the conditions as used by Li et al.,62 the apparent diffusion coefficients (closed circles) were calculated from the theoretical monomer diffusion coefficient using the association parameters of Li et a1 and assuming that the higher oligomers were spherical. The theoretical values for the monomer diffusion coefficient at each temperature are denoted by the dotted line and those corrected f o r crowding are denoted by the dotted line with plus symbols

Magnetic Resonance in Food: The Developing Scene

41

3.3 Diffusion in Supersaturated Solution (10.0 m M Lysozyme)

To observe aggregation in supersaturated protein solutions, samples were prepared containing 10 mM lysozyme in 0.15 M NaCl at various pH values and the results of the diffusion measurements are shown in Figure 4. The effects of crowding at this protein concentration are very pronounced and, after allowing for the effects of crowding; the diffusion coefficient of a monomer is reduced to that of a (uncrowded) dimer. At this protein concentration it is found that only at the lowest pH and highest temperature does the observed diffusion coefficient approach that of a (crowded) monomer. The .diffusion coefficient decreases significantly with increasing pH, consistent with high degree of aggregation. Large changes are also observed in the corresponding 'H NMR spectra (not shown). This is consistent with large reductions in the rotational correlation time as would be expected with the formation of higher oligomers.

0.2 0.0 0.4

-

n

"E

-0.2

-

-0.4

-

W

2-

2 Q

-c

W

-0.8 -0.6

-1.0

1

3.20

~

3.25

1

3.30

~

3.35

1

3.40

I

3.45

~

I

3.50

,

I ]

3.55

1000/T (K-') Figure 4 Change in the diffusion coeficient of lysozyme with p H @H 3.2: up triangle; pH 4.0: down triangle; p H 5.0: circle and p H 6.2: diamond) for a sample containing I 0 mM lysozyme and 0.15 M NaCl versus temperature. The calculated diffusion coeficients for the lysozyme monomer (dotted line), dimer (dashed line) and octamer (solid line) uncorrected for crowding are also shown. The monomer diffusion coeficient corrected for crowding is denoted by the plus symbols connected by the dotted line (NB this is almost perfectly coincident with the dimer dflusion coeficient which was derived from the uncorrected monomer diffusion coeficient).

~

l

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Advances in Magnetic Resonance in Food Science

4 CONCLUSIONS The results of our diffusion measurements on lysozyme show that the aggregation state and the interactions between the protein molecules are very sensitive to the experimental conditions (i.e., protein and salt concentrations, pH, and temperature). Even in the most supersaturated lysozyme solution studied (i.e., 10 mM) it can be inferred from the observed difision coefficient that the average oligomer is less than that of an octamer. PFG NMR diffusion measurements are a powerful technique for studying the solution behaviour of proteins since the method can be used at high protein concentrations in contradistinction to many traditional techniques. Further, compared to NMR relaxation measurements, the interpretation of the diffusion data is more straightforward. Nevertheless, to properly interpret the diffusion data factors leading to non-ideal solution behaviour such as crowding and electrostatic effects need to be considered. A major problem in this respect is that there are as yet only very approximate models for accounting for the effects of crowding.

5 ACKNOWLEDGMENTS Dr. Bertil Halle (University of Lund, Sweden) and Dr. V. V. Krishnan (Lawrence Livermore National Laboratory, USA) are thanked for useful discussions and performing the theoretical calculations of lysozyme diffusion, respectively.

References

L. R. De Young, A. L. Fink and K. A. Dill, Acc. Chem. Rex, 1993,26,614. D. E. Kuehner, H. W. Blanch and J. M. Prausnitz, Fluid Phase Equilibria, 1996,116, 140. 3 W. S. Price, in Annual Reports on NMR Spectroscopy; ed. G.A. Webb, Academic Press, London, 1996, p. 5 1 . 4 W. S. Price, Concepts Magn. Reson., 1997,9,299. 5 W. S. Price, Concepts Magn. Reson., 1998,10, 197. 6 M. Li, A. Nadarajah and M. L. Pusey, J. Cryst. Growth, 1995,156,121. 7 A. Nadarajah, M. Li and M. L. Pusey, Acta Cryst., 1997, D53, 524. 8 D. C. Teller, E. Swanson and C. De Haen, Methods Enzymol., 1979,61, 103. 9 J. Garcia de la Torre and V. A. Bloomfield, Q. Rev. Biophys., 1981, 14, 81. 10 J. Han and J. Herzfeld, Biophys. J., 1993,65, 1155. 1 1 L. J. Smith, M. J. Sutcliffe, C. Redfield and C. M. Dobson, J. Mol. Biol., 1993, 229, 930. 12 V. Yu. Orekhov, D. E. Nolde, A. P. Golovanov, D. M. Korzhnev and A. S. Arseniev, Appl. Magn. Reson., 1995,9,581.

1 2

Water, Ions and Small Molecules in Food

A Multistate Theory of Water Relations in Biopolymer Systems B. P. Hills, C. E. Manning and J. Godward INSTITUTE OF FOOD RESEARCH, NORWICH RESEARCH PARK, COLNEY, NORWICH NR4 7UA, UK

1. INTRODUCTION

N M R and MRI techniques now exist for monitoring water mobility over distance scales ranging fiom the molecular to the macroscopic. However, there is still much that needs to be learnt before dynamic information on the molecular distance scale can be used to predict the transport behaviour of water on the microscopic and macroscopic distance scales in complex, heterogeneous food systems. In this paper an attempt is made to show how, beginning with a new multistate theory of water relationships on the molecular distance scale, transport behaviour on larger distance scales can be understood. Such understanding is essential if we are to predict the effects of biopolymer engineering and novel processing operations on the quality of manufacured foods. 2. THE STATE OF WATER IN SINGLE COMPONENT BIOPOLYMER SYSTEMS 2.1 The multistate formalism

Recent multinuclear N M R experiments and molecular dynamics calculations have clearly demonstrated that it is usually sufficient to consider three stqtes of water in biopolymer systems and biological tissue. The first could be called “~tructural” or strongly interacting water hydrogen bonded inside the cavities and gropves of globular proteins and polysaccharides and which plays an important role in determining the structure and dynamics of the biopolymer chains. There is a spectrum of lifetimes of this “structural” water ranging from nanoseconds to microseconds depending on the nature of the water-biopolymer interaction. In addition there is “surface” or “m$tilayer” water, which is water at the biopolymer surface having a dynamic state pgrturbed by the presence of the interface. This surface water extends for several molecular layers from the surface and is extremely mobile, having exchange lifetimes on the order of subnanoseconds. Bulk water comprises the third state. In this section we take this simple molecular model and try to elucidate its implications for the dependence of readily measurable quantities such as water activity, G, NMR water relaxation rates, y, and water diffusivity, D, on water content and composition in single and multicomponent biopolymer mixtures,

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Advances in Magnetic Resonance in Food Science

The starting point for the model is the set of equations

yav =

ci xiyi

These equations give the observed “average” value as the weighted average of the value over all states of water, where xi is the mol fraction of water in the state i. The first equation is a consequence of the Ergodic theorem of equilibrium thermodynamics which states that the time averaged property of an individual water molecule as it diffuses between the various states in the system equals the time-independent ensemble average over the system, so that a,

=

limt,,

I/t I,”“ dt a(t)

= Xi Xiai

[41

Equation [2] is the well-known fast exchange limit equation in Nh4R relaxometry which was originally derived by Zimerman and Britten It remains valid provided there is fast exchange of all water proton pools on the N M R measurement timescale (typically a few 100’s of microseconds), so that the water proton relaxation is single exponential. It also neglects the dephasing effects of chemical shift offsets between the pools, so that proton exchange between biopolymer and water protons is, for the moment, neglected.

’.

2.2 The sorption isotherm for a single component biopolymer system

We first show how the well-known “sigmoidal” shape of a sorption isotherm for a simple one-component biopolymer-water system emerges from equation [ 11. Acknowledging the three states of water explicitly, equation [11 becomes,

structural

multilayer

bulk

where we have assumed there are n states of the structural water in the biopolymer. If mi is the mass of the ith state of water per gram of dry biopolymer, then xi = m,/W where W is the total mass of water per gram of dry biopolymer. To proceed to a sorption isotherm, we assume, for simplicity, that the order of ‘Yilling” of the various states as water molecules are added to a dry biopolymer is first the structural states, then the multilayer state and finally bulk water. Of course, a more rigorous theoretical approach would be to incorporate a Boltman distribution over the energy levels characterizing each state of the water, but for present purposes this complexity is left for a later generalization.

Water, Ions and Small Molecules in Food

41

2.2.I The structural states

These are occupied at very low water contents and, because of the limited number of such sites, also over a narrow range of water content, W, typically less than 15-20%. Because there are many different structural sites in any particular biopolymer, such as a globular protein it is probably unwise to try to elucidate the detailed form of the isotherm in this region, but merely leave the general form,

where a, is the stuctural water activity and m;(max) is the mass of water when the structural state i is saturated. The shape of the sorption isotherm in this very low water content region will clearly depend on the biochemical details of the number and binding energies of the hydration sites in the biopolymer. 2.2.2 The multilayer state

When all the structural sites are occupied, further addition of water creates the “multilayer” state and in the water content range for which there is only structural water and multilayer water, equation [11 becomes

which is equivalent to the sorption isotherm,

where the constant C, is

2.2.3 The bulk water state

When all structural and multilayer sites are occupied, it is assumed that further addition of water causes formation of the bulk state, for which,

which can be rewritten in the form, aav=

+ Cdw

for m,(max) I WI m

where the constant cb is defined as

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Advances in Magnetic Resonance in Food Science

Unlike equation [6] describing the structured water region, equations [8] and [12] have the same dependence on W’, which no doubt explains the frequently observed sigmoidal shape of sorption isotherms. The equations also suggest that to identifjr the states and their water content ranges, the data should be plotted as water activity verses IN, or alternatively, against the solid content, (I-W,)N,, where W, is now the mass of water per total mass of water plus biopolyrner. In this way the constants C ,, Cb can be determined and the populations of the various states can be identified from breaks in the slope. Figure 1 shows an example for the adsorption and desorption isotherms of pregelled potato starch. The desorption isotherm shows breaks at water activities of about 0.9 and 0.3, indicating that structural water is characterized by water activities less than 0.3 while multilayer water exists in the activity range between 0.3 and ca. 0.9. The small additional break in the adsorption plot at water activities close to unity indicates that adsorption leads to greater amounts of essentially bulk water at low solid contents. Figure 2 shows the more conventional sorption and desorption isotherm plots , together with the theoretical fits. Because of our ignorance about the number and states of structural water in the pregelled starch no attempt has been made to fit the very low water content regime where only structural water exists and equation [6] should apply.

Figure I . i%e multistate theory for the desorption and adsorption isotherms of pregelledpotato starch. The straight lines are best fits of equations [8] and (111 expressed in terms of solid content.

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Water, Ions and Small Molecules in Food

Pm-gelled potato starch

0

0.4

~

adsorpaon desorphontheoly

-adsorption

02

theory I

0

20

60

40

80

100

WY Figure 2. The adsorption and &sorption isothermsfor pregelled potato starch based on the linearfits infigure 1. . 2.3 Water relaxation in a single component biopolymer system

The theory for the dependence of the single exponential water relaxation rate on water content can be developed in complete analogy with equations [5] to [12]. It follows that,

where the constant B, is

where the constant Bb is defined as

These equations neglect the effects of proton exchange and secular dipolar cross relaxation and focus exclusively on the states of water. Figure 3 shows the water proton transverse relaxation rate for the adsorption and desorption isotherms of pre-gelled potato starch corresponding to figures 1 and 2. Note how the desorption plot shows a break at the same water content as the water activity plot in figure 1.

Advances in Magnetic Resonance in Food Science

50

Pre gelled potato starch 3000

2500

i

2000

-

In \

1

1500

N

K

1000

0 0 0

abosrbR2 desorbR2max

-des reg R2 max

500

-des

0

5

10

15

reg R2max

20

(1-WY)MfY

Figure 3. The dependence of the CPMG water proton transverse relaxation rate (measured at a short p l s e spacing of 200.11.9agaimt solid content.

The proton exchange contribution can vary with water content so it is undoubtedly better to test the theory with the proton dewupled water oxygen-17 relaxation rate or in situations where proton exchange is known to be negligible such as all glassy states of the biopolymer and also in the low water content regimes lacking bulk water. This has been done on model Sephadex and silica systems4but not yet in biopolymer systems.

2.4 The relationship between water activity and NMR relaxation in the dilute regime Equations [ 111 and [ 151 imply that, over the same range of water contents there is a simple linear relationship between the water activity and the water relaxation rate. In dimensionlessform, this relationship takes the form,

Figure 4 shows the linear dependence predicted by equation [16] for the adsorption isotherm of pregelled potato starch. The break at a water activity of ca. 0.9 corresponds

51

Water, Ions and Small Molecules in Food

pre gelled potato starch adsorption 1800 T

/

1600

1200

2 m :.Iloo0

"1

/

600

/

"d reg wet __t_l

0

02

0.4

0.6

0.8

1

1-aw

Figure 4. The linear relationship between the water relaxation rate and (I-aw) for pregelledpotarostarchpredicted by equation [I61 to the bulk water break in the adsorption plot in figure 1. The oxygen-17 data in reference (5) also lends support to the simple form of this equation. It should, however, be remembered that factors such as proton exchange, that can also contribute to water relaxation and that these may show changes at water contents independently of water activity. For this reason, linear activity-relaxation relationships should only be expected over limited ranges of water content, temperature and pH. Moreover, there is no implied fimdamentaJ physical relationship between water activity, which is an equilibrium thermodynamic quantity, and NMR relaxation, which is in essence a non-equilibrium, kinetic phenomena. AU that is implied is that the changing states of water in a biopolymer system will affect both activity and relaxation in parallel ways. 2.5 FID amplitudes for a single component biopolymer system

At low water contents, where structural water is of special importance, relaxation times become too short for measurement by the CPMG method and relaxation times need to be extracted from the FID itself One complication in doing this is the appearance of fast decaying Gaussian components from the biopolymer itself. While these are of interest in their own right, especially for studies of the mechanics of plasticization, they complicate the analysis of the various states of water. One way to circumvent this complication is to focus on a single time point, say t., in the FID, chosen so that all solid biopolyrner signal has decayed to zero, leaving only the mobile water component. The FID amplitude at t, is then,

Advances in Magnetic Resonance in Food Science

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amplitude 1.25

1 .oo

0 0000 00000 000 000 0 00 0

0.75 MO 0.50 0.25

xxxxxxxxxxxxxxxxxx

......

0 c

0 0.0

0.00 0

0.

o.oooo..

I

I

I

I

0.02

0.04

0.06

0.08

I

0.1 0.12 time (msec)

Fig. 5 : Normalized amplitude of the FID curvesfor gelatines observed at 20 MHz and 20°C at dflerent water contents ( 0 :4.93 ; :15.86 ;x :20.1 ; 9 :57.73%, wet basis). The extrapolated signal intensityfor the slow relaxing component, MO,is indicated as well as the total signal intensity at 11 p,FIDf1. Takenfrom reference 9.

-

where yav is given by the equations above. For example, taking equation [ 151, we deduce that In M(ta) = In A - Em

[181

where the constants A and E are given as A=M(0)eXp{-ybda} and E is mo{ ys-ybullr)fa. Figure 5, taken from reference 9, shows the proton FIDs for gelatine gels at various water contents and figure 6 shows that the FID amplitude does indeed have a very similar “sigmoidal” dependence on moisture content as the sorption isotherm itself In figure 6, the straight line segments on the FID amplitude plot as the water content increases correspond to addition of structural, multilayer and bulk water respectively. The derivation of equation [18] assumes that the mobile component comprises only water. It should, however be remembered that in many biopolymer systems there may also be contributions from mobile side chains that increase with increasing water content

Water, Ions and Small Molecules in Food

53

1.8 1.6 1.4 1.2 1.0 0.8

0.6 0.4 0.2

Fig.6 : Initial 'H A&&? signal amplitude of the slow relaxing protons divided by the amplitude of the FID signal at I 1 psec ( 8, :MOsrJFID~J and sodium relaxation times ( : 23iVal / R 2 ) as a finction of the water content. A sudden change in slope is visible for both parameters in the 10-20% water content region. Takenfrom reference 9.

as they become increasingly plasticized. The use of water oxygen-17 relaxation would alleviate this complication. 2.6 Water diffusivity in a single component biopolymer system

Because the biopolymer self diffusion coefficient is negligible compared to that of bulk water it is usually safe to assume that the water self diffusion coefficients in the structural and multilayer states are also negligible, so we can write,

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or

xi - x,,,. Of course, a fill calculation of the water self diffusion where mJW = coefficient must also take account of the well-known obstruction effects of the biopolymer on the bulk water translation. 3. THE STATE OF WATER IN MULTICOMPONENT BIOPOLYMER SYSTEMS 3.1 Sorption isotherms for multicomponent biopolymer/solute/water systems

Let the moisture content fraction of water associated with the jth biopolymer or solute component be x(j), then clearly X,x(i) = 1. Including the various water states, i, associated with each component, we can write, Cijxi(j)= 1. Except for the bulk phase, the activities will depend on both the state of the water (j) and the component, i, so

This assumes an “ideal” mixing, whereby the water is not preferentially associated with one or other component. In other words, if the system is made by mixing component (1) with water fraction x(1) with component (2) of water fraction x(2), then the corresponding water fractions in the mixture remain x( 1) and x(2). If this is not the case, we can define preference coefficients c(j) for each component and generalize equation [21] by writing

such that Zij c(i)xi(i) = 1. Using these equations it is now possible to derive sorption isotherms for both ideal and non-ideal multicomponent biopolymer/solute/watersystems. For notational simplicity we combine the structural and multilayer states into a single state “s” and specialize to the situation where some bulk water exists. The derivation for other cases follows along obvious lines. For the two-component case, equation [21] becomes

But -(I) becomes

=

%&(2) = 1 and using the normalization condition, the sorption isotherm

But for the sorption isotherms measured on the separate components at the same water contents,

Water. Ions and Small Molecules in Food

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Equations [23] to [26] show that, for the case of ideal mixtures, for which the preference coefficients, Es(j), are all unity, a,(1,2) = a d 1 ) a 4 2 )

+ second order terms in &(1)&(2)

~ 7 1

which, to first order is the Ross equation for multicomponent sorption isotherms. However, in general, this is only a starting approximation and the fill equation [23] should be used in the non-ideal mixture. Clearly, an outstanding task is to acquire tables of the preference coefficients in analogy with activity coefficients. 3.2 Water relaxation and diffusion in multicomponent biopolymer/solute/water systems An analogous derivation shows that the water relaxation rate in a two component mixture, yav(1,2), is given as

where yb& is the relaxation rate in the bulk water state, usually pure water. For ideal mixing, equation [28] implies the validity of a Ross-type equation for relaxation,

The limits of validity of this relationship have yet to explored experimentally. In like manner, the corresponding relationships for water difisivity are

which, for ideal mixing, leads to the corresponding Ross relation: 1,2)/ Dbud = (DS(1)/ h u l k )(Ds(2)/ h u l k )

+ higher order terms

[3 11

3.3 The origin of hysteresis in sorption isotherms and relaxation state diagrams

Figure 2 shows a typical example of hysteresis during the adsorbtion and desorption of water in a single component biopolymer system. Hysteresis in sorption isotherms is often assumed to be caused by differing microscopic condensation patterns of water condensed in capillaries during adsorption and desorption. The idea being that surface tension effects cause a depression of vapour pressure and hence of water activity'. However simple order of magnitude calculations* serve to show that, in most systems, this is unlikely to be the whole explanantion. Indeed, the large differences in water activity observed in figure 5 at the same water content would require capillary pores of the order of a few water molecular diameters, when the very concept of surface tension breaks

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down. The multistate theory provides a simple alternative explanation of hysteresis since it merely requires that the number and nature of the structural (and, possibly, multilayer) states of water differ during the adsorption and desorption processes. Any change in biopolymer conformation or state of aggregation induced by the adsorption and desorption cycles would therefore serve to alter the water activity. Indeed, in principle, the multistate theory provides the opportunity for quantitatively predicting the effect of engineering a biopolymer on its sorption isotherm.

A further consequence of the multistate theory is that sortion-desortion isotherms should also exist in measurements of the water relaxation rates and FID amplitudes. Figure 6 gives an example of this behaviour and confirms that the isotherm and relaxation loops have similar shapes.

I

I

Pre-gelled potato starch: relaxation hysteresis

0.8

0.7

20

I

40

60

wy %

80

100

~

I

Figure 7. Hysteresis in both the water proton transverse relaxation rate and the water activity during adsorption and desorption isotherms of pregelled potato starch

3.4 Ion solvation and the multistate theory

Most biopolymers, including proteins and important food polysaccharides, carry ionic groups and their properties therefore depend, often sensitively, on the nature of the counter ions. The question therefore arises as to the relationship between the various states of water and the dynamic properties of the counter ion. A priori one would expect that ionic mobility would be minimal in the dry biopolymer system when only structural

Water, Ions and Small Molecules in Food

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water exists, because there would be little or no water available for ionic solvation. Conversely, ionic mobility would be essentially that of the hlly hydrated aqueous ion when a bulk water state exists. It follows that there should be a transition zone in ionic mobility as the multilayer state of water is formed. To test this hypothesis, =Na NMR relaxation was used to monitor sodium ion mobility in a gelatine gel system over a wide range of water contents. The data, along with the normalized amplitudes of the mobile fraction in the proton FID at a t, of 170ps, is shown in figure 6 and supports the hypothesis In the structural state, corresponding to moisture contents of ca. 15% the sodium relaxation time is too short to measurement. As the multilayer states are occupied between moisture contents of ca. 15-20%, there is a rapid increase in the sodium transverse relaxation time and, as the bulk water state is formed at water contents above ca. 20%, there is a dramatic change of slope and a slower increase in sodium relaxation time corresponding to dilution of the now, fully hydrated, ions. Consistent with the multistate model, these sodium relaxation transition zones correspond very nicely with the changes predicted in the amplitude of the mobile fraction of the proton FID.

’.

4. GENERALIZATION TO MULTICOMPARTMENT BIOPOLYMER SYSTEMS

4.1 The dynamic state of water in multicompartment, multicomponent systems Up to this point it has been assumed that the biopolymer system is spatially homogeneous on all distance scales greater than the macromolecular. This assumption breaks down in phase-separated mixed biopolymer systems and in all biological tissue so it is necessary to consider how the multistate theory applies to such systems. Figure 8 shows a schematic of a two compartment system in which water can exchange, by diffusion, between compartments 1 and 2. Suppose that the inner compartment consists of a dense biopolymer network such as a water-saturated starch granule and the outer compartment is bulk water. The equilibrium water activity measured fiom the ratio of vapour pressures will, of course, be unity, because there is a bulk water phase. This does not, however, imply that the equilibrium water activity inside the biopolymer compartment is also unity, as is sometimes supposed. Indeed, it is manifestly less than unity because it comprises a rather concentrated biopolymer gel network. The resolution of this paradox must be the presence of additional terms in the expression for the water chemical potential inside the biopolymer network, most probably an osmotic pressure term, xV1, because the bulk water outside will exert a swelling pressure on the biopolymer compartment. We can therefore write,

where V, is the partial molar volume of water. This example serves to demonstrate that the water activity in a microscopically heterogeneous system can vary spatially. It should therefore be possible to once again invoke the Ergodic theorem and define an average water activity as the volume integral over all microphases in the ensemble:

a,

= fdv a(r) = Cka(k)V(k)

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where the sum is taken over all compartments k of volume V(k). While this average value can be defined, it is clearly not the same as the overall water activity measured from the equilibrium vapour pressure and it is questionable whether this Ergodicallydefined water activity has any measureable significance. In contrast the analogous equation for water relaxation has obvious measureable significance as it is the average relaxation rate, yN , observed when diffusive exchange of water between all the compartments is fast on the NMR measurement timescale, so that the observed water proton relaxation is single exponential, and

Of course, if the diffusive exchange is very slow on the NMR measurement timescale then a multiple exponential relaxation is observed, each exponential component arising from one compartment. More complicated multiple exponential behaviour is observed when the exchange and relaxation timescales are comparable, in which case the BlochTorrey equations must be solved. Analogous expressions exist for water diffusivity and these hold provided diffusion is unrestricted by permeability barriers at the compartment interfaces. Then D,

=

Jdv D(r) =

ck D(k)V(k)

1351

If there are diffusive barriers, then the q-space formalism can be used to relate the NMR d i s i o n measurements to

0

mr

001

I

Figure 8, The dependence of the water proton transverse relacation time distribution (in seconds) for packed be& of native corn starch g r m l e s on water content. The dotted line shows the water-saturated bed Reducing the water content progressively increases the relative amplitude (%) of thefmter relaxing peak.

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4.2 Application to native, granular starch and cellular tissue

Figure 8 shows the distribution of water proton transverse relaxation times for a bed of native (i.e.ungelatinized) corn starch granules at various water contents. Assigning the two major peaks is far from straightforward. Based on comparison with electrical conductivity data the author originally assigned the long relaxation time peak to water outside the starch granules and assumed its shift to lower relaxation times was a result of exchange with water inside the granules on an intermediate timescale’2. However this model fails to explain the q-space diffusion data and suggests that the peaks in figure 8 actually arise from water in internal domains within the starch granules. For example, the longer relaxation time component could be water inside the amorphous growth rings inside the granule; whereas the shorter component to water inside the semi-crystalline stacks. The observation of a third “shoulder“ in the case of potato (see figure 9) is consistent with the larger and more heady pitted nature of the potato starch granules. When water is replaced by DzO the starch proton relaxation time distribution also shows two peaks (figure lo), consistent with the more mobile nature of the amorphous region compared to the semi-crystalhe domains. Furthermore, the deuterium transverse relaxation time distribution is also multiple exponential, consistent with the proton data.

Figure 9. The dependence of the water proton transverse relaxation time distribution (in second) for packzd beds of native potato starch granules on water content. The dotted line shows the water-saturated bed Reducing the water content progressively increases the relative amplitude (%) of the fmter relaxingpeak-s.

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Figure 10. The distribution of starch proton transverse relaxation times for a packed bed of corn starch granules saturated in D20.

The q-space diffusion data, acquired with the stimulated echo pulse sequence, is unusual in that it is dependent on the product q2A and yet can be fitted only with a twodimensional diffusion model (see reference 12 and figure 12). It would appear that, on the NMR disfhion timescale of milliseconds the water is confined to a s i o n inside the granules, most probably in the channels created by the amorphous growth rings.

Figure 11. The dependence of the relative amplitude of the stimulated echo on $A for a packzd bed of corn starch granules containing 35% water. Note the f i t of the 2dimensional d f i s i o n model.

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In contrast to carrot, apple tissue displays three distinct water proton transverse relaxation times peaks whose position depends on diffusion of water between the vacuolar, cell wall and cytoplasmic compartments. In this case the effects of drying and freezing on this relaxation time distribution require solution of the Bloch-Torrey equations in a three-compartment system14. The details of the analysis can be found in reference (14). 4.3 Combining distance scales

Developing theoretical models which consistently incorporate transport and mobility data over all the distance scales accessible by NMR and MRI " remains an outstanding Cture challenge. Consider, for example, the drying of raw potato tissue. MRI has already been used to monitor moisture gradients set up in a piece of raw potato during drying and these were interpreted using a Fickian diffusion model involving an effect water dfision coefficient and ~hrinkage'~. However, at the microscopic distance scale it is the arrangement of cells and air-gaps in the tissue as well as membrane permeability barriers that determine the observed macroscopic diffusivity and shrinkage. As we have seen, NMR relaxometry and q-space microscopy can be used to probe water compartmentation and diffusion on this distance scale. What is lacking is the theoretical formalism for relating the macroscopic parameters to microstructure. One possibility is to treat the cellular structure of the tissue with some lattice or tessellation algorithm and include subcellular compartmentation in the model by incorporating a two- or threecompartment cell model 14,17. In principle tissue drying or freezing could then be analysed by the removal of subcelluar compartments and, at the tissue level, by the random removal of cells, which also results in the shrinkage. However, these possibilities have yet to be systematically explored. At the molecular level, the values of the intrinsic diffisivities and relaxation rates characterizing the microscopic compartments could, in principle, be calculated as a hnction of concentration during drying or freezing, by use of the proton exchange formalism andor the multicomponent theory discussed above. Clearly much remains to be done before this integration of distance and timescales can be achieved in any given multicompartment and multicomponent food system.

Acknowledgments The author wishes to thank the Biological and Biotechnology Science Research Council (BBSRC) for financial support.

References 1. R.M.Brunne, E.Liepinsch, G.Otting, G.Wuthrich, and W.F. van Gunsteren, JMoIec. Biol., 1993, 231, 1040. 2. V.P.Denisov, K.Venu, J.Peters, H.D.Horlein and B.Halle, JPhys. Chem B., 997, 101,9380. 3. J.R.Zimmerman and W.E.Britten, JPhys. Chem., 1957,61, 1328. 4. B.P.Hills and C.E.Manning, JMolec. Liq., 1998, 75, 61. 5. B.P.Hills, C.E.Manning, YRidge, and T.Brocklehurst, JSci. Food and Agrzc. 996, 71, 185.

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6. M.C.Vackier and D.N.Rutledge, Food Chem. 1996,57,287. 7. R.Zsigmondy, Z.AnorgChem., 1911, 71, 356. 8. P.S.Belton, in Food Freezing: today and tomorrow, ed. W.B.Bald, Springer Verlag, Berlin, 1989 ch. 1 . 9. M.-C.Vackier, B.P.Hills and D.N.Rutledge,JMagn.Reson., in press. 10. P.T.Callaghan,Principles of hMR microscopy, Oxford Science Publications, Oxford, 1991. 1 1 . B.P.Hills,Magnetic Resonance Imaging in Food Science, John Wiley & Sons, New York, 1998. 12. B.P.Hills, J.Godward, C.E.Manning, J.L.Biechlin and K.M.Wright, Magn. Reson. Imaging, 1998, 16, no. 516 inpress. 13. B.P.Hills, Molec. Phys., 1992, 76,489. 14. B.P.Hills, and B.Remigereau, Int. J. Food Sci. and Tech., in press. 15. R.Ruan, S.J. Schmidt, A.R.Schmidt, and J.B.Litchfield, J Food Process Eng., 1991, 14,297. 16. A. Szafer, J.Zhong, and J.C.Gore, Magn. Reson, inMed., 1995,33,697. 17. B.P.Hills and J.E.M.Snaar, Molec. Phys., 1992,76,979.

Molecular Mobility of a System: Waxy Maize, Glycerol and Water, Studied by NMR D. C. P. Jardim,' J. R. Mitchell? W. Derbyshire,2 J. M. V. Blanshard2 and J. A. G. Areas3*

' INSTITUTO DE TECNOLOGIA DE ALIMENTOS, CP 139, CAMPINAS, S. P., BRAZIL FACULTY OF AGRICULTURAL SCIENCES, UNIVERSITY OF NOlTINGHAM, SUlTON BONINGTON, UK DEP. DE NUTRICAO, FACULDADE DE SAI~DE PUBLICA DA USP, AV. DR. ARNALDO, 715, CEP 01246-904, SAO PAULO, SP, BRAZIL

ABSTRACT Molecular mobility and physical state of biopolymers are important and informative aspects related to food stability. The glass transition temperature (Tg) better describes key constituents of foods, and it is calculated either for each component or for the whole system The present work studied the molecular mobility of a system constituted by starch and the plasticizers glycerol and water, by means of several techniques, namely, DMTA, water sorption isotherms, X-ray diffraction, texture measurements and NMR relaxation methods. Waxy maize was used as such or extruded to produce regular unexpanded semi-transparent ribbons of gelatinised starch. After drying to 22% moisture these ribbons presented glassy state characteristics. The sample was then placed in contact with glycerol water solutions of several Aws for 21 days both through ambient atmosphere in sealed chambers (system I ) and immersed in the solutions (systemII). System I samples lost water, even those placed in environments with relative humidity higher than the samples. On the other hand, system I1 samples gained water after this period. Crystal structure of starch was lost after extrusion and did not rewver on the samples of system I. However, a gradual increase on crystalline order was observed on system I1 samples as their moisture increased. Samples of system I showed a slight increase on log E (E=Young's modulus, calculated from three point bend test) with decrease on moisture content of the samples, whereas system I1 presented a marked dependence of Young's modulus and moisture wntent of the samples. There was also a correlation between log E and glass transition temperature determined by DMTA. Relaxation times of the samples, determined by CPMG, was practically unaffected in system I, whereas presented in system I1 a marked increase in TZas the Aw of glycerol solutions increased. Tg and TZwere also correlated, indicating the relationship between proton mobility and glass transition temperature. Food stability is related to biopolymers low mobility that is achieved by keeping them in environments of low water activity, preferably below their Tg. Relaxation parameters obtained by NMR may contribute to describe the idealised situation for low biopolymer activity. This technique has proved valuable to assess molecular mobility in the systems under study.

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1 INTRODUCTION Molecular interactions, molecular mobility and physical state of biopolymers are important and informative aspects related to food stability. Several phenomena as retrogradation, crystallisation and gelatinization have been more efficiently explained when one uses molecular mobility or state transition concepts. The glass transition temperature (Tg) better describes the physical state of key constituents of foods, and it is calculated either for each component or for the whole system. Starch is an example of a partially crystalline biopolymer that presents physical changes governed by nonequilibrium phenomena that affect deterioration and shelf-life of starch products (Biliaderis et al, 1986; Slade and Levine, 1991; Noel et al, 1996; Tsoubeli et al, 1995; Chinachoti, 1993; Karel, 1985; Kalichewski et al, 1992; Jardim, 1998). The glass transition temperature concept has been used more recently preferentially to water activity for forecasting food deterioration. Whereas this approach has proved successful to predict deterioration due to chemical transformation that is determined mainly by molecular mobility of food components, it has been useless to predict microbial growth in the same materials. Molecular motion around this transition state has proved to be an important piece of information amenable to be addressed by non destructive, non invasive, low cost, low resolution proton NMR, which can add more information to food systems. The present work studied the molecular mobility of a system constituted by starch and the plasticizers glycerol and water, by means of several techniques, namely, DMTA, Aw, X-ray diffraction, texture measurements and NMR relaxation methods.

2 EXPERIMENTAL

Samples: Waxy maize starch was provided by National Starch and Chemical Co (Manchester, UK). It was used as such or extruded in a Clextral BC-21 extruder (Clextral, France) with successive extrusion temperatures of 48, 100, 120 and 99°C in consecutive sections of the equipment, producing a continuous ribbon (rectangular die of lmm x 3 cm) of homogeneous gelatinised starch in the rubbery state without air bubbles. These ribbons were dried over P205 under vacuum to a final moisture of 22% (d.s.b.) and cut in regular pieces for further experiments. After drying these ribbons presented characteristic glassy state behaviour. Glass transition temperature: Glass transition temperature (Tg) was determined in a dynamic mechanic thermo-analyser (DMTA) from Polymers Laboratories (UK) at a 5”C.min” heating rate, 1 Hz frequency and deformation xl in a single cantilever bending mode. Tg was obtained in the peak of tan 6 in the resulting thermogram. Glass transition was also calculated using Couchman-Karasz equation (Couchman and Karasz, 1978):

Tg = w,ACP, Tg, + W2ACP2TiT2 + W,ACP, Tg, W,ACp, + W2ACp, + W,ACp,

(1)

Water, Ions and Small Molecules in Food

65

where: W1.2,~ - mass fraction of each component, ACpl,z,3- specific heat difference between the liquid and glass state of each component, Tg,,~,3 - glass transition temperature of the pure components.

Glass transition temperatures utilised were: 139K for water, 500K for the wax maize and 180K for the glycerol with respective specific heat differences of 1.94Jg-1K-7, 0.41Jg-'K-' and 0.88Jg-'K1. (Davies and Jones, 1953; Kalichewsky et al, 1992) Texture: Texture parameters were obtained using TA-XT2 (Stable Micro Systems, Ltd., UK) using the three point bend test. Young's modulus was calculated as: F

L3 E = -D 4bd3 -

where: E = elastic modulus, FA3 = gradient of the first section of the deformation line, b and d = width and thickness of the sample, respectively, L = distance between the two supporting points of the sample (9.3 mm).

M R experiments: They were performed in a spectrometer (Bruker Minispec PC 120) at 4OoC. The signal amplitudes and the decay time (T2)for the solid and liquid portions of the samples were obtained from the Free Induction Decay @ID)recorded after a single pulse. TZ of the whole sample was also calculated by CPMG (Carr and Purcell, 1954; Meibom and Gill, 1958) experiments, with T spacing of 1500ps between xi2 and x pulses. Aw measurements: Water activities were determined in a Decagon hygrometer mod. CX-1 (Decagon, USA) at 25°C. Before each experiment calibration curves were obtained through known salt saturated solutions and corrections of the measured values were made according to Greenspan (I 977).

X-ray diflaction: Crystalline patterns were obtained in X-ray diffractometer Phillips, model APD-15 (Phillips, Netherlands), fitted with a copper tube X-ray generator operating at 40kV and 50mA, producing a aCuK radiation of 1.54A wavelength. Data were acquired in the range 4 to 38", in 0.005" interval (28). 3 RESULTS AND DISCUSSION For simplicity, experiments will be herewith referred to as: System I: extruded waxy maize, equilibrated m chambers containing water glycerol solutions of several Aws without direct contact with the solution. Classical experiment for obtaining water sorption isotherms.

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Advances in Magnetic Resonance in Food Science

System 11: extruded waxy maize, glycerol and water, with direct contact of the starch ribbons with the glycerol water solutions of several Aws. Experiments for detecting diffusion of both the water and the glycerol. System 111: Native starch pre-equilibrated 21 days in an ambient of controlled RH. After this period the determined Aw of this native starch was 0.52. These samples were then placed in direct contact with a glycerol solution of same Aw. AAer 21 days equilibrium, extruded starch fiom both systems I and I1 had their Aws determined and the results are presented in Figure 1. Water content of these same samples are presented in Figure 2. All samples in System I1 presented an increase in

' I 0.8

'

EXprpeded

Original

.

Aws

Sample

sflm I1 0.6

.

sflm I 0.4

'

'

0.2 0.2

I

I

I

0.4

0.6

0.8

1

Aw ofthe glycerol solutions

Figure 1 - Water activity of gelatinised starch after 21 days of storage either with environments of known R H s provided by glycerol solutions of known Aws (system I); or direct contact with glycerol solutions of known Aws (system 11)

water content after the diffusion period, except the ones in contact with glycerol solution 0.34 Aw. However, their water activities experimentally determined were below the expected Aws after equilibrium. Samples of the System I lost water; even those placed in environments with relative humidities higher than the original sample (RHs = 68.7, 72.4 and 82.4%). Their observed Aws were also below the expected. The crystallinity of starch was monitored in all conditions studied and the results are presented in Figures 3 and 4, for System I and 11, respectively. The results clearly indicate that starch samples equilibrated in chambers without direct contact with glycerol solution had their crystalline pattern lost and it was not recovered even at high relative humidities (Figure 3). On the other hand, when direct contact occurred, the samples that were initially amorphous presented a gradual increase in crystalline order of starch as water activity of the glycerol solutions (and water content of the samples) increased.

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Water, Ions and Small Molecules in Food

Glass transition temperatures determined by the peak of tan 6 obtained in DMTA thermograms were also calculated for the system according to equation (1). The plasticizer effect of water was clearly observed when plotting Tg against moisture (not shown). The plot of the calculated Tgs pointed to the same pattern as for the experimental ones, but the absolute values were discrepant, being the calculated values

' I 0.8 0*9

1

A

system1

0'41i

0.2 0.3

20

10

0

40

30

70

60

SO

g H,O/lOO g dry sample

Figure 2 - Moisture of gelatinised starch, in indirect (system I) and direct (system 11) contact with glycerol solutions of known Aws

Aw

I

I

4

8

.

12

=

0.34

1

I

I

I

1

I

16

20

24

28

32

36

40

ze

Figure 3 - X-ray dieaction spectra for System I samples after 21 days of storage in chamber of various RHs produced by glycerol solutions of known Aw.

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Advances in Magnetic Resonance in Food Science

h 0.63

h 01.

4

8

1

2

1

~

2

0

2

4

2

~

3

2

3

0

4

0

20

Figure 4 - X-ray diffraction spectra for System I1 samples in direct contact with glycerol solutions of known Aw.

well below the experimental ones. Young moduli calculated from equation (2) by three point bend test of both systems are presented against the experimentally determined Tg in Figure 5 , where

Figure 5 - Log E (Young’s modulus) as a function of Tg after 21 days of storage in indirect (system I) and direct (system 11) contact with glycerol solutions of known Aws. Appearance of the samples are indicated as: R - rubbery; G - glassy.

observations about the visual aspect of both systems are also indicated. These results showed that system I was practically unaffected by the different Aw of the glycerol

Water, Ions and Small Molecules in Food

69

solutions. For system 11, however, a clear dependence was noted and, after an initial decreasing, Young's modulus increased steadily, as the water content of the samples increased. Solid-liquid relationship of the systems was assessed by NMR based on signal amplitude at two distinct times of data acquisition. Values of Tz, which are related to the mobility of the whole system, obtained by the CMPG sequence, are displayed in Figure 6. The initial amplitude (T = 11ps, detector dead time) was taken as an indication of the total protons present, whereas the value obtained at 70 ps was considered signal from solids. Solid and liquid FID amplitudes are presented in Figures 6 and 7 for both systems studied Molecular mobility and solid and liquid components, assessed in this way, were usefd to describe the system.

. t

01 0.9

system11

System I

*

_.-

".

A

:

0.4

0.5

A.

0.6

0.7

0.8

0.9

1

glycerol solution Aw

Figure 6 - Tz relaxation (CPMG)of starch samples after 21 days of storage indirectly (system I) and directly (system 11) with glycerol solutions.

Based on signal amplitude the relative composition of solid and liquid were estimated. For systems I and I1 the results presented an expected behaviour, with more liquid component on system 11, which was the one with more water uptake. For the system I11 (native starch), however, it was observed (Figure 9) that there was more solid than expected due to liquid, probably glycerol, behaving like solid. This was checked by determining the density of glycerol solution after equilibrium, which presented lower values than expected. This difision of glycerol to starch granules altered mobility behaviour only detected by using NMR. The observed change in Tz as Tg varied indicated the relationship between samples in these two states.

Advunces in Magnetic Resonance in Food Science

70 7 -

System I

6 -

0

a

a

a^

E

5 -

Original

4 -

+

B

2

B j 3

O n

0

swan I1

2 -

+*

1 0.2

I

1

I

0.4

0.6

0.8

1

glycaol solution Aw

Figure 7 - Solid amplitudes after 21 days of equilibrium with indirect (system I) and direct (system 11) contact of the extruded starch samples with glycerol solutions of known Aw.

8

Original Sample

system I

0

60

a

0

I

0.2

0.4

0,s

0.8

1

glyceol solulicm Aw

Figure 8 - Liquid amplitude (FID) of extruded starch samples after 21 days in indirect (system I) and direct (system 11) contact with glycerol solutions of known Aw.

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Water, Ions and Small Molecules in Food

15

I

/ 0

-

did 51-

0

1

2

3

4

5

6

7

8

9

10

11

@ymd sdlllm:anylopedin

Figure 9 - Calculated and experimental solid and liquid amplitude estimated from FID of native starch immersed in glycerol solution of Aw = 0.52 (system III).

Food stability is related to biopolymers mobility that is achieved by keeping them in environments preferably below their Tg. Relaxation parameters obtained by NMR also contribute to describe the idealised situation for low biopolymer mobility and represents the best tool to describe this property. This technique has proved valuable to assess this characteristic in the systems under study and complemented information derived from other techniques, providing thus a better understanding of the physical aspects of food components related to deterioration. 4 REFERENCES

Biliaderis, C.G.; Page, C.M and Maurice, T.J. (1986) Carbohydrate Pol. 6: 269-288. Cam, H.Y. and Purcell, E.M. (1954) Pys. Rev. 94: 630.

Chinachoti, P. (1993) Food Technol., Jan. 134-140. Couchman, P.R. and Karasz, F.E. (1978)MacromoZecuZes 11: 117-1 19. Davies, R.O. and Jones, G.O. (1953) A h . Phys. 8: 370-410. Greenspan, L. (1977) J. Res. Nut. Bur. Stand A. Phys. Chem. 1: 89-96. Jardim, D.C.P. (1998) MobiZidade Molecular de um Sistema: Amido Glicerol e Agua, PhD Thesis, Universidade de Siio Paulo, Brazil, 140 pp. Kalichevsky, M. T.; Jaroszkiewicz, E.M.; Ablett, S.; Blanshard, J.M.V. and Lillford, P.J. (1992) Carbohydr. Polym. 18: 77-88.

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Karel, M. (1985) In Simatos, D. and Multon, J.L. Properties of water in Foods in Relation to Quality and Stability, Martinus Nijhof Publishers, N.York, pp 153169. Meibom, A. and Gill, D. (1958) Rev. Sci. Instrum. 29: 688. Noel, T.R; Park, R. and Ring, S.G. (1996) Carbohydrate Res. 282: 193-206. Slade, L. and Levine, H. (1991) Crit. Rev. FoodSci. Nutr. 30: 115-360. Tsoubeli, M.N.; Davis, E.A. and Gordon, J. (1995) Cereal Chem. 72: 64-69.

Water Dynamics in Gelatine. Relaxation and Diffusion Analysis L. Foucat, A. Traor6 and J. P. Renou STRUCTURES TISSULAIRES ET INTERACTIONSMOLECULAIRES, SRV, INRA-THEE, 63122 ST GENkS-CHAMPANNELLE, FRANCE

1 INTRODUCTION

Gelatines are water soluble products of thermal and chemical degradation of collageneous tissues. A reversible sol-gel phase transition is one of the most characteristics of watergelatine systems. The gelling of gelatine results in the formation of the three-dimensional network of cross-links.' The specific interactions between water and gelatine chains play an important role in the stabilisationof gelatine gel. NMR relaxation rates (R, = l/T1 and R2 = l/TZ) and diffusion coefficient (D) provided an insight into the dynamics of water molecules and their local environments. The effects of various experimental variables such as gelatine concentration, gel strength, pH, temperature and measurement frequency were taken into account to characterise water-protein interactions.

2 MATERlALsANDMJXHODS Gelatine powders (Sigma) of two gel strengths (60 and 300 Bloom) were dissolved in deionised, distilled water at 60°C. NaN3 (400 ppm) was added to prevent microbiological growth, and pH adjusted with either NaOH (1M) for basic gelatines or HCI (1 M) for acid gelatines. The final concentrations were in the range of 5 to 20% (weight of dry gelatine per total weight). NMR measurements were canied out at 400 MHz on a Bruker AMX400 spectrometer (equipped with a microimaging accessory) and at 20 MHz on Minispec Bruker spectrometer PC20. Field frequency lock was not required. The temperature was controlled to f 0.1"C. The longitudinal relaxation rate (RI=l/TI) was measured using Inversion Recovery. The Cam-Purcell-Meiboom-Gill (CPMG) pulse sequence was used to determine transverse relaxation rates. The 90-180" pulse spacing (z) was varied between 50 ps and 2 ms. At 20 MHz, the acquisition time was maintained constant (800 ms) irrespective of the interpulse delay by varying the echo number between 40 and 160. Diffusion experiments were performed at 400 MHz with pulse field gradient multi-spinecho (PFGMSE) sequence.2

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3 RESULTS AND DISCUSSION For each sample studied, a single R, value was observed.' This result is explained by an exchange process between water and gelatine protons. According to the fast exchange model in the limit of diluted systems R1 can be expressed by the relation ?

where Pb is the exchangeable proton population with the longitudinal relaxation rate Rlb and (I-pb) is the fraction of bulk water protons with the relaxation rate R,. The linear increase of RI with gelatine concentration (Figure 1) is consistent with an increase of P b and in good agreement with equation 1.

0.2

I

1 ' 0

5 10 gelatine concentration (%)

15

Figure 1 Variation of the relaxationparameter RI as afinction of gelatine (300 Bloom; pH 4.85) concentration at 400 MHzfor 2 temperatures (0) : I0"C; (0) :40°C This exchange phenomenon is confirmed by the transverse relaxation measurements. Rz values are frequency dependent and vary (at 400 MHz) with the inverse of the CPMG interpulse delay (g=l/z) (Figure 2).

100

1000

g=lh (s-1)

10000

100000

Figure 2 Variation of Rz as a &netion of the inverse of interpulse delay g (=I/z) for gelatine (300 Bloom, I5%, pH 4.85, 40°C) at two frequencies 20 and 400 MHz

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In the range of g values used on Minispec (at 20 MHz), a much smaller variation in R2 was observed. Indeed, the contribution of the exchange process to the transverse relaxation mechanisms is 400 (Square of frequency ratio) times less important at 20 MHz than at 400 MHz. As a result, the R2 at 20 MHz depends almost exclusively on dipolar interactions. At 400 MHz, the behaviour of R2 as a function of g can be described (see Figure 2) by the Luz-Meiboom relation5as : R, =(R,),+k;'x[l-g

k;'xth(k,g-1)]xpb62

where k, is the rate of proton exchange, Pb the exchangeable proton population and 6 the chemical shift difference between water and exchangeable protons, (1-pb) is the fraction of bulk water protons with the transverse relaxation rate R2w.R2bis the weighted sum of spin-spin relaxation rates of water molecules at the protein interface that have lifetimes dependent on the number of binding hydrogens.6 From our experimental results, the different parameters k,(R2)o and pb62 were determined and are given in Table 1. Table 1 Exchange parameters determined >om equation 2 as a firnction of experimental parameters (gel strength,pH, temperature and gelatine concentration) Bloom pH "C concentration (R2)o (s-') k, (x lo3 s-l) ph6' ( x I O ~ S - ~ ) 300

4.85 40

300

6 40 7.15 8

5% 10% 15% 20% 15 %

0.49 f 0.03 0.6 f 0.1 0.8 f 0.1 0.8 f 0.3

7.3 f 0.3 8.3 f 0.6 8.9 f 0.4 8.8 f 0.6

13.9 f 0.6 35 f 3 60f3 96f8

0.71 f 0.08 1.OfO.1 1.5 f 0.2

8.6f 0.1 10.4fO.l 9.5 f 0.1

92*9 163 f 12 229 f 16

300

6

10

15%

2.4 f 0.2

10.3 f 0.1

60

6

40 10

15%

1.7 f 0.2 4.3 f 0.4

9.1 f 0.1 10.2 f 0.1

25

*2

111 f 10 37*4

The exchange rate k, is slightly influenced by gelatine concentration and by pH, and its values are very similar in "sol" and "gel" states, as already reported.' The (R2)o values depended on concentration and pH. Assuming a number of exchangeable-gelati-protons (hydroxyl and amino protons) equal to 0.33 per 100 g of gelatine at pH 4.85,' the P b was calculated for each concentration. From this assumption, a linear relationship was found between (R2)o and Pb (R2 = 0.993; F = 297). The intercept of 0.39 s-' corresponds to the RzWand is in good agreement with the transverse relaxation rate of pure water. From the slope of 64 s-' corresponding to R2b, the population associated with water molecules held

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by four hydrogen bonds was less than 1% of the hydration layer, consistent with previous results obtained on bovine serum albumin.6 The pbZ2values correlated closely with Pb (R2 = 0.991; F = 228). Hence 6 did not vary within the gelatine concentration range and from the slope 6 was about 1.5 ppm. This value agrees with previous results of Hills.' This 6 low value forecasts a small contribution to the exchange process at 20 MHz. (R2)0 and pb6* decrease with the increase in gel strength. The increase in Bloom number is derived from the formation of covalent intermolecular cross links. These protein-protein interactions increased at the expense of the water-protein interactions and induced the decrease in Pb. In the gel state, the diffusion through the high internal magnetic field gradient G may affect the transverse magnetisation, according to the echo amplitude detected at time t with the CPMG sequence :

[ (

M(t) = M, x exp -t x R, + D$2)]

(4)

where D is the molecular self diffusion constant. Figure 3 displays R2 versus g-2.With the first four experimental values, a linear relationship between R2 and g" was found in the gel state, whereas in the sol state no linearity was observed. Assuming a water D value of about 1 in gelatine gel, the G value will be 600 Gausdcm, according to the CarrPurcell t h e ~ r yAt . ~ higher values of g-2, R2 becomes independent of g-2.[o,'*According to the restricted diffusion theory," the microheterogeneity length of 1.5 pm was determined. This microheterogeneity distance, which may characterise some biological systemsi2 is greater than the 0.02-0.1 l m of the nucleated junction zones in gelatine gel reported by Djabourod3 from the electronic microscopy study. This inner magnetic field gradient may be attributed to the presence of air-bubbles trapped in gelatine during the gelling process.

lE09

1E-07 g-'

(s')

Figure 3 R2 as a function of g" for gelatine 15%, 300 bloom, pH 6

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Diffusion coefficient values determined at 400 MHz are reduced by a factor 1.5 to 2 compared with pure water at the same temperature, and decrease linearly with the increase in gelatine concentration. Hydration and obstruction models predict this behaviour. The study of D in function of the diffusion time (A) shows a decrease of D when A increase (Figure 4). This non-Brownian behaviour may be due either to the inner magnetic field gradient G, or to the restricted diffu~ion.’~ Approximating the distribution of the internal gradients Go by a Gaussian distribution, the relation DaPp= D[1- 0.5yZdDA(z- A/2)’]

(5)

is derived,I4 where D is the diffusion coefficient fiee from the magnetic susceptibility effects and o2stands for the variance of the internal gradients. The D,,-vs-A plot can be used to derive $. The observed dependence of D,, (Figure 4) is not well simulated by equation 5. At all events, the d values thus determined for different gelatines are very small (
2.2 0 0OO*O

g 2.0

0

0

w

1.0

0

0

Y

0

0 3

0

0

0 0

0

0

0

200

300

0.9

0

0.8

1.8 0

100

400

500

(ms 1

Figure 4 Variation of D (10” cm2/s) as a finction of the d f i s i o n time A (gelatine 300 Bloom, 15%, pH 4.85) at 10°C (0) and 40°C (0)

4 CONCLUSION

The analysis of the Rl and the R2 measured at 400 MHz showed, for the gelatine sol state, the dominant role of chemical exchange between water protons and the exchangeable macromolecule protons. The exchange rate of about 10,000 s‘l was

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determined from the Luz-Meiboom adjustment of R2 dispersion at 400 MHz. The variation of the physico-chemical conditions @H, gelatine strength, concentration and temperature) confrmed the exchange process. For the gelatine gels, the water transverse relaxation was less well described by the exchange model and a contribution of diffusion through an inner magnetic field gradient was assumed. These results suggest that gelatine gels, although macroscopically homogeneous, display heterogeneity at the microscopic level. Water diffusion in the gelatine sol and gel states is isotropic and restricted. This restriction was mainly related to macromolecular barriers.

References

1 . W. H. Harrington and P. H. Von Hippel, Adv. Protein Chem., 1961, 16, 1 . 2. D. Van Dusschoten, P. A. De Jager, H. Van As, J. Magn. Reson., 1995, A 112,237. 3. A. Traore, PhD Thesis, Universite d’Auvergne, Clermont-Ferrand, France, 1997. 4. J. R. Zimmerman and W. E. Brittin, J. Phys. Chem., 1957,61, 1328. 5. Z. Luz and S. Meiboom, J. Chem. Phys., 1963,39, 366. 6. S. H. Koenig, R. D. Brown, R. Ugolini, Mugn. Reson. Med., 1993,29, 77. 7. B. P. Hills, Molecular Physics, 1992,76, 509. 8. A. Veis, ‘The Macromolecular Chemistry of Gelatin’, B. Horecker, N. 0. Kaplan, H. A. Scheraga Ed., Academic Press, New York and London, 1964. 9. H. Y. Carr and E. M. Purcell, Phys. Rev., 1954,94,630. 10. I. Yu, J. M a p . Reson., 1993, A 104,209. 1 1 . A. Allerhand, J. Chem. Phys., 1965,44, 1 . 12. B. P. Hills, S. F. Takacs, P. S. Belton, Food Chem., 1990,37,95. 13. M. Djabourov, PhD Thesis, Universite Paris VI, France, 1986. 14. J. Zhong and J. Gore, Mugn. Reson. Med., 1991,19,276. 15. E. 0. Stejskal, J. Chem. Phys., 1965,43,3597.

Probing the Physical and Sensory Properties of Food Systems Using NMR Spectroscopy S. J. Schmidt DEPARTMENT OF FOOD SCIENCE AND HUMAN NUTRITION, UNIVERSITY OF ILLINOIS, URBANA-CHAMPAIGN, IL 61801, USA

1 INTRODUCTION An adequate supply of quality food and water is essential to the survival and growth of the world's population. However, for a large number of people, food and water are no longer consumed just for sustenance, but must now satisfy a whole host of consumer needs, desires, and expectations. The statement "man does not live on bread alone" is taking on an entirely new meaning as we prepare to enter.the 21st century. Contemporary consumers want a bountiful food supply that is safe, nutritious, convenient, low cost, with high taste appeal, high quality, ample variety, and is environmental friendly. Providing consumers with a food supply featuring all of these attributes is an extremely challenging, multi-faceted endeavor facing today's food and nutrition professionals. In order to produce a food supply with these attributes, the food and nutrition professional needs to objectively capture what the consumer desires to experience with their senses (sight, touch, smell, taste, hearing, and trigeminal sensations) and correlate this information with measurable physical and sensory properties (e.g., color, texture, moisture content, glass transition, chemical composition, sodium content, flavor profile, solubility, microbial safety, heat capacity, etc.,) of the food product. These measurable food properties can also be related to food material functionality - processability, nutritional quality, sensory quality, stability and safety. To improve our understanding and ability to predict the functionality of food materials, techniques and methods are needed that can probe the molecular level composition, structure, and dynamics of food systems in situ.

2 SELECTING A TECHNIQUE TO PROBE COMPLEX FOOD MATERIALS When selecting a technique to probe the molecular level behavior of food materials it is essential for the scientist to consider the impact the nature of the technique has on the data obtained, as well as on the subsequent analysis and interpretation of that data. Each technique applies some type of external force under specified and controlled conditions in order to elicit a response from the material during measurement; thus probing the property of interest. The measured response is moderated by the underlying principles and procedures of the technique employed, as well as the specific methods and experimental parameters used. The response obtained must be interpreted in light of

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these factors. For example, one important physical property of a food material is its glass transition (Tg). The Tg temperature is defined as the temperature at which an amorphous, glassy material softens (becomes rubbery or viscous) due to the onset of long-range coordinated molecular motion'.'. Tg can be quantified by several different techniques, including Differential Scanning Calorimetry (DSC) and Nuclear Magnetic Resonance (NMR) spectroscopy. However, as described below and displayed in Table 1, the Tg data obtained on the same or similar samples using DSC and NMR can sometimes vary widely in magnitude.

Table 1 Tg for selected food materials as measured by DSC and NMR Food Material I Tg DSC I Tp: NMR Amorphous amylopectin I 10°C/min I 'H T2rigid lattice limit maize- starch at ~ 1-(w/w 5 (60 MHZ) %) water content 56 "C midpoint 38°C 1O0C/min Am ylopectin-sugar 'H T, rigid lattice limit mixtures at 15 (w/w %) (60 MHz) water content glucose (10: 1) 42°C midpoint 4°C glucose (5:1) 38°C midpoint 38°C fructose (10: 1) I 40°C midpoint 40°C Maltodextrin (DE25) I S°C/min 'H NMR (20 MHz) at 5% water content 59°C midpoint 50°C T, (dry basis) 45°C T, solid component at 29% water content (dry basis) Amorphous lactose at 6.4% moisture content 6.2% moisture content Maltose at 10% water content (dry basis)

-4 1"C midpoint S"C/min 33°C onset S"C/min -2°C midpoint

-45°C TI -41°C T, solid component 'H NMR TI 30°C onset 'H T, rigid component (300MHz) 9.5"C

Ref.

3 4

5

4

In the case of DSC, the Tg is obtained by measuring the temperature at which a step change in the heat capacity occurs between the glassy (lower heat capacity) and the rubbery-liquid (higher heat capacity) states of the material. The Tg obtained using DSC reflects the macroscopic mobility of the entire system. In the case of NMR, the Tg can be obtained by measuring the temperature at which a change in molecular mobility is observed. The nuclei and specific NMR method selected to measure Tg depends on both the attributes of the component (water or solids, referring to the non-water components) and the type of mobility being assessed. For example, the Tg obtained using 'H Hahnspin echo NMR (transverse relaxation time, T,) reflects the rotational mobility of the water protons in the sample; the Tg obtained using 'H pulse-field gradient spin echo (PGSE) NMR reflects the translational mobility of the water protons in the sample; and the Tg obtained using "C cross-polarization magic angle spinning (CP-MAS) NMR reflects the carbon backbone mobility in the sample. The Tg values obtained by all three NMR techniques, 'H T,, 'H PGSE and I3C CP-MAS NMR, are reporting the molecular

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mobility of the system that they experience, based on their unique point of view; all are correct, but not necessarily equal. It becomes quickly apparent that not only can the Tg values obtained within the NMR technique vary, but also between the NMR and DSC techniques. This technique dependency issue has far reaching implications for correctly understanding and predicting food material functionality. Foods are complex dynamically heterogeneous materials making their molecular level behavior difficult to probe. Eadsg has classified the complexity of food materials into three main dimensions or types: 1) compositional, 2) structural, and 3) dynamical complexity. Table 2 illustrates the extensive nature of each complexity by using a variety of food material examples.

The complex dynamically heterogeneous nature of food materials points to Nuclear Magnetic Resonance (NMR) spectroscopy and Magnetic Resonance Imaging (MRI) as the methods of choice for exploring their molecular level behavior in situ. NMR and MRI are extremely versatile, non-invasive, non-destructive techniques which can be used to extract a plethora of rich, static and dynamic information from intact, highly complex, heterogeneous materials. The magnitude of length and time scales over which NMR and MRI information is available is very large, ranging from chemical bond distances (-10" meters; chemical shift effects) to diffusion distances meters; translational mobility) to imaging distances (- 1 meter; spatial information) for length scales and from picoseconds (10" seconds) to tens of seconds for time scales". The complex nature of food materials profoundly affects the quality of NMR spectroscopic measurements and analysis, specifically the selectivity, sensitivity, and spatio-temporal resolutiong. The good news is that strategic analytical NMR approaches are being developed to overcome, embrace and even explore the complexities inherent to food Eadsg has recently compiled an outstanding theoretical, as well as

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practical resource on how to strategically develop and implement NMR techniques to investigate complex, intact food materials.

3 PROBING THE PHYSICAL AND SENSORY PROPERTIES O F FOOD MATERIALS USING NMR SPECTROSCOPY All materials, including foods, poses a unique set of properties or characteristic that allows us to recognize and distinguish them from other materials. The goal of the food scientist is to objectively quantify and evaluate these properties so as to monitor existing food processes and products to assure consumers of consistent quality and to develop new food processes and products that meet the needs and desires of the consumer. The physical properties of a material are those properties which can be observed and measured without changing the chemical identity of the material. Physical properties can usually be ascribed to one of four categories: compositional, structural, dynamical, and thermal. The sensory properties of a material are those properties that can be perceived through one or more of the senses of an observer. Sensory properties can usually be ascribed to one of three categorizes: appearance, as sensed by sight, texture (kinesthetics), as sensed by touch (in the hand or mouth), and flavor as sensed by taste (receptor cells in the taste buds on the tongue) and by smell (olfactory epithelium in the nose)”. There are four basic (or primary) taste sensations - sweet, salt, sour and bitter. Some sensory properties can be classified under more than one category, depending on how the propeny was assessed. For example, viscosity can be assessed by sight (the appearance sensory category) or by touch (the texture sensory category). Some food material properties, such as texture and color, can be evaluated using both human senses and instrumental methods. The correlation between sensory and instrumental measures is by definition a psychophysical relationship - a relationship between the perceived attributes of an object (food) and the physiochemical dimensions of the object that elicit those perceptions”. Despite a great deal of effort, instrumental methods that are used to measure the same properties as can be assessed by the human senses are not as accurate, sensitive, discriminating or comprehensive. For example, numerous simple, as well as advanced instruments and techniques have been developed to assess the textural properties of food products (e.g., consistometer, penetrometer, rotoviscometer, viscograph, texture analyzer, rheometer) however, they have yet to replicate the vast array of sensory responses that can be obtained from human panelists when they are asked to evaluate the texture of a food product through activities such as touching, biting, chewing and/or swallowing.

3.1 How Can NMR Be Used to Probe the Physical and Sensory Properties of Food Materials? In order to use NMR spectroscopy to probe the physical and sensory properties of food materials we must identify a relationship (either direct or indirect) between the measurable NMR parameters (also called NMR observables by Eads’) and the physical and sensory properties of interest. The association between NMR observables and physical and sensory properties is based on a series of connections between fundamental spectroscopic events and molecular, microscopic and macroscopic properties of the sample. Molecular properties (i.e., structure, symmetry, motion, interaction and order)

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are directly related to fundamental spectroscopic events, while microscopic and macroscopic properties are indirectly related. For example, two NMR observables that can be measured in an NMR experiment are the peak height or area (number) and the longitudinal and transverse relaxation rates (rotational mobility) of the protons ('H) in the sample. These two NMR observables can then be related to specific physical properties of the food material, such as the moisture andor oil content and the lipid solid-fat content (SFC), respectively. If the lipid SFC is obtained as a function of temperature, the resulting curve can then be used to predict a number of physical and/or sensory properties of the lipid material, such as hardness, heat resistance, mouthfeel and flavor release". In addition, insight into the crystallization process of fat blends can be obtained by monitoring the SFC of the lipid as a function of time. Table 3 lists several physical properties important. to food materials and examples of NMR techniques which have been used to measure these properties in model and real food systems. muterials and representative examptes of NMR pure these properties in model and real food

Compositional

Structural

Representative NMR Application(s) Determination of moisture and oil content in amount of water, oilseeds using low resolution 'H NMR carbohydrates [simple ~pectroscopy'~ and complex], proteins, I Increase in sucrose, fructose and glucose lipids, ions and other content during ripening of banana tissue trace components using high resolution 'Hmagic angle spinning (MAS) NMR spectroscopy with water peak suppre~sion'~ Solid-fat content [SFC] SFC of butter and dairy spreads using low resolution time domain 'HNMR - amount of solids contained in a lipid spectroscopy with analysis of amplitudes 1 sample at a specified ' for solid-like ,and liquid-like relaxation components16 temperature Authentication of fruit juices and origin of Confirm authenticity and detect adulteration - wines using high resolution site-specific determination of natural isotope fractionation (SNIF) NMR material purity and spectroscopy1' origin Physical phases (or Possible phases (crystalline, rigid, viscous, states) - distribution of liquid) in ice cream mix using wide-line and food components among high resolution 'H NMR spectroscopy" co-existing phases Molecular structure Determination of the structure of crystalline organization of atoms a-D-glucose using high resolution "C solid contained in a molecule state NMR spectroscopy with subsequent interpretation of spectral features"

I Proximate Analysis -

-

84

Category Structural (con't)

Physical Property Microstructure - short and long range spatial arrangements

Molecular level component interactions

-I

Dynamical

Macrostructure features

Molecular motion longitudinal and transverse relaxation rates (Rotational correlation time)

Molecular mobility Translational diffusion coefficient Rheology

Glass transition temperature (Tg) coefficient (h,)

Advances in Magnetic Resonance in Food Science

Representative NMR Application(s) Determination of the internal organization of the starch granule components using I3C cross polarization magic angle spinning (CP-MAS) and single-pulse MAS NMR spectroscopy" Molecular size and shape of the polymer amylopectin using 'H pulse-field gradient NMR2' Interaction of sorbed water with potato starch granules by probing the pulse spacing dependence of CPMG T2experiments" Porosity of agarose gels using 'H NMR transverse relaxation times (T2)23 Surface area of solid silica powders using low resolution 'H NMR TI measurementsz4 Vascular architecture of apple fruit using 'H T, weighted MR imagingz Mobility of water in sucrose and lactose solutions using IH,'H and 170NMR R, relaxation rates for water mobilityz6 Mobility of solid-like starch components in low solids systems using cross-relaxation spectros~opy~~ Mobility of starch backbone carbons (Tip) in high solid systems using "C CP-MAS NMR~* Mobility of sodium ions in ionic and nonionic gum solutions using 23NaNMR R,,R, and R,* relaxation rates" Measurement of water and fat self-diffusion coefficients in cheese using pulse-field gradient NMR3* Viscosity of polyacrylamide solutions using MR Imaging3' Non-linear viscosities of various food materials, including cream, egg whites and tomato sauce, using NMR microscop? Tg of maltodextrin (DE25)using low resolution 'HNMR TI and T,s measurements' Determination of the fluid-to-particle h, during aseptic processing of potato cubes using chemical shift MR Imaging, using temperature mapping and finite element modeling techniques33

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A comprehensive examination of the relationship between NMR and MRI observables and the analytical properties of intact food materials is outlined in a series of three tables by Eadsg. The first table in Eadsg series contains the NMR and MRI observables, the second table in the series contains the molecular properties (structure, symmetry, motion, interaction and order) which can be determined directly from the MR observables, and the third table in the series presents various sample properties and analytical quantities and their molecular origins. Similar to Table 3 for the physical properties, Table 4 lists several sensory properties of food materials and representative examples of NMR techniques which have been used to measure these properties in model and real food systems. Table 4 Some sensory properties of food materials and representative examples of NMR techniques whic:h can be used to measure ihese properties in model and real food system Category Sensory Property Representative NMR Application Appearance MR Imaging for non-destructive internal Internal quality evaluation quality evaluation measurements of fruits and vegetable^'^ Texture Mechanical (tough and 'HNMR Tz CPMG (20 MHz and 2OOC) and fibrous) and dryness apparent population measurements of frozen (i.e., moisture and fat) cod3' characteristics evaluated in the mouth Flavor: Taste Saltiness High field 23NaNMR R,* of chicken soup systems containing various thickener^^^ Sweetness Relationship between sweet taste and water perturbation in a, a-trehalose using 'H NMR T, and T, CPMG (20 MHz and 2O"C)" Structural identification of the bitter tasting compounds in cassava using 'H and "C NMR s p e c t r o s ~ o p y ~ ~ Structural identification of the sour tasting compound isolated from beef broth using- 'H and i3C NMR s p e c t r o s ~ o p y ~ ~ Flavor: Smell Structural identification of the seafood aroma compounds using 'Hand "C NMR spectroscopy@

A new MRI technique that could be of potential interest for investigating sensory perceptions of foods by human is functional mapping of the brain using MRI (~?v~RI)~'. 3.2 Special Concerns When Relating Sensory and Instrumental Measures One question that needs to be considered when attempting to relate sensory and instrumental measures is which technique produces the "true" or "correct" response, the instrument or the human subject? At first this seems like an easy question to answer, we all know that instruments are more accurate, reliable and objective than human subjects when measuring the properties of an object. But is that really true when assessing the sensory attributes of a food? Cardello'* ardently contends, and I agree, that "sensory data

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are the primary criteria to which all other measures must be compared and on which all other measures rely for their validity." Sensory data contains information about both the compositional, structural and internal physical forces contained in the food material, as well as the physiological, psychological and cognitive variables that influence the perceptional experiences of the human interacting with the food material. Instrumental measures can only assess the former, human subjects can assess both with the greatest of accuracy, sensitivity and individuality. The variability inherent in sensory testing is due in large part to the genuine variation associated with these physiological, psychological and cognitive variables. However, if we want to measure perception then these variables are important and provide very valuable information about the food material (and about the human!). Despite the shortcomings of using instrumental measures for predicting sensory properties, instrumental methods are still very useful for understanding and predicting food material functionality and are receiving continued attention12. Two additional factors that need to be considered when relating sensory measures and instrumental measures, such as NMR observables, are: 1) instrumental measures should not be correlated to affective sensory measures, such as those obtained by the hedonic scale, since these test are designed to measure the subjective (emotionaVfeelings) response of the consumer (e.g., how much do they like or dislike the food product?), not an objective response (e.g., how hardness is the food product?), and 2) since sensory data is nonlinear, simple linear regression statistics should not be used to relate sensory and instrumental data; nonlinear models should be considered. Sensory tests also have limitations that need to be taken into account. One advantage worth noting that instruments have over human panelists is that instruments are not influenced by the various factors that can adversely affect human judgements, such as physiological factors (e.g., adaptation error - the change in sensitivity to a given stimulus as a result of continued exposure), psychological factors (e.g., expectation error information given with the sample may trigger positive or negative preconceived ideas about the sample), poor physical conditions (e.g., having a common cold), and poor environmental conditions ( e g , undesirable interactions between panel members due to inadequate physical separation during 4 PROBING PHYSICAL PROPERTIES - AN EXAMPLE An important physical property of a food material is the mobility (dynamics) of the water and solid components. The mobility is useful for predicting the stability of the food matenall". In a recent study2*,a suite of NMR and DSC techniques were used to fully characterize the water and solid component mobility and the T g of three model food systems: sucrose, instant dent #1 corn starch, and a 1:l sucroseistarch mixture. Germination of Aspergillus niger conidia was used to assess the microbial stability of each model system. Samples were equilibrated at 20OC using 10 saturated salt solutions made with either H 2 0 or D 2 0 and ranging in water activity from 0.331 to 0.976. The D 2 0 equilibrated samples were used in the 2H NMR experiments. The Tg values were determined from the second scan using a TA Instruments DSC (TA Instruments, New Castle, DE) with a scanning rate of 20"C/min. Tg values were also calculated using either the Gordon-Taylor (sucrose and starch) or the Couchman-Karasz (sucrosdstarch mixture) equations. The 2H NMR relaxation rates and the 13C NMR spin-lattice relaxation times in the rotating frame (Tip) were obtained using a GN-300WB spectrometer (General Electric, Inc., Fremont, CA) operating at 46.06 MHz and 75.47

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MHz, respectively, using a 10 mm multinuclear probe. The *H NMR R,, R,, and R,* values were obtained using the inversion-recovery pulse sequence, the Hahn-spin echo pulse sequence and the line width at half peak height multiplied by IC, respectively. The TI, values were obtained from solid state CP MAS I3C NMR spectra of starch using a spin lock field of 45.5 kHz. The water self-diffusion coefficient (D) values were obtained using a UI-SOOWB spectrometer (Varian Associates Inc., Palo Alto, CA) operating at 499.87 MHz proton resonance frequency using a 5 mm multinuclear probe. The D values were obtained using a 'H pulse-field gradient spin-echo (PGSE) pulse sequence. All NMR measurements were done in duplicate at 20OC. Samples were inoculated with Aspergillus niger conidia and incubated at 2OoC for 30 days. The samples were examined for mold germination at 400X magnification everyday for 30 days. Figure 1 shows the DSC midpoint (points) and calculated Tg (lines) values for the three model systems plotted as a function of weight fraction of solids (g soliddg sample; subsequently referred to as weight fraction). In order to compare the DSC Tg results to the NMR and mold germination results, we need to compare the changes in the NMR and mold germination experiments at the weight fractions which corresponds to the DSC Tg values at 2OoC (the temperature at which the NMR and mold germination experiments were performed). The weight fraction for each of the model systems at the DSC Tg at 2OoC are given in Figure 1: sucrose 0.95 (5%moisture content, wet basis ); sucrosdstarch 0.87 (13% moisture content, wet basis); and starch 0.78 (22% moisture content, wet basis). 3

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Figure 1 Plot of the DSC midpoint (points) and calculated Tg (lines) values for sucrose, instant dent #I corn starch, and a I:] sucrose/starch mixture. Arrowspoint to the weight fraction of solids corresponding to the temperature at which the NMR and mold germination experiments were done (20"C)

In general, the 'H NMR R,, R,, and R,* values for all three model systems (data not shown) increased as the weight fraction increased (moisture content decreased). In the case of sucrose, the samples which could be measured (diluted [0.39 weight fraction] to saturated r0.64 weight fraction] solutions; crystalline sucrose samples could not be measured) had weight fractions much less than the DSC Tg weight fraction of 0.95. Thus, a comparison between the 'H NMR relaxation rate data for sucrose and the DSC Tg

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data was not possible. In the case of starch and sucroselstarch model systems, R,* began to increase greatly near the corresponding DSC weight fractions of 0.78 and 0.87, respectively. This increase in R,* is due to chemical shift anisotropy produced by electrons which shield the nucleus from the applied magnetic field, which cause excessive line broadening in the 'H NMR spectrum. R, and R, for the starch and sucrosdstarch model systems increased only moderately over the entire weight fraction range measured and exhibited no marked change at the corresponding DSC Tg weight fractions. A semi-log plot of the TI, as a function of weight fraction for each resolved carbon signal in the starch samples is shown in Figure 2. The trends observed in the TI, measurements as a function of weight fraction, ranging from 0.70 to 0.93, were the same for each resolved carbon signal in all starch samples. The TI, values for both the backbone (C, and C,) and ring carbons (C,, C3and C,) of the starch are larger than that of the side chain carbon (C6).

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Figure 2 A semi-log plot of the "C TIPvalues as a function of solid weightfraction for each resolved carbon signal of instant dent #I starch. The arrow point to the weight fraction of solids which corresponds to the DSC Tg at 20°C

For all carbons, the TI, values increased slightly with decreasing weight fraction from 0.93 to 0.80. This increase may, in part, be due to cross-polarization contributions from the water protons absorbed on the starch. At weight fractions less than 0.80 there was an appreciable decrease (i.e., about a two fold decrease in TI, values, with the values dropping below the higher weight fraction TI, values) in the T,, values of both the backbone and ring carbons. As can be seen in Figure 2, there are two starch samples below the weight fraction DSC Tg at 2OOC. This suggests that these two starch samples have greater solid molecular mobility and are in the rubbery-liquid state compared to the higherweight fraction starch samples that are in the glassy state. These results indicate that the "C TI, measurements are sensitive to the backbone carbons motions of starch in the tenth of kHz region, which is characteristic of relatively long-range cooperative motions of solid polymers.

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Figure 3 shows the water self-diffusion coefficients (D) as a function of weight fraction for the three model systems. In the case of sucrose, the samples which could be measured had weight fractions much less than the weight fraction of 0.95 (as previously discussed for the sucrose 'H NMR relaxation rate results). For the starch and sucrose/starch model systems, D decreased approximately 10 fold near the correspond DSC weight fractions (solid and large dashed arrows, respectively). Weight Fraction

.- 0.4

0.4

Starch SucroselStarch

0.5 0.6 0.7 0.8 0.9 1 Weight Fraction of Solids (g solids/g sample)

Figure 3 Plot of the water self-difJusion coeflcients as a function of weight fraction of solids for sucrose, instant dent #I corn starch, and a 1:l sucrosehtarch mixture. The arrows point to the weightfraction of solids which corresponds to the DSC Tg values at 20oc

For all three model systems, the mold conidia germination was observed only for the samples with weight fractions less than the DSC Tg weight fractions at 20°C. In other words, mold conidia germination occurred only in those samples which had DSC Tg values below the experimental temperature of 2OoC (rubbery-liquid state samples). All samples which had DSC Tg values above the experimental temperature (2OOC) did not support mold conidia germination (glassy state samples). It can be concluded from this study that the translational mobility of water molecules (as monitored by the water self-diffusion coefficient, D), the solids mobility (as monitored by the "C T,,),and the macroscopic mobility (as monitored by the DSC Tg) of the three model food systems can be used as effective tools for predicting food stability. These parameters can also be used for improving current food systems, as well as developing new ones. 5 PROBING SENSORY PROPERTIES - AN EXAMPLE

Taste is sensed by the receptor cells in the taste bud on the tongue. In order to be tasted a substance must be dissolved and make contact with a taste receptor cell. Once the stimulus (sweet, salt, sour, and bitter) interacts with the taste receptor cell a cascade of reactions occur resulting in cell depolarization and release of neurotransmitters that convey taste information between the neurons and the brain4'. The ability of the stimulus

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to interact with the receptor cell is dependent on a variety of factors, including the structure and concentration of the stimulus, the properties of the medium containing the stimulus (e.g.. chemical composition, viscosity, pH, temperature), and the interaction of the stimulus with water and other ingredients. It has been postulated that the molecular mobility of the stimulus, as measured by NMR spectroscopy, is related to the perceived primary taste intensity of the stimulus46. The relationship between perceived saltiness and the binding of Na' in aqueous ionic and non-ionic gum systems has been studied by 23NaNMR spectroscopy. In two separate studies, it was observed that ionic gum solutions (carboxymethycellulose. xanthan, and carrageenan) received lower saltiness intensity ratings compared to nonionic gum solutions (methycellulose, guar, and locust bean) with equivalent added Na' contents. The lower intensity ratings of the ionic gum solutions were attributed to the interaction between Na' and the ionic g ~ m s ~ The ~ * Na+-gum ~ ~ . interaction was investigated by measuring the 23Na NMR transverse relaxation rate (Rz*)of the gum systems. In both studies, the ionic gum solutions had higher R,* values (lower rotational mobility) compared to the non-ionic gum solutions, in the same added Na' concentration range, indicating Na'-ionic gum interactions. More recently, the relationship between perceived primary taste intensity of two K' containing salts was investigated4*. The study was initiated because of two recent trends in the food industry: 1) a desire to reduce the amount of sodium added to processed foods, and 2) a movement to fortify foods with select vitamins and mineral. These trends have led us to begin investigating the sensory attributes of potassium salts. Potassium chloride has been used as a salt substitute and is being considered for fortification purposes. Rosett et al.49investigated the flavor attributes of KC1 in distilled, deionized water at KC1 concentrations above the taste threshold concentration of 0.08% (w/v). Twelve sensory panelists detected all four primary tastes, in the following order of perceived intensity bitter (9) > sweet (7) > salty ( 5 ) > sour (3). The relationship between perception of the primary tastes and the binding of K' in aqueous solutions containing three common food thickeners, xanthan gum, instant corn starch, and wheat flour (at 0.3 %) was investigated. In addition to the thickener, the model systems contained: sucrose at 3.56%, fructose at 1.53%, and one of two sources of added K' with a balance of water. This liquid model system was developed to mimic a standard batter formulation. The two sources of K' were: 1) KC1, and 2) a 1:l molar blend of KH,PO, and KHCO,, which in the liquid reacted to form K,HPO,. Two levels of added K' were used 200 mg and 400 mg per 100 g of sample. A 14 member sensory panel judged the intensity of the twelve samples (three thickeners, two sources of K', and two levels of added K') at room temperature using an anchored (e.g., not sweet and very sweet) unstructured line scale (129 mm). The 39KNMR R2* values were obtained using a GN-300WB spectrometer (General Electric, Inc., Fremont, CA) operating at 14.0035 MHz. Single-pulse experiments were preformed in duplicate at 20-12OC using 20 mm NMR tubes. The Rz* values were calculated by multiplying the line width at half peak height by R . The Rz* values for pure 1M aqueous solutions of KCI, KH,P04 and KHC03 were 25.23, 33.48, and 30.18 sec-*,respectively. Comparison between thickener types was not done in this study due to the major three differences between the thickeners: 1) xanthan contained a much greater amount of endogenous K' (36,037 ppm, as measured by inductively coupled plasma-atomic emissions spectroscopy) than the starch (0 ppm) or flour (1 195 ppm), 2) xanthan had a greater viscosity than the other two thickeners and a different

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sensory studies for the discovery of features of food systems that affect perception". NMR can be used to not only identify the relationship between molecular structure and taste perception, but also to study further the relationships between the dynamics of food systems and sensory attributes. Some future and ongoing applications of NMR techniques in the sensory science field include: 1) identification of taste threshold levels based on quantification of the levels of preferred configurations and/or conformations of compounds (e.g., the difference in perceived sweetness of the four fructose anomers); 2) the effect of water mobility on sweetness and other sensory attributes; 3) the use of MRI to map the distribution and mobility of components such as Na' and water that are

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Figure 4B The perceived sweet, salty, sour, and bitter taste intensities (129 mm unstructured line scale) of two 0.3% xanthan gum solutions (including 3.56% sucrose, 1.538 fructose and a balance of water) containing 400 mg of added KCl or a I:I blend of KH,P04 and KHCO, (which in the liquid reacted to form K,HP04) important to sensory properties; 4) the correlation of translational mobility measurements with perceived taste intensities; 5 ) the investigation of competitive binding effects on sensory perception as determined by the relative molecular mobilities of various added, as well as endogenous ions; 6 ) the measurement of viscosity and studies of the influence of macro- and micro-viscosity on taste; 7) expanded use of NMR measurements as a predictor of taste intensity and texture qualities; and 8) the development and use of online NMR and MRI quality assurance techniques.

6 SUMMARY NMR is an extremely useful technique that can be used to probe the origins of structure and function in complex dynamically heterogeneous food materials, which, in turn, can be related to the macroscopic physical and sensory properties of the food materials and to human intentions. The potential of using NMR spectroscopy to probe the physical and sensory properties of food materials is an exciting research area, open and waiting for further exploration and explorers!

7 ACKNOWLEDGEMENTS I would like to thank Dr. Thomas Eads, a valued friend and colleague, for the many enlightening discussions we have had about NMR and food. I would also like to thank my graduate students who have happily accompanied me on the exciting journey of placing food materials into NMR and MRI instruments. The secretarial assistance of Barbara Vandeventer and Donelle Testory is gratefully appreciated.

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References

1. L. Sperling, ‘Introduction to Physical Polymer Science’, John Wiley & Sons, Inc.. New York, 1992, Second Edition, Chapter 1, p. 1. 2. Y. Roos, 1995. ‘Phase Transitions in Foods’, Academic Press, New York, Chapter 2, page 19. 3. M. Kalichevsky, E. Jaroszkiewicz, S. Ablett, J. Blanshard, P. Lillford, Carbohydrate Polymers, 1992,18.77. 4. M. Kalichevsky, E. Jaroszkiewicz. J. Blanshard, Polymer, 1993,34(2), 346. 5. R. Ruan. P. Chen, Water in Foods and Biological Materials: A Nuclear Magnetic Resonance Approach. Technomic Publishing Co. Inc.. Lancaster, PA, 1998,269. 6. R. Lloyd, X. Chen, J. Hargreaves, Int. J. Food Sci. and Tech., 1996,31,305. 7. Y. Roos, M. Karel, J. Food Sci., 1991,56(6), 1676. 8. B. Hills, K. Pardoe, J. Mol. Liquids, 1995,63,229. . 9. T. Eads, Principles for nuclear magnetic resonance analysis of intact food materials, in Spectral Methods in Food Analysis, Instrumentation and Applications, M. Mossoba. ed., Marcel Dekker, Inc., (in press). 10. P. Belton, M. McCarthy, ‘Annual Reports on N M R Spectroscopy’, Academic Press, Inc., San Diego, CA, Volume 31, Chapter 1, p. 1. 11. A. Kramer, Food Techn., 1972,26(1), 34. 12. A. Cardello, Cereal Foods World, 1994,39(8), 567. 13. M. Gribnau, Trendr in Food Sci. and Techn.. 1992,3,186. 14. P. Garnbhir, Trends in FoodSci. & Tech., 1992, 3, 191. 15. Q. Ni, T. Eads, J. Agric. FoodChem, 1993,41,1035. 16. N. Wahlgren, T. Drakenberg, Annual Reports on NMR Spectroscopy, 1995,31,275. 17. G . Martin, M. Martin, ‘Annual Reports on NMR Spectroscopy’, 1995,31,81. 18. T. Eads, ‘Annual Reports on N M R Spectroscopy’, 1995,31,143. 19. P. Pfeffer, K. Hicks, W., Earl, Carbohydr.Res., 1983a, 111, 181. 20. K. Morgan, R. Furneaux, N. Larsen, Carbohydr. Res.. 1995,276,387. 21. P. Callaghan, J. Lelievre, Biopolymers, 1985,24,441. 22. S . Tanner, B. Hills, R. Parker, J. Chem SOC.Furaday Trans., 1991,87,2613. 23. S . Ablett, A. Darke, P. Lillford, ‘Water Relationships in Food’, Plenum Press, New York, 1991,, 453. 24. P. Davis, D. Gallegos, D. Smith, Powder Technol.. 1987,53,39. 25. J. MacFall, G. Johnson, Can.J. Boz., 1994,72, 1561. 26. H. Lai, S. Schmidt, J. Agric. Food Chem, 1991.39, 1921. 21. J. Wu, R. Bryant, T. Eads, J. Agric. Food Chem, 1992,40,449. 28. Y. Kou, Mobility and Stability Characterization of Model Food Systems Using NMR, DSC, and Conidia Germination Techniques, 1998, Ph.D. Thesis, University of Illinois, Urbana, IL. 29. L. Shirley, S. Schmidt, Food Hydrocolloids, 1993,7(2), 147. 30. P. Callaghan, K. Jolley, R. Humphrey, J. Colloidand Interjace Sci., 1983,93(2), 521. 31. R. Powell, J. Maneval, J. Seymour, K. McCarthy, M. McCarthy, J. Rheol., 1994, 38(5), 1465. 32. M. Britton, P. Callaghan, Magn. Reson. Chem, 1997,35, S37. 33. C. Kantt, S. Schmidt, C. Sizer, S. Palaniappan, J. Litchfield, J. Food Sci., 1998,63(2), 305. 34. C. Clark, P. Hockings, D. Joyce, R. Mazucco, Postharvest Biology and Techn., 1997, 11.1.

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35. C. Steen, P. Lambelet, J. Sci. FoodAgric., 1997,75268. 36. T. Rosett, S. Kendregan, Y. Gao. S. Schmidt, B, Klein, J. Food Sci., 1996,61(5), 1099. 37. M. Portmann, G. Birch, J. Sci. FoodAgric., 1995,69,275. 38. N. King, J. Bradbury, J. Sci. FoodAgric., 1995,68,223. 39. K. Shima, N. Yamada, E. Suzuki, T. Harada, J. Agric. Food Chem, 1998,46,1465. 40. A. Kobayashi, K. Kubota, M. Iwamoto, H. Tamura, J. Agric. Food Chem, 1989,37, 151. 41. R. Turner. Seminars in the Neurosciences, 1995,7(3), 179. 42. M. Meilgaard, G. Civille, B. Carr, ‘Sensory Evaluation Techniques,’ CRC Press, Inc. Boca Raton. FL, 1987, Chapter 1 p. 1. 43. L. Poste, D. Mackie, G. Butler, E. Larmond, ‘Laboratory Methods For Sensory Analysis Of Food’, Minister of Supply and Services, Canada, 1991, Chapter 2, p. 21. 44.K. Lang, “Physical, Chemical and Microbiological Characterization of Polymer and Solute Bound Water”, 1981, PbD. Thesis, University.of Illinois, Urbana, IL. 45. H. Charley, C. Weaver, ‘Foods A Scientific Approach’, Prentice Hall, New Jersey, 3rd Edition, 1998, Chapter 2, p.21. 46. N. Ayya, “Physicochemical and Sensory Characterisation of Interaction in NaC1Hydrocolloid Systems”, 1988, Ph. D. Thesis, University of Illinois, Urbana, IL. 47. T. Rosett, L. Shirley, S. Schmidt, B. Klein, J. FoodSci., 1994,59(1), 206. 48. N. Mentavlos, T. Mahawanich, unpublished data, Department of Food Science and Human Nutrition, University of Illinois, Urbana, IL, 1988. 49. T. Rosett, Z. Wu, S. Schmidt, D. Ennis, B.Klein, J. Food Sci., 1995,60(4), 849. 50. J. Mennella, Food Technology, 1998,52(8), 58. 51. T. Robertson, S. Schmidt, B. Klein, Trenh in Food Sci. and Techn., 1992,3,236.

1

H Relaxation of Hydrated Carbohydrate Systems

J. M. V. Blanshard,' W. Derbyshire,' W. MacNaughtan,' S . Ablett? D. Martin2 and M. J. Izzard2

' DEFT APPLIED BIOCHEMISTRY AND FOOD SCIENCE, UNIVERSITY OF NOTTINGHAM, SUITON BONINGTON LEI2 5RD, UK

* UNILEVER RESEARCH COLWORTH, COLWORTH HOUSE, SHARNBROOK, BEDFORD MK44 lLQ, UK

ABSTRACT The work reported was undertaken as a contribution to the MMF Project with the objective of providing information on the molecular scale dynamics of the constituents in sugar/water, polymer/water and polymer/sugar/water systems over temperature and composition ranges straddling the glass transition. The overall intent is to correlate the molecular mobilities represented by N M R Relaxation with bulk properties of interest to the Food Processor. Attention will be concentrated upon pullulan as a representative polymer and upon the sugars glucose and maltose. Hydration levels vary from 10 to 90%. Some real foods, eg raisins boiled sweets and baked products have been subjected to similar examination to confirm that the procedures have a general validity and are not restricted to simple model systems. The NMR interest has been concerned with the confirmation that the different relaxation components can be relatively unambiguously assigned to the different constituents and that molecular scale mobilities can be derived. Whilst information has been obtained on many features, eg the properties above 273K, and the time course of phase changes induced by passage through the glass transition these are not reported here. Ice formation occurs in the water rich samples, but in general the ice has not been of interest, attention has been focussed upon the properties of the non frozen water and upon those of the sugar and/or the polymer. This attention has been concentrated in the sub Tg region. The amplitude of the signal of the non fiozen water and its temperature dependence coupled with that of its spin spin decay rate, change systematically with concentration and the identity of the other species. Some changes in polymer and sugar signal are observable. These assume the form of some changes in decay rate and the onset of a beat pattern in the Free Induction Decay.

INTRODUCTION The work outlined in this report has been undertaken as one contribution to a multi disciplinary investigation of the significance of the glass transition in foods and model foods. The intent is to utilise 1H Nh4R relaxation as a measure of the molecular scale interactions and dynamics, and to relate these to macroscopic properties, with the aim of developing an improved understanding and maybe usable protocols for characterisation of samples and the prediction of process properties and end product behaviour. The potential benefits are significant, many of the other testing methods involve the submission of samples in some narrowly prescribed, and possibly

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inconvenient form, NMR signals can be obtained whatever the physical form of the sample. Furthermore the Nh4R Relaxation measurements can be made on the low cost bench top equipment already in extensive use in the Industry. In this report attention is directed towards an appraisal of the performance of NMR relaxation in the task of probing the molecular scale interactions and dynamics. Some test criteria can be specified, any of the relaxation measurements, spin lattice, spin spin, rotating kame etc. can be expected to yield a complex decay resolvable into several components. The premise is that the amplitudes of those decays are relatable to the concentrations of the constituents generating that component, whilst the resolved decay rates are relatable to the dynamics. The Project requires a demonstration that resolution can be achieved robustly and unambiguously, accompanied by a credible assignment of the components. This objective has decreed the use of relatively simple model systems, sugar/water and polymer/sugar/water. In principle components ascribable to each constituent can be sought. Whilst this is feasible for such simple model systems it might not be practicable for real foods. An alternative phenomenological approach seeks to utilise the minimum number, of components, in practice two, capable of providing a glass transition based description. A resolution into components inevitably forces an assumption of the decay shape, or decay shapes. This requirement is enhanced by the necessity of an extrapolation of the signal decay to zero time to determine the signal amplitudes. The selection of correct decay hnctions is necessary if the parameters derived are to have a numerical integrity, the use of incorrect functions will restrict the use of the technique to a comparative one where relative differences are recorded as temperature or composition is changed. It might be hoped that a comparison of the different relaxation processes will serve to identify and characterise the specific motions responsible for the observed relaxation. Accompanying this is a requirement to devise simple and rapid procedures that can be used by a practitioner not skilled in the arts of NMR. This paper reports the experiences to date of devising and applying a programme of first order relaxation procedures in order to provide a general framework which is usable directly, but which would also serve as a base for subsequent more detailed and sophisticated investigations to address specific issues identified by the first order study. Included in these are solid state N M R investigations of the behaviour of the “solid” component. There is additional parallel work on the diffusion of the water and sugar components in these syslems.

EXPERIMENTAL PROCEDURES 1H Nmr relaxation measurements, FID and CPMG, were made using two instruments, a multi nuclear Bruker CXP60 spectrometerand a 20MHz Maran model bench top spectrometer supplied by Resonance Instruments. In parallel with the normal DSC practice NMR measurements were recorded as a function of increasing temperature albeit at much slower rates, although on occasion completed and repeated temperature cycles have been recorded. The temperature control systems have been those supplied with the instruments, the setting accuracy is typically 0.5K. The CPMG decays were generally recorded over a range of pulse spacings.

The samples can be divided into two categories dependent upon water content. Freezing of high

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water content samples resulted in a f7eeze concentration. For these studies the primary interest was in the properties of the residual, the non ice phase, and to reduce complexity a strategy of suppressing the pure ice signal was adopted. This was achieved by arranging that the signal repetition rate exceeded the expected spin lattice relaxation rates of ice. The signal decays were routinely fitted to up to four components using a Newton Raphson technique.

DECAY SHAPES As noted a phenomenological model requires a minimum of two components, representing the matrix former and the plasticiser. A preference for a simplicity of language implies the use of two components. However, whilst the fitting of the signal decays varied with temperature and composition, as a general statement significant deviations were obtained when two component fits were imposed. This occurred whatever the form of the decay functions, gaussian, exponential etc. The numerical detail of the fits was also dependent upon the data acquisition range. The conclusions are firstly that inappropriate functions may have been applied, secondly that the decay may be that of a continuous distribution and not of discrete functions attributable to specific components, and thirdly and possibly related that this may be a manifestation of compositional heterogeneity within the sample. The consistency of fit and the scatter of fitted parameters deteriorated as the number of fitted components was increased. Whilst the quality of the data and the scatter of fit is such that three components would be usable the preference for a simple language is such that a first order treatment involving two components has been applied. The CPCM decays are fitted to exponentials. The forms of the Free Induction Decays and Solid Echoes are less certain. At high molecular mobilities the FID is determined by the form of the inhomogeneity of the magnetic field over the sample. This might be generated by the applied field directly or by susceptibility variability over the sample. The form is difficult to specify but a gaussian function is considered a reasonable representation. At somewhat lower mobilities the decay time is directly relatable to the rate of molecular motion and an exponential decay is expected, although this would be modified by compositional heterogeneity. When rigid lattice conditions prevail the spectrum is effectively a measure of the distribution of local fields generated by neighbouring magnetic nuclei. The exact forms of the rigid lattice spectrum and of its Fourier Transform represented by the FID are not calculable(l), but the first order treatment is to use a gaussian representation. There is an instinct to invoke consistency and to represent the FID as a series of gaussian functions. In parallel with this the FID is also routinely fitted to a series of exponential functions. In the present studies below the glass transition the matrix former is normally in rigid lattice conditions whilst the plasticiser is more mobile(2) and the decay curve is fitted to a combination of a gaussian and a series of exponentials.

In practice whilst the numerical values of the derived parameters are changed the general patterns of the component amplitudes and decay rates as functions of composition and temperature are very similar whatever the functions selected. The differences in the derived amplitudes and decay rates are typically the order of a few per cent. The amplitudes are relatable to the concentrationsofthe constituents and are more readily cross checked against known composition and are thus more critical. However, the major conclusion must be that in terms of providing a general description of behaviour over most of the composition and temperature ranges the

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selection of decay h c t i o n is not critical. It would become important if NMR Relaxation was to be used to provide absolute determinations of composition to better than a few percent. The CPGM technique loses effectiveness as mobility is restricted and the CPGM decay rates become comparable to those of the inhomogeneity determined FID. In all cases examined the junction between the FID and CPGM amplitude and decay rate data has been a smooth one.

A BEAT IN THE FID At low temperatures below the glass transition the FID displays a beat. This is a common observation normally attributed to the onset of a local order in spatial but not necessarily orientational distribution. In the rigid lattice the molecules forming a glass may be oriented randomly but there will be a residual radial order between the magnetic nuclei, here 1H, within the molecule. When incorporating a beat an FID is often (3) represented by y(t) = Aexp(-(tlT)"2) *sync(pt) From the perspective of this project if a beat is ignored and the data is force fitted to a two component decay a signal component could be allocated to a "liquid" phase even when no such signal is present. The extension of the data analytical procedures to incorporate a sync fimction is reasonably straight forward but the data fits obtained are not good in the region of the beat and more particularly at short time. In essence the amplitudes derived may be in enor by 20 to 30%. The situation may be represented as follows. 60

, - FID at 200K

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Fit of Two Component Relaxation Decay, (Gaussian with a sync function and an exponential) to the FID of a 40% Maltose sample at 200K

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The dominant interaction determining the local field experienced by a 1H nucleus is that with the nearest neighbour. In a powder (or glass) this is a classic Pake pattern. This is subsequently broadened by interactions with more distant neighbours and the resultant spectrum is a convolute of the initial and the broadening function. In the absence of a more precise function this could be represented by a gaussian. The FT of this is a product of the individual Fourier Transforms of the primary and the broadening functions. The sync function is the Fourier transform of a rectangular function and not sUrprisingly is incorrect. The solution adopted is to substitute an FT for the Pake function in the place of the sync term. In practice the function derived for the FT of a Pake curve was complex and so an inelegant numerical solution has been adopted. A resulting fit is shown in figure 2. There are still some residual deviations but these are probable explicable by noting that the molecules constituting the “solid” phase contain different 1H sites with different near neighbour distances. The spectrum obtained is a superposition of individual spectra for the different sites. 60

- FID at 190K - - Fit to Gauss With Pake FT +

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Figure 2 .

Equivalent fit where the FT of a Pake Pattern has been substituted for the sync function

WATER RICH SAMPLES At temperatures above 273K all the 1H signals are observed. In the two component sugar water signals the CPGM decays are influenced by proton exchange between water and hydroxyl sites on the sugars. In a polymer/sugar/water system the polymer reflecting a decreased mobility usually generates a separate signal. On cooling a bulk ice phase is formed. The spin lattice relaxation times of the bulk ice signal are Iong, many hundreds of seconds, and acquisition conditions are such that these signals are repressed. There is a reduction of signal amplitude that could be used to monitor ice formation and hence to determine a phase diagram. The relaxation time of the “water’ signal decreases rapidly with decreasing temperature as a concentration

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occurs and the effect of interactions with the non aqueous components become more pronounced. The decrease in “water” decay time with decreasing temperature is echoed by a decrease in decay time of the “solid” signal. In summary the observed behaviour of the “water” signal in this region is consistent with expectations, and that of the ‘‘solid’’ is not unexpected, less free water is available to mobilise the sugar or polymer and sugar. Data acquired &om samples at different concentrations are consistent.

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The rapid decrease in “liquid” signal amplitude and decay time terminates at a temperature predictable fiom the phase diagram. A residual water signal remains, this is water so closely associated with the polymer or solute that it’s local organisation is incompatible with that of bulk ice(4,5). The amplitude of the “liquidplasticiser” signal generally exhibits a plateau over a temperature range. Over this region the decay time continues to decrease but at a lower rate consistent with a decreasing mobility. Accompanying this the “solid” decay time is in the region of the rigid lattice limit. It has been noted that the transition to rigid lattice conditions is coincident with the glass transition(6). This transition is very pronounced and easily recognisable, in all but very special circumstances the NMR transition does appear to be consistent with the glass transition. However, the naive model is that below the glass transition temperature the system is locked and that there is little mobility other than zero point vibrations etc. This view has subsequently been modified by observations similar to those reported here where the plasticiser phase has decay times longer than rigid lattice and suggestive of a continuing mobility. This continuing mobility is not restricted to the liquid phase. Examination of the “solid” signal decay rates reveals evidence of further changes in the solid mobility below the glass transition. This takes several forms including the development of a beat pattern in the FID, a reduction in

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the T2 value, and when solid state N M R techniques are employed changes in contact time, rotating frame relaxation and 13C spin lattice relaxation times. Sharp transitions are observed in the glass transition below Tg analogous to those recorded in DSC plots. 25

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Decay Times of Solid Component of a Maltose (10%) Pullulan (10%) 80% Water sample

The plateau region for the “liquid” amplitude does not continue indefinitely. At some temperature below Tg the amplitude begins to decrease. This is not a data fitting problem occurring because the “liquid” decay time is becoming comparable to that of the “solid”. Furthermore this is not a new observation, similar effects have been reported for water adsorbed on a range of polymers, eg agarose, keratin etc(7). The standard explanation is that in such circumstances the observed N M R relaxations of the water are consistent with the water molecule mobilities being characterised by a broad distribution of correlation times. At the higher temperatures in the plateau region exchange between the “sites” with different correlation times is rapid and a mean single component relaxation is observed. As the temperature is further reduced a ftaction of these will enter slow exchange conditions and no longer contribute to a “liquid” signal. It is common practice to relate the slow to fast exchange to a correlation frequency related to the onset of rigid lattice conditions. If this model were to be accepted the shape and temperature dependencies of the correlation frequency distribution of this water component could be determined by investigating the rotating frame and variable fkequency spin lattice relaxations as functions of temperature and composition. Equivalently the behaviour of the solid component could be probed by deploying many of the solid state techniques available. These could also be used to investigate the nature of the interactions between the matrix former and the plasticiser.

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LOW WATER CONTENT SAMPLES The system is simpler in that a freezing of water to bulk ice does not occur in low water content samples. Consequently the system is attractive in the respect that behaviour above the glass transition is not complicated by a dilution effect caused by the intervention of melted ice. At low temperatures below the glass transition the behaviour is very similar to that of the fieeze concentrated systems. Analogo~lythe amplitude of the “liquid” signal is expected to be lower than that of the water at low temperatures rising to the water content value as the fraction of the water in rapid exchange increases. At even higher temperatures exchange with hydroxyl goups on the polymer and/or sugar molecules can be expected to occur as can solution of sugar molecules. In some circumstances the plateau region can disappear and the liquid amplitude passes smoothly through the water content value. It has been noted (7) that the temperature at which this occurs is that of the glass transition. Insufficient numbers of systems have been examined to determine if this is more than coincidence. There is a curiosity that is worthy of note. In dry samples the glass transition temperature is high.

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Free Induction Decays of a PulluladMaltose (3/1) sample with a moisture content of 8.4 secs

When such samples are placed in open tubes water evaporation can occur, the effect is to elevate the Tg value, the elevation of the glass transition tracks the sample temperature rise. In such circumstances the decay time of the solid component does not serve as monitor of the glass transition but fortunately the decay of the other component can be substituted.

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REAL FOODS A selection of real foods display relaxation behaviour that has a form recognisably similar to that of the model systems discussed in this paper. The foods examined involve confectionery products, cereal grains, cooked and uncooked, raisins etc. The amount of independent evidence available is limited but what there is does suggest that the assignments made for the model systems have a general validity. 100ooO

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CONCLUSIONS it is clear that there are no serious technical obstacles to the application of N M R Relaxation techniques to any food product. Furthermore the observed decays are readily and robustly resolvable into components that in general are not critically model dependent. Transitions in relaxation properties are directly relatable to features on the phase diagram reflecting glass transition behaviour. The connection is sufficiently strong for NMR to be considered as a technique for the establishment of such phase diagrams. However, it cannot be considered as independently absolute, the concentrations of components are subject to a variation of a few per cent dependent upon the function used to represent the decay. In addition the stability and settability of current temperature controllers is limiting. However, in providing a measure of molecular scale dynamics N M R is providing an additional dimension, the evidence is that the relaxation parameters are comelatable with other features of interest, providing an insight into mechanisms coupled with a potential predictive capacity. This is where benefits are to be expected, the work reported in this paper demonstrate that N M R

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relaxation can even on modest low cost equipment provide robust hard data utilisable in such developments.

REFERENCES 1.

I.J. Lowe and R.E. Norberg. Phys. Rev., 1957,107,46.

2.

S. Ablett, A.H. Darke, M.J. Iuard and P.J. Lillford. In ‘The Glassy State in Foods’. J.M.V. Blanshard and P.J. Lillford (eds.), University of Nottingham Press, 1993, 189-206.

3.

A Abragam. ‘The Principles of Nuclear Magnetism’, Oxford University Press, 1960.

4.

W. Derbyshire. The dynamics of water in heterogeneous systems with emphasis on subzero temperatures, in ‘ Water a Comprehensive Treatise’, F. Franks (ed), Plenum Press, Vol. 7,339-430.

5.

I.D. Kuntz Jr. and W. Kauunann. Adv. Protein. Chem., 1974.28,239.

6.

M.T. Kalichevsky, E.M. Jaroskiewicz, S. Ablett, J.M.V. Blanshard and P.J. Liltford. Carbohydrate Polymers, 1992,18,77-88.

7.

H.A. Resing. Adv. Mol. Relax Proc., 1972,3, 199.

8.

I de Dries, D V Dusshoten and M. Hemminga, to be published in J. Phys. Chern., 1998.

ACKNOWLEDGEMENTS This work was undertaken as part of the FAIR CT 96-1085 Project “Enhancement of the Quality of Food and Related Systems by Control of Molecular Mobility”. The authors thank the European Commission for funding.

Thermodynamicsof Relaxation Phenomena in Freeze-dried Wheat Starch Gel S. Poliszko, D. M.Napierara, R. kezler and G. Hoffmann DEPARTMENT OF PHYS~CS,AGRI~~ULTLJRALUNIVERSITY OF POZNAN, WOJSKA POLSKIEGO 38/42,60-637 PdZNAN, POLAND

Abstract Local relaxation phenomena in freeze-dried wheat starch gel of the density 0.13 g/cm3, were studied by three independent relaxation methods: dynamic mechanicalthermal analysis (DMTA) at a frequency of about 0.1 Hz; dielectric-thermal analysis (DETA) at the electric field frequency of 2 kHz and 1H-NMR relaxation at the magnetic field frequency of 25 MHz. Temperature dependencies of the components of complex rigidity modulus, complex permittivity, and spin-lattice relaxation rate were measured in the range 100 - 380 K. A comparison of the data obtained by the methods DMTA, DETA and 1H-NMR was possible thanks to a special transformation procedure eliminating the effect of different frequencies of the measuring fields on localisation of the ranges of dispersion. It was shown that the main relaxation process revealed in the temperature range studied was related to the dynamics of hydroxymethylene groups in the starch. 'I'his process was found to be characterised by the same value of the energy of relaxation activation irrespective of the kind of field stimulatingthe relaxation process. 1 INTRODUCTION

Products of starch processing belon to the most common food products, their contribution in nutrients reaches over 80 % The products of starch processing occur in the two main states described as rubbery (e.g. bread crumb) and glassy (e.g. RTE's crackers, snacks). Quality of the glassy products is characterised by such sensory parameters as crispness and crunchiness related to the mechanical properties detennined by the structure of the amorphous phase Dynamic mechanical-thermal analysis (DMTA) as well as dielectric-thermal analysis (DETA) and NMR relaxation spectroscopy are believed to be the methods providing infomation on molecular dynamics and changes in structure of polymer system in glassy . Usually, the response of a system to an external electric, mechanical or magnetic perturbation is analysed on the basis of the non-equilibrium thermodynamics. By the effect of an external force field, the system is shifted to a nonequilibrium state which tends to reach a new equilibrium through the time-dependent relaxation processes. Earlier studies of dynamic mechanical and dielectric properties of starchI3 and other p o l y ~ a c c h a r i d e s ~have ~ ~ ~shown ~ that their relaxation spectra reveal common

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characteristics. A comparison of results obtained by different methods requires the use of a special procedure for elimination of the effect of different frequencies of measuring fields on localisation of dispersion regions. This paper reports results of thermodynamic analysis of the relaxation phenomena in the glassy state of starch performed on the basis of DMTA, DETA and 1H-NMR methods.

2 MATERIALS AND METHODS Freeze-dried wheat starch gel was obtained from solutions of Canadian wheat (Int. Grain Products) gelated during cooking for 1h under constant stining and supplementing water loss. The solutions of the density of 0.10 g/cm3 were used to fill cylindrical pipes stored in a dessicator for 24 h in the atmosphere of saturated water vapour at 298 K. After this time the cross-linked starch el was subjected to sublimation drying in a lyophilisator after freezing at 284 K (-25 C). The process of dehydration lasted for about 72 hours. As a result of 10% reduction of the sample volume the xerogel of the density of 0.13 g/cm3 was obtained. For DMTA and NMR measurements, the samples were in the form of rods, whereas for dielectric studies they were formed as disks. Measurements were made in the temperature range 100 -380 K in the nitrogen atmosphere.

0

2.1 DMTA measurements. DMTA measurements were performed in the free vibration system based on the inverted torsion pendulum described in previous workt4.The measuring set was equipped in an optical-electronic set for vibration periods and vibrating amplitudes reading. The frequencies of free vibrations and logarithmic decrements of damping were measured in the system with and without the sample. On the basis of these data two components of the complex rigidity modulus, real part (GI) and imaginary part (G2) of the studied material were calculated’2.The mean frequency of mechanical perturbation was 0.1 Hz. 2.2 DETA measurements. The construction of the DETA spectrometerused in the experiment was described previously5. The main part of the equipment is a capacity bridge and a measuring capacitor, placed in a variable temperature chamber with liquid nitrogen vapours as a working medium. The real part (E’) of the complex dielectric permittivity was measured as a ratio of the capacity of the measuring capacitor with a studied material between its plates to the capacity of the same capacitor with the dielectric replaced by vacuum. The imaginary part (E”) of permittivity was found as a function of a tangent of the angle of phase shift between the voltage and current in the circuit of the measuring capacitor with the sample. The measuring electric field frequency was 2 kHz.

2.3 1H-NMR spin-lattice relaxation measurements. The measurements of spin-lattice proton relaxation rate Rl in freeze-dried wheat starch gel were carried out on a pulse solid-state spectrometer working at 25 MHz using the sequence of pulses composed of a saturating series which nullifies the transversal component of magnetisation and a d2 pulse measuring the recovery of the magnetisation vector (method of progressive saturation). The system of spins subjected to the sequence of pulses at a proper repetition time is in the stationary state. A deviation of the signal intensity from the value at equilibrium is an exponential function of time with a time constant 1MI. All magnetisation recovery curves were monoexponential functions of

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time. The xerogel samples for measurements of the relaxation rate R1 were placed in tubes of 5 mm in diameter subjected to degassing and sealed under vacuum. The measurements were performed at the temperature stabilisationwithin k 1 K. 3 RESULTS AND DISSCUSION Temperature dependencies of the components of the complex rigidity modulus, complex pemittivi? and nuclear spin-lattice relaxation in the starch xerogel of the density of 0.13 g/cm are shown in Figures 1,2 and 3, respectively.

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1’ Figure 1. Temperature dependencies of the real part (GI)and imaginarypart (GZ)of the complex rigidity modulus of wheat starch xerogel of the density of 0.I3 g/cm3. Results of DMTA measurements reveal over 3-fold decrease in the rigidity modulus of the xerogel for temperatures increasing fiom 100 K to 300 K. The lowest value of the storage modulus GI equal to 35 MPa obtained at 300 K is lo5 higher than GI (350 Pa) reported for fully hydrated gel of the same concentration of starch”. This comparison suggests that the mechanical properties of the amorphous regions of starch are determined not by the density of spatial lattice which is the same in the fully hydrated and l l l y dehydrated state, but the rigidity of the fragments of macromolecules making this lattice. This effect is related to the saturation of intrachain hydrogen bonds in freezedried gel. The above-mentionedover 3-fold decrease in the rigidity modulus observed for temperature increasing h m 100 to 300 K indicates a gradual decrease in the energy of the intrachain barriers rigidifying the polymer chains of starch. These changes are accompanied by the relaxation transitions manifested as the maxima at 150, 210 and about 290 K on the G2(T) curve. Identification of the molecular mechanisms related to these relaxation transitions requires M e r study using the supplementing methods for analysis of the relaxation phenomena.

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According to the DETA results shown in Figure 2, in the range of very low temperatures the components of the complex permittivity tend to the limits d+ 1 and E”+ 0, characteristic of vacuum. This result proves that below 100 K the reorientations of all polar groups of starch (mainly involved in intrachain hydrogen bonds) are frozen. An increase of temperature above 100 K reveals a single process of dielectric relaxation characterised by dispersion of the real component E’ and a single maximum of the imaginary component E” at 240 K. A similar region of dispersion appears on the curve of the temperature dependence of spin-lattice relaxation rate, (Figure 3), obtained for the starch xerogel at 25 MHz field frequency. At this frequency the relaxation transition manifested at the inflection point of the dispersion curve is observed at about 320 K. In contrast to the functions of loss obtained by DMTA and DETA which in the first approximation reflect the temperature spectra of mechanical and dielectric relaxation, the temperature curve describing dispersion of the nuclear relaxation of protons reflects the spectrum of the correlation times characteristic of molecular dynamics of the system studied. The shape of this spectrum can be determined on the basis of the dispersion curve Rl(T) having applied appropriate transformation procedures. In the first stage, the temperature dependence of RI was transformed into the frequency dependence by using the earlier described procedure6 following from the theory of absolute reaction rates. The recovered profile of dispersion was fit to the equation proposed by Koenig’ for NMR dispersion profiles assuming the Cole-Cole2type of the correlation times distribution.The formula applied was:

The fitting procedure performed on a computer gave the following parameters of distribution: degree of dispersion A = 8.80 s-‘, inflection frequency a,= 0.56 MHz and steepness of the inflection p = 0.88. These parameters enable a determination of the spectrum of correlation times of the magnetic nuclear resonance from the following formula16:

where zc = 1 h c . A comparison of the results obtained by the methods DMTA, DETA and 111NMR relaxation requires the application of a special transformation procedure which would eliminate the influence of differences in the measuring fields frequencies used in these techniques, on the position of the dispersion regions. The relaxation and correlation times of local relaxation processes satisfy the relationship derived in the theory o f absolute reaction rates:

z = ( A / kT) exp(AF/RT)

(3)

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where AF is the free energy of activation of relaxation processes, h, k, R are - the Planck, Boltzmann and gas constant, respectively, T is temperature. Taking into account the fact that angular frequency o = llr , the free energy of activation of the relaxation process can be expressed by the formula:

AF

= - RT ln(Ao / kT)

(4)

This relationship implies that if the modulus of mechanical loss G2 or the coefficient of dielectric loss E” or the function of nuclear correlation @ are determined as a function of temperature or frequency, the values of G2, E” as well as 0 and AF are interrelated. This fact implies the possibility of drawing the reduced curves representing the spectra of mechanical, dielectric and magnetic relaxation as a function of free energy of activation, obtained by particular methods for investigation of relaxation processes. The courses obtained in this way for freeze-dried wheat starch gel of 0.13 g/cm3 density, normalised to unity at the maximum, are shown in Figure 4.

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AF [kT/mol] Figure 4. The normalised curves of mechanical loss (DMTA), dielectric loss (DETA) and 1H- NUR relaxation spectrum as afunction of @ee energy of activation of relaxation in fleeze-dried wheat starch gel of 0.13 g/cm3 density: R,(dF) = GdG2- (DMTA), R,(M) = E”/E”,, (DETA) and R,(dF) = qdF)/@- (IH-NMR)). The position of the most intense maximum of mechanical relaxation is consistent with the course of the spectra of dielectric and magnetic relaxations. As follows from the DETA data, the relaxation process observed characterised by the activation energy AF of the order of 40 Idlmol, is related to the reorientation of the polar groups of starch weakest bonded in the network of intrachain bonds. The NMR spectrum is similar in character

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and its main maximum appears in the same range of the activation energy. This suggests that the transition recorded by the DETA and 1H-NMR is related to the onset of molecular motion of the same functional groups. The 1H-NMR method is sensitive to the dynamics of groups rich in protons, which indicates that the relaxation transition analysed, of the activation energy of 40 kJ/mol, can be attributed to the polar hydroxymethylenegroups, which is in agreement with the data reported by Mikhailov'. Since the most intense maximum in the mechanical relaxation spectrum coincides with the maxima in the other two spectra, it can be assumed that the intrachain bonds of the hydroxymethylenegroups are responsible for the high rigidity of the polymer chains in very low temperatures, (Figure 1). Dissociation of these bonds with increasing temperature leads to increased flexibility of the starch chains. The latter can trigger further transitions (of the configuration - conformation types) manifested as relaxation peaks, and characterised by the activation energies of the order of 55 kJ/mol and 75 kJ/mol, in the DMTA curve in Figure 4. The transitions of this type cannot be detected by the DETA and NMR methods, and their identification requires further studies.

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4 CONCLUSIONS The thermodymanic approach to the relaxation phenomena in glassy state, which can be detected by different methods, seems effective in identification of molecular processes controlling these phenomena. The proposed method of the reduction of data obtained by different measuring techniques allowing their presentation as a function of the activation energy has revealed that the relaxation transitions recorded at 150,240 and 320 K by the DMTA, DETA and 1H-NMR methods, respectively, refer to the same process related to the molecular dynamics of hydroxymethylene groups in starch, characterised by the free energy of activation 40 kJ/mol. The subsequent relaxation transitions recorded by DMTA at 210 and 290 K do not have their correspondents in the courses of dielectric and magnetic relaxation. They presence has been explained as the result of the configuration - conformational processes taking place without disturbance to the intrachain hydrogen bonds of the other hydroxyl groups of starch. As the network of the intrachain hydrogen bonds has not been disturbed, the rigidity of l l l y dehydrated starch chains at 300 K is lo5 times higher than in the high elastic state in the condition of strach full hydration. The above data suggest that the glass-rubber transition in the amorphous regions in starch can be determined by dissociation of the intrachain hydrogen bonds in the presence of water molecules.

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References 1. J. M. V. Blanshard and P. J. Lillford, "The Glassy State in Foods", Nottingham Univ. Press, 1993. 2. K. S. Cole and R. H. Cole, J. Chem. Phys., 1941,9,341. 3. C. T Greenwood in J. M. V. Blanshad and V. R. Mitchell ed. "Polysaccharides in Food", Butterworth, 1979.

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4. P. Hedvig, ”Dielectric Spectroscopy of Polymers”, Academy of Kiado, Budapest, 1977. 5. G. Hoffmann and S. Poliszko, Foliu Forestuliu Polonicu, 1985, B(16), 5. 6. G. Hoffinann and S. Poliszko, J. Appl. Polym, Sci., 1996,59(2), 269. 7. S. H. Koenig in ”Water in Polymers”, E. Rowland (ed.). ACS. Symp. Ser., 1980, 127, 157. 8. G. P. Mikhailov, A. I. Artukov, V. A. Szeveiev, Vys. Soed., 1969, A 11, 553. 9. T. Morooka, Wood Research, 1987,74,45. 10. T. Murayama, ”Dynamic mechanical analysis of polymeric material”, Elsevier, Amsterdam, 1978. 1 1. M. Peleg ,Rheol. Actu, 1995,34(2), 215. 12. J. Perepetchko, ”Acoustic Methods of Investigating Polymers”, Mir. Publ. Moscov, 1975. 13. S. Poliszko, G. Hoffmann, R. Rezler, Acta Alim. Pol., 1991,4,351. 14. S. Poliszko, S. Jankowski, C. Lesiewicz, Acta Alim. Pol., 1980,6,201. 15. R. Rezler, PhD Thesis, Agricultural University of Poznah, 1998. 16. T. L.Tchelidze, A. J. Derevianko, 0. D. Kurilenko, ”Electric Spectroscopy of Heterogeneous System” ed. Kiev -Naukova Dumka, 1977.

Functional Constituents of Food

NMR of Food Biopolymers Peter S. Belton INSTITUTEOF FOOD RESEARCH, NORWICH RESEARCH PARK,COLNJZY, NORWICH NR4 7UA. UK

INTRODUCTION

Food biopolymers come in a great range of chemical and physical properties they are typically heterogeneous in chemical composition and in spatial distribution. There are also associated with a variety of other chemical species such as salt, sugars, water, oil, vinegar and a host of minor components. Foods such as a thickened salad dressing will contain all of these components as well as two or three different polymers. In biological systems, such as cell walls, there may be a number of plysaccharides with different chemical compositions and conformations. The similarities of these molecules can make discrimination between them on the basis of chemical shifts difficult. The task facing the spectroscopist is a daunting one. If useful results are to be obtained there must be collaboration with those using other methods to characterise the system. Very often the whole system cannot be usefully studied until the behaviour of the individual components is understood. Sometimes it is necessary to have recourse to model compounds where fundamental knowledge of the spectroscopy is lacking. This situation contrasts strongly with that in synthetic polymers where often only a single polymer is used together with a plasticiser. In addition synthetic pol mers are typically of much simpler chemical constitution than biopolymers in that dey consist of only one or two repeat units. This difference in complexity has limited the transfer of techniques, developed for synthetic polymers, to biopolymers. Nevertheless considerable advances have been made and traditional methods such as relaxation time measurements continue to be used to great effect. In order to examine the ways in which NMR can elucidate the behaviour of polymers it is useful to a "pseudo phase diagram" as shown in Figure 1. In this figure the various states that can exist in a polymeric system as temperature and water content are shown. It is a pseudo phase diagram because the states indicated are not equilibrium states and they cannot all be reached from one another. In addition not all polymers exist in all of the states indicated. The two thin lines on the diagram, labelled AB and CD, are the true equilibrium states for a two phase system. They represent part of the eutectic curve. This true equilibrium curve is rarely, if ever, Seen for many polymer systems. Most polymers at low hydration form glasses, typically the glass transition temperature decreases with increasing water content. The glassy state is characterised by very slow polymer reorientations and virtually no translahonal motion of the polymers, although there may still be motion in the smaller molecules present.

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SOL

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FIGURE 1 A pseudo phase diagram of a polymer system, The phase boundaries shown are illustrative, in reality not all types of behaviour are shown by all polymer systems and the ‘‘phases’’ are not true phases On heating or further hydration glasses transform to rubbery phases. Typically the rubbery is characterised by greater extensibility than the glassy phase and viscoelastic behaviour. In this state the molecules are much more mobile and translational motion as well as reorientation of polymers can take place. The precise boundaries between the rubbery state and the other states shown in the diagram are not well defined. However the rubbery state is usually one in which the biopolymer is in high concentration and which has tangibly solid like properties (dough is an example). Gels, in contrast, usually have a large excess of water or other small molecules present and may be difficult to hold, even though they exhibit elastic properties (consider for example a weak gelatine gel as in a jelly). In order to form a gel there must be continuous network of polymers whose interactions are strong enough to prevent viscous flow under gravity. Usually the network consists of regions of strong interaction, called junction zones, separated by regions of amorphous, mobile polymer. In the sol state the system is clearly liquid and its rheology is dominated by viscosity, although the viscosity is likely to be shear dependent due to the entanglement of polymers. However the entanglement must be of a sufficiently weak nature that they can be broken by gravitational forces alone. At high temperatures and low water contents a melt may be formed (for instance in an extruder) which is characterised by liquid like behaviour, but, because of high polymer concentration, has a highly entangled polymer network and typically viscoelastic behaviour.

THE MOLECULAR BASIS OF POLYMER BEHAVIOUR In order to think about the behaviour of polymers it is useful to consider some basic concepts in polymer theory. (There is an excellent review by Kimmich,

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Schur and Koepf in which the theory of polymer dynamics applied to N M R is reviewed'. The account given here closely follows that review.) The principal characteristic of polymers is that they have a highly extended chain of molecular subunits. In some proteins this chain is coiled very tightly into a globular bundle. Typically such proteins do not exhibit valuable functional properties unless the globules interact with each other to form concatenations. The useful functional properties of pol mers arise as a result of the extension of the polymers giving rise to some form o network by interactions that are extended across space. The covalent coupling between adjacent units on the polymer chain means that motions of neighbours must be correlated. Inevitably, however, as the correlation will be weaker for more distant neighbours and ultimately will die away for very distant nfighbours. This gives rise to the notion of a correlation length or Kuhn segment .

fy

FIGURE 2 Kuhn segments: the circles represent the molulmer units of the chain and the straight lines are the Kuhn segments. The Kuhn segment may be considered as the fundamental unit of the polymer chain since it is the distance over which local motions are correlated. Figure 2 shows the reduction of a molecular chain to Kuhn segments. For a polymer in dilute solution a simple scaling law applies which relates the radius of gyration of the polymer (R) to the segment length@)and the number of segments (N).

z is an exponent of the order 0.5. For constant N the radius of gyration increases

with b. That is as the correlation length gets greater the polymer becomes more extended. Figure 3 shows a plot of correlation length versus segment length. The increase in correlation length is a reflection of the increase in chain stiffness. In the limit that b is the whole length of the chain, correlation time for motion of the segment will be the same as the reorientation time for the whole chain, which for a large polymer will be large. In the other limit, in which the segment length is one monomer unit, the correlation time for the segment will be of the order of the time for one small molecule which will be much less than the reorientation time for the whole chain. In general the Kuhn segment length will be much less than the whole chain length and local motions will be on a short time scale. When the polymer system becomes highly concentrated there will be many polymer-polymer interactions. These may be purely of the van der Waals type but since the number of these will be very large they are of great importance. In order to deal with the problem of man2 interactions the tube, or reptation, model of polymers was proposed by de Gennes'. . In this concept the many interactions that the polymer undergoes are represented by the polymer being constrained to a tube by the interactions. This is illustrated in figure 4. The motions available to the polymer are local ones due to motions in the Kuhn segment and diffusion along the tube.

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FIGURE 3 A plot of the relative radius of gyration of a 500 unit polymer versus the number of monomers in a Kuhn segment.

FIGURE 4 Restraint of a sample polymer c h a r b y interactions with other polymers.

A cross section in the plane of the sample polymer is shown so that polymers whose

paths cross that of the sample are elliptical sections.

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As diffusion occurs the polymer leaves its existing tube and creates new tube, after a suitably long time all the old tube is unoccupied and a new tube is created. This generates a new correlation time, that for the lifetime of the tube. When the polymers can interact with each other by covalent interactions a new level of complexity is added; however in many of the systems of interest covalent links are widely separated and the tube model is still appropriate'. In gels polymer concentrations may be relatively low but there are always strong intexactions between chains, although of necessity these interactions are not distributed homogeneously throughout he chain. The simplest gel to consider is one in which there is random chemical crosslinking of single residues. In this model the junction zones may be taken as points with zero concentration. This, of course, does not accord with the reality of food gels but is useful for exploring the NMR consequences. Cohen-Addad3 has developed a scaling theory for swollen polymer gels the basis of which is shown in figure 5 . The immobilisation of the polymer chain at the crosslinking points A and B restricts the motion of the chain between these points.

Kuhn segments

FIGURE 5 cross linking of a polymer chain with the cross links a distance r apart. The chain is shown as consisting of Kuhn segments. Typically the chain will consist of a number of Kuhn segments.Not all possible orientations will be available to these segments and as a result there will be a small unaveraged static interaction remaining. The size of this interaction will depend on the stiffness of the chain and the distance between crosslinks. If the distance is zero there will total restriction of the chain and no averaging of the static interaction. In the limit of infinite distance between the cross link there will be no restrictions on chain motion and, provided the chain is sufficiently mobile, there will be no residual static interactions. In the normal, intermediate, case even in the limit of infinite chain mobility there will always be some residual interaction because of the restriction of the chain between the cross links link to a limited set of orientations.

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N M R TECI-INIQUES FOR POLYMERS Often food polymer matrices are complex mixtures of polymers (for example: cell walls) and the first, and non trivial, problem is to assign the spectra. The next problem is to associate particular components with particular dynamic properties. This is the general problem of understanding the functionality of biopolymers in the context of food. Figure 6 shows the range of time scales over which N M R may be used to measure dynamics.

Timescale(s) 10-’2 spinlattice relaxation laboratory frame

10-11 10-10

1o 1o-8 1o 1o-6

~ ~

10-5

1o

field cycu

1

spinlattice

frame

methods

lramrse relaxation

-~

10” 10-2

10’’ 100

FIGURE 6 The range of timescales accessible by NMR The fastest time scales may be addressed by measuring spin lattice relaxation in the laboratory frame, this is most responsive to motions at the Larmor frequency but is affected by a wide range of frequencies about this region. For most modern spectrometers the Larmor frequency is rarely below 20 MHz; however the increasing availability of field cycling spectrometry has made measurements down to the kilohertz range possible4. This represents a very useful development as spin lattice relaxation times Q may be generally expressed as:

T1=f (0,t)

(2)

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Where the symbol f represents is a function of, o is the frequency associated with the relaxation field and t is the correlation time. Single temperature and frequency relaxation time measurements are therefore underdetermined and it has therefore been practice to use temperature as a method of varying the correlation time. For foods this is a problem as it is in the nature of foods to have very temperature dependent chemical and physical properties. Thus at best only a very limited temperature range is possible. Field cycling spectroscopy overcomes this problem by varying o and keeping the temperature constant. Relaxation in the rotating frame allows access to lower frequencies if field cycling methods are not available. The relevant frequency is that of the spin locking field which is usually of the order of 70 to 40 kHz. It is important to recognise that if the spin locking field is reduced to a low value the effective field for relaxation is a combination of the spin locking field and the local dipolar field’. The local dipolar field is the lowest frequency field that the sample can experience. In the case of spins in which there is a partially averaged dipolar interaction this may be of the order of kilohertz or less. In principal, the measurement of the loss of local dipolar order by measuring the parameter TI, is a way of doing this6. So far there has been very little application of this method to biopolymers but it has been used for small molecules6*’ Measurement of transverse relaxation gives information about the lowest range of frequencies of motion. In general the relationship between lineshape or relaxation curve and motion is not simple and apparently motionally narrowed lines can contain signals in which residual static interactions are The use of magic angle spinning methods or multipulse sequences can be very useful for investigating these**’. Then use of exchange methods to investigate low frequency processes has greatly increased with the development of multidimensional NMR methods. The basis of these methods is shown in figure 7.

First pulse (goo)

Second pulse

1

1

Prepare

Sample pulse

Mix

Sample

FIGURE 7 The pulse sequencefor exchange NMR

The sequence consists of the creation of transverse relaxation by the first pulse, the second pulse returns spins to the Z direction where they are labelled by intensity. If there is no exchange of polarisation between the spins during the mixing period the third pulse recovers the original situation as it was after the first pulse. If there has been exchange of polarisation between the spins cross peaks will appear in the two dimensional spectrum. There are many variants of this basic scheme and the form of the experiment will determine the range of frequencies of motion that are sampled. The simplest experiment is the one dimensional experiment called the

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Goldman-Shen experiment". Very often polymers contain both immobile and mobile spins which can easily be distinguished on the basis of their transverse relaxation times, an example is given in figure 8.

I

0

20

40

Time FIGURE 8 The recovery of fast decaying magnetisation in the Goldman -Shen experiment, as the time between the Prst pulse and the second increases achange causes the reappearance of the fast decaying component If a pulse is applied after the relaxation of the fast component such that magnetisation is returned to the Z direction and there is then exchange between the fast and slow relaxing sites there will be recovery of the fast relaxing component. Provided proper compensation is made for the effects of spin lattice relaxation the rate of recovery of the intensity of the fast component will be a measure of the rate of exchange between the sites. The time scales available to these experiments are of the order of the spin lattice relaxation in the laboratory frame which can be of the order of a second for protons. In the case of nuclei such as "C the chemical shift anisotropy is a good marker for exchange on long time scales. If carbons have a correlation slower than about 100 microseconds their chemical shift tensor will not be averaged by motion and the carbon spectrum will be considerably broadened by the range of frequencies occupied by the chemical shift tensor. The frequency of each carbon atom will be determined by its orientation. If during the mixing time its

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orientation changes so will its frequency. It will therefore appear to have undergone an exchange with another carbon atom at another frequency. As a result of this application of the sequence shown in figure 7 followed by two dimensional Fourier transformation will result in the observation of cross peaks in the spectrum. As carbon relaxation times can be very long it is possible in principle to observe very slow exchange rates. APPLICATIONS OF NMR

Much of the activity using NMR in biopolymer systems has concentrated on the elucidation of chemical constitution using solution state methods or on characterising the behaviour of water in these systems. An interesting variant of observation of water is to use cross relaxation to observe the polymer spectrum by transfer of polarisation to the water". Generally the magnetic behaviour becomes more interesting as the systems start to become mobile; thus in glasses, one area of interest has been the observation of the glass to rubber transition by NMR. This is characterised by a shift from a temperature invariant transverse relaxation rate in the glassy state to a temperature dependent one in the rubbery Systematic studies of polymers in the rubbery state have been limited to gluten and some related protein system^^"^"^ recently the area of most activity in the low water regime has been cell walls; this aspect has been reviewed16and is discussed elsewhere in these proceedings. Gels are a particularly interesting case for study as inevitably they contain at least two environments but need only contain one type of polymer. Starch in gels, during retrogradation and in the granule has been studied by a number of workers amongst which the work of Gidley and his group" has been notable. In gels, as in other polymer systems, much attention has been paid to the water in the system. In gels and sols the problem of "non-freezing" water has been much discussed. It is usually attributed to "bound" water or to the effects of polymer on water structure. However an alternative explanation may lie in the fact that biopolymer systems are typically non- uilibrium. systems and the non-freezing effect may simply be a %. reflection of this Figure 9 is an extension of the phase line AB and CD shown in figure 1. They are the lines the phase diagram would follow if the system were in equilibrium. Sols and gels at room temperature are on the water rich (right hand side) of the diagram. As the system is cooled water precipitates out as ice. This continues as the temperature is lowered and follows the eutectic curve. At the eutectic, if equilibrium were preserved, polymer would precipitate out. This is kinetically inhibited in sols and inhibited by the gel structure in gels. As cooling continues therefore more ice is precipitated and the polymer concentration increases, this goes on until the activity of the water is the same as the vapour pressure of the water is the same as the ice. At this point no further freezing occurs as there is no energy gain on transfer from water to ice. The remaining water is therefore non-freezing water. Calculation shows that the vapour pressure required is reached when the water to biopolymer ratio is of the order of 0.2 to 0.6 gm of water per gm of polymer. The non-freezing effect is therefore simply a of the non-equilibrium behaviour of biopolymer systems. Acknowledgement

This work was funded by the Competitive Strategic Grant of the BBSRC and the EU FAIRCT96-1170 "EUROWHEAT" project.

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TEMPERATURE

LIQUID WATER + POLYMER IN SOLUTION

POLYMER + ICE POLYMER RICH

WATER RICH

FIGURE 9 i%e equilibriumphase diagramfor a polymer water mixture REFERENCES 1.

2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

12. 13. 14.

R. Kimmich, G. Schnur and M. Koepf Progress in NMR Spectroscopy, 1988 20, 385. M. Doi and S. Edwards, "The Theory of Polymer Dynamics", Oxford Science Publications, Oxford, 1986. J. P. Cohen-Addad Prog. NMR Spectrosc. 1993, 25, 1. H-W. Weber, R. Kimmich, M. Koepf, T. Ramikand R. Oeser, Prog. Colloid and Polymer Sci. 1992, 90, 104 Y. L. Wang, P. S. Belton and H. Tang, chem. Phys. Lett. 1997, 268, 387. P. S. Belton and Y. L. Wang, Molec. Phys. 1997, 90,119. E. R. Andrew, D. N. Bone, P. J. Bryant, E. M. Cashell, R. Gasper and Q. A. Meng, Pure Appl. Chem. 1982, 54, 585. P. S. Belton and A. M. Gil, J. Chem. SOC.Farad. Tram. 1993, 89, 4203. P. Callaghan and E. T. Samulski, Macromolecules 1998, 31, 3693. V. J. McBrierty and K. J. Packer, "Nuclear Magnetic Resonance in Solid Polymers", Cambridge University Press, Cambridge, 1993. J. Y. Wu, R. G. Bryant and T. M. Eads, J. Agric. Food Chem. 1992,40, 449. M. T. Kalichevsky, E. M. Jaroszkiewicz and J. M. V. Blanshard, Int.J.Biol.Macromo1. 1992, 14, 257. M. T. Kalichevsky, E. M. Jaroszkiewicz and J. M. V. Blanshard, Znt. J. Biol. Macromol. 1992, 14, 267 A. M. Gil, P. S. Belton, K. Masui, A. Naito, A. S. Tatham and H. Saito, Biopolymers, 1997, 41, 289.

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15. 16. 17. 18.

125

P. S. Belton, A. M. Gil, A. Grant, E. Alberti and A. S. Tatham, Spectrmhim. Acta, 1998,54A, 955. A. M. Gil, P. S. Belton and B. P. Hills, Ann Reps NMR Spectrosc, 1996, 32,

1. J. M. V. Blanshard, E. M. Jaroszkiewicz and M. J. Gidley, in "NMR Applications in Biopolymers" J. W. Finley, S. J. Schmidt and A. Seriani, eds, Plenum, New York,1990, p 155. P. S. Belton, Znt. J. Biol. Macromol. 1997, 21, 81.

Solid State 13CNMR Studies of Wheat High Molecular Weight Subunits A. M. Gil,’ E. Alberti,’ A. Nait8,2 K. Okuda,2 H. Sait8,2A. S. Tatham3 and S. Gilbert3

’ DEPARTMENT OF CHEMISTRY, UNIVERSITY OF AVEIRO, CAMPUS DE SANTIAGO, 3800 AVEIRO, PORTUGAL

* DEPARTMENT OF LIFE SCIENCES, HIMEJI INSTITUTE OF TECHNOLOGY, HARIMA SCIENCE GARDEN CITY, KAMIGORI, HYOGO, JAPAN 678-12 DEPARTMENT OF AGRICULTURAL SCIENCES, UNIVERSITY OF BRISTOL, INSTITUTE OF ARABLE CROPS RESEARCH, LONG ASHTON RESEARCH STATION, BRISTOL BS18 9AF, UK

1 INTRODUCTION The relationship between bread quality and gluten viscoelasticity has been known for many years and studies have shown that good dough performance depends both on the quantity and the quality of gluten.’ The origins of such functional properties, at the molecular level, are however not known thus hindering a complete quality control of bread and derived foodst* through the selection of the most favourable gluten characteristics. Gluten is a complex mixture of many protein fiactions with different molecular weights and structural properties.’ Among these fractions, the Wheat High Molecular Weight (HMW) subunits have been identified as particularly important in determining the viscoelastic properties of g~uten.~ HMW proteins have a main central chain with a repetitive primary structure, with high content of glutamine, glycine and proline, and the ability of establishing cross-links through cysteine sidechainslocated at the chain ends. In this work we report a I3C NMR study of the hydration of HMW proteins. The effect of hydration on the protein structure is of particular importance since it is in the hydrated state that the viscoelasticity arises. The changes observed in the I3C crosspolarisation and magic angle spinning (CP/MAS) and single pulse excitation (SPE) spectra upon sample hydration should give information about the resulting structure. In order to systematically investigate the role of 1) disulphide bonds, 2) irregular chain ends, 3) length of repetitive chain and 4) heterogeneity on the structure of the hydrated system, the samples presented in Figure 1 were studied. Comparison of 1Dx5 SS with 1Dx5, 1Dx5 with the 58 kDa peptide, and 58 kDa peptide with the 21 mer peptide (Figure 1) should give infoxmation, respectively, about the effects of disulphide bonds, chain ends and chain length. In addition, comparison of the whole HMW fiaction with the single 1Dx5 SS protein should give information about the effect of heterogeneity. 2 EXPERIMENTAL

All proteins and the 58kDa peptide were obtained by puritication procedures described elsewhere4*’.The 21 mer peptide was synthetically prepared.6The samples were dried to constant weight and hydrated to different extents by standing in a 100% RHE atmosphere. Hydration is expressed in g DzO/lOO g dry protein or, in some cases, in g

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H20/100 g dry protein. The 13CN M R spectra were recorded using a Bruker MSL400P spectrometer, using 90" pulse lengths of 4-5 ps, 4 seconds recycle times, 1 ms contact times and spinning rates of 5-7 kHz. 13CTI and 'H TI, measurements were carried out using, respectively,the Torchia sequence' and variable contact time experiments.

4OOH

NH2

1 cys

3 to 5 cys

Whole HMW fiaction comprising 67-88 kDa proteins and residual lipid (W.HMV) NHZ

4OOH ss ss ss

ss

1Dx5 subunit: single 88 kDa protein with SS bonds ( l k 5 SS)

NH2

4OOH

REPEATS

1Dx5 subunit with ablated Cys sidechains single 88 kDa protein, without SS bonds (IDx5)

I

[

REPEATS

I

58 kDa peptide: central 1Dx5 chain, no chain ends (58 ma)

1 PGQGQQGYYPTSPQQPGQGQQ:1Dx5 repeat unit (dlmer) Figure 1. Schematic representation of the samples studied.

3 RESULTS AND DISCUSSION Figure 2 shows that the 13CCP/MAS spectra of the dry samples are practically identical. The assignments indicated were achieved by comparison with small model pep tide^^"'^ and reflect mainly the most abundant amino acids: glutamine, glycine and proline. In the 0-35 ppm region, most of the amino acids sidechain resonances may be found, followed by the alpha resonances of glycine (42 ppm), glutamine (52 ppm) and proline (60 ppm). A shoulder at 48 ppm arises fiom the carbons at the delta position in the proline ring. Tyrosine residues resonate in the aromatic region of the spectra and the carbonyl region shows a broad peak at about 172 ppm which arises fiom the backbone carbonyls in the protein. A shoulder may be identified at 177 ppm with the carbnyl carbon in glutamine sidechains.

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3 W.HMW

Q6

Figure 2. I3C CP/uAS NMR spectra of dry protein and peptide samples. side bands. 3.1

*:

spinning

Hydration of 1Dx5 SS and 1Dx5: The Effect of Disulphide Bonds

3.1.1 13C NMR Spectra. The response to hydration of akylated (1Dx5) and nonakylated 1Dx5 protein (1Dx5 SS) may be observed in the 13CCP/MAS spectra shown in Figure 3. The spectra show that, for both proteins, the signal to noise ratio decreases as hydration increases. This reflects the increasing mobilisation of the system with increasing hydration, and consequent decrease in the amount of rigid protein. Although such change is observed for both proteins, the rate at which the signal to noise decreases was found to be different. By normalising the spectra in Figure 3 to spectral area per scan and per gram of protein, direct comparison of the spectral areas may be carried out. These calculations showed that, at 65% hydration, the 13C CP/MAS signal decreases down to 48% for 1Dx5 SS and to 60% for 1Dx5. This shows that protein mobilisation is somewhat easier when the chain ends are engaged in covalent disulphide bonds. In the carbonyl region of the 13CCP/MAS spectra shown in Figure 3, the 177 pPm shoulder becomes clearer at higher hydration. The 177 ppm peak observed in the 3C CP/MAS spectra has previously been identiiied with glutamine sidechains held motionally hindered in the hydrated Those groups may be engaged in hydrogen bonding with neighbouring glutamine residues. By deconvoluting the carbonyl region of

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the normalised spectra, the area ratio ( A r e a 1 7 7 for ~ ~ hydrated ~ pr0tein)/(Areal72~~~ for dry protein) may be calculated. If the area under the 172 ppm peak in the spectrum of the dry protein is assumed to correspond to all the backbone carbonyls in the sample, the above ratio may be taken as an estimate of % glutamines engaged in interchainhydrogen bonding. A value of about 9% was found, in this way, for both proteins indicating that, in approximately every ten amino acids, there is one hydrogen bonded glutamine. The independence of this value on the presence of disulphide bonds suggests that the hydrogen bonded glutamines may be preferentially located in the central chain of the protein.

Figure 3. I3C CP/uAS NMR spectra of a) 1Dx5 SSprotein and b) IDx5protein. *: spinning side banh. The carbonyl region inserts in Figure 3 show that the 177 ppm peak is also significantly intense in the SPE spectra of the hydrated samples, which indicates that some glutamine sidechains are highly mobile, probably located in the most mobilised chain segmentsor loops. In the aliphatic region of the I3C CPiMAS spectra, the Ga resonance at 42 ppm decreases significantly with hydration indicating that some glycine-rich chain segments are easily mobilised by hydration, relatively to the remaining carbons observed in the CPiMAS spectra. 3.1.2 Relaxation Times Measurements. The values of 13CTI of the dry samples (tens of seconds) were found to decrease of about ten fold, upon 65% hydration. This indicates, as expected, a raise in the number of l o 8 s motions as hydration increases. The proton TI, relaxation times measured for the dry proteins are determined by spin difhsion so that all peaks correspond to an average 5-6 ms value. At high hydration,

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,

distinct spin reservoirs are formed and TI values may be used to distinguish sites with different dynamics in the s timescale. Hydration to 65% causes a decrease of most TI values to 1.8-3.1 ms, reflecting an increase in the number of 10” s molecular motions. However, the value for the 177 ppm peak remained the longest (4.9 ms) together with that for the hydrophobic sidechains at 20 ppm (4.7 ms). These long values confirm the relative rigidity of the glutamine sidechains observed by CP/MAS and indicate the hinderance of some hydrophobic sidechains, probably engaged in hydrophobic interactions. In addition, the fact that the two peaks have similar TI, values suggests that the hydrogen-bonded glutamines and the hydrophobic bonded residues may be close in space. Slightly shorter TI values were observed for the hydrated 1Dx5 SS protein, compared to 1Dx5, which is consistent with the higher mobility already suggested for the protein containing disulphide bonds. Figure 4 shows a possible hydration model for the 1Dx5 SS and 1Dx5 proteins. Hydration may give rise to the formation of loops, held by interchainjunction zones, or trains. In the presence of disulphide bonds, the chain ends may only interact with each other, forming large loops filled with water. In the absence of disulphide bonds the chain ends may interact with other specific groups in the central chain. The resulting structure for 1Dx5 may thus involve a higher number of smaller loops, compared to 1Dx5 SS. This would explain the lower mobility observed in the absence of disulphide bonds. The loop regions formed upon hydration should contain a high number of glycines and glutamines, as suggested by the changes in the CP/MAS and SPE spectra, having therefore a strong hydrophilic character. The train regions should involve both glutamines and hydrophobic residues. The proposed model is consistent with the known primary structure of 1Dx5, as discussed below.

,

,

b)

a)

\--.

.-‘c-.”.

....

....... -SM

--bad -H@#ldiCM

Figure 4. Schematic representation of a possible hydration model for a) lDx5 SS and b) lDx5 proteins.

3.2 Hydration of 1Dx5 Protein and 58 kDa Peptide: The Effect of Chain Ends 3.2.1 13CNMR Spectra. In order to obtain information about the role of the irregular amino acid sequences at the chain ends, the spectra of 1Dx5 (Figure 3 b) and of the 58 kDa peptide (Figure 5 a) should be compared. The 13C CP/MAS signal of the corresponding normalised spectra decreases much quicker with hydration for the 58 kDa peptide. This indicates that the absence of the chain ends causes a more mobile network to be formed. The presence of the 177 ppm peak in all the CP/MAS spectra c0ntkn-i~ that hydrogen bonded glutamines remain present, even for the peptide. In the peptide spectra, the 172 ppm peak weaker relatively to the 177 ppm signal, due to the enhanced backbone mobility. An estimated value of 6% hydrogen bonded glutamines was obtained

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for the peptide at high hydration, showing a decreasing tendency for the number of hindered glutamine residues. 3.2.2 Relaxation Times Measurements. The low signal to noise registered for the spectra of hydrated 58 kDa peptide, particularly at 65% hydration, hindered reliable measurements of relaxation times. However, some values of proton TI, were obtained, at 35% hydration, for the 30 and 25 ppm peaks arising from glutamine and proline sidechains. Such values were, respectively, 1.4 and 2.5 ms. For the same peaks in the spectrum of 1Dx5,the values of TI, were 3.2 and 3.8 ms thus indicating that a system of enhanced mobility forms for the peptide, at intermediatehydration. Figure 6a shows a schematic representation of a possible model of hydration for the 58 kDa peptide. The absence of the irregular sequenced chain ends may cause the chains to move further fiom each other giving rise to a more mobile and looser network.

65% 40

35% DzO

32% 40

Ooh 40

0% 40

I . . . .

t

I

.

.

m

.

.

,

t

.

.

.

m

.

,

.

w

.

.,

WM

Figure 5. "C C P / M S NMR spectra of a) 58 kDa peptide and b) 21 merpeptide. *: spinning side bands.

b)

a)

x

Hydration

\

-\

233

Figure 6. Schematic representation of a possible hydration model for the a) 58 kDa and b) 21 mer peptides

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3.3 Hydration of the 58 kDa Peptide and 21 mer Peptide: The Effect of Chain Length 3.3.1 "C NMR Spectra, Figure 5b shows the set of spectra obtained for the 21 mer peptide. Above 32% hydration, no I3C CP/MAS signal was observed and an intense I3C SPE spectra was registered. This shows that the short peptide dissolves promptly at hydration higher than about 32%. Although a signiscant loss of 13C C P M S signal is also observed for the longer peptide, as mentioned above, dissolution does not occur. Interestingly, the C P M S spectrum of the 21 mer at 32% hydration still shows a strong peak at 177 ppm, as well as a strong peak at 20 ppm (Figure 5b). This suggests that, at 32% hydration, the short peptide still forms a network in which the most hindered groups are hydrogen bonded glutamine sidechains and hydrophobic sidechains. 3.3.2 Relaxation Times Measurements. 13C TI relaxation times were found to be longer for the short peptide in the dry state (23-39 s), compared to the dry 58 kDa peptide (12-32 s). This suggests that a closer molecular packing is established when shorter chains are involved. Hydration of the 21 mer peptide to 32% causes a very slight TI decrease (15-33 s) indicating that the packing formed in the dry state is broken up with signiscant dif6culty, before dissolution occurs. Proton TI relaxation times showed longer values for the 21 mer peptide in the absence of water (8.1-8.7 ms). These are significantly longer than the values of about 5 ms measured for the 58 kDa peptide, suggesting that a more rigid packing of the short chains is formed. TI values decrease with hydration but again, at 32% hydration, the values remain longer for the short peptide (3.5-5.2 ms) than for the 58 kDa peptide (1.43.4 ms), contirming the dficulty in breaking up the rigid 21 mer network before dissolution occurs. Figure 6b shows a schematic representation of a possible model of hydration for the 21 mer peptide.

3.4 Hydration of the Whole HMW Fraction and 1Dx5 SS protein: The Effect of Heterogeneity 3.4.1 "C NMR Spectra. The spectra shown in Figure 7 for the whole HMW fiaction should be compared with those obtained for the single 1Dx5 SS protein (Figure 3a). The same general changes are observed upon hydration, for both proteins. However, the 177 ppm peak in the spectra of W.HMW, arising fiom hydrogen bonded glutamine sidechains, is stronger relatively to the backbone 172 ppm peak. This observation results fiom the more marked relative decrease of the 172 ppm peak, which suggests enhanced mobility of the backbone. 3.4.2 Relaxation Times Measurements. The variation in relaxation times with hydration was similar for both proteins confirming the general shortening of carbon TI and proton TI values as hydration increases. The similarity of the relaxation values measured for the two proteins suggests that there are no signiscant changes of molecular motion densities at the 10" s and the lo" s timescales.

4 CONCLUSIONS The above results show that, for all the systems investigated, a network is formed upon hydration, even for short peptide chains, with only 21 monomers. In all cases, the

Functional Constituents of Food

I

.

.

.

.

1

l l

133

.

.

.

.

I

.

100 PPY

.

.

.

I

.

50

.

.

.

Figure 7. "C CP/uAS NMR spectra of whole H M .

I

*: spinning side bands.

network formed seems to be held together by junction zones (or train sections) involving hydrogen bonded glutamine sidechains, close to hydrophobic interactions established between the sidechains of hydrophobic residues. The network may comprise sections of higher mobility (or loops) which seem to involve hydrophylic glycine-rich and glutaminerich segments. The formation of the network described above is consistent with the primary structure of lDx5" part of which is represented below:

-

-

-

Q

~

~

r

n

G.rn.TSSQbQ--. T ~

The double underlined region of the above sequence may correspond to the parts of the chain which preferentially interact closely with the water, taking the form of loops. The dotted underlined regions contain a high number of hydrophobic residues intermingled with glutamines which may establish, respectively, hydrophobic bonding and hydrogen bonding between neighbouring chains. In addition, the I3CNMR results hereby presented helped to draw conclusions about the effects of some chain structural properties on the characteristics of the network formed upon hydration. The presence of disulphide bonds was shown to give rise to a more mobile network, probably involving larger loops. The irregular sequences found at the chain ends also play an important role when they are not covalently linked: their presence causes general motional hindrance in the network. This may reflect the formation of a higher number of smaller loops, due to the ability of the chains to move relatively to each other and adopt more stable arrangementsin which a higher number of

~

~

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Advances in Magnetic Resonance in Food Science

interchain bonds are established. In the absence of the irregular chain ends, long chain peptides are characterised by water insolubility and lead to the formation of a rather mobile network. Chain lengths as short as 21 monomers were also seen to form some sort of network a! intermediate hydration, before solubilising in water. The effect of having a mixture of proteins containing some residual lipid, compared to a single protein system, was not very significant, as viewed by I3C NMR.Some spectral features suggested, however, a slight enhancement in the mobility of the heterogeneous system. References

1. K.F. Finney and M.A. Barmore, Cereal Chem.,1948,25,291. 2. P.R. Shewry, A.S. Tatham, J. Forde, M. Kreis and B.J. Miflin, J. Cereal Sci., 1986, 4, 97. 3. P.I. Payne, K.G. Corfield and J.A. Blackman, Theor.Appl.Genet.,1979,55,153. 4. P.R. Shewry, J.M. Field, A.J. Faulks, S. Parmar, B.J. Miflin, M.D. Dietler, E.J. Lew and D.D. Kasarda, Biochim. Biophys. Actu, 1984,23,788. 5 . AS. Tatham and S. Gilbert, unpublished results. 6. A. NaitB, K. Okuda, A.M. Gil, S. Tuzi and H. SaitB, Proceed. Intern. Con$ Magn. Reson. And Other Spectrosc. Techn. Food Sci-, Himeji, Japan, September 1997. 7. D. Torchia,J. Magn. Reson., 1978,30,613. 8. A.M. Gil, E. Alberti, A.S. Tatham, P.S. Belton, E. Hurnpfer and M. Spraul, Mugn. Res. Chem., 1997,35, S101. 9. A.M. Gil, K. Masui, A. Nait6, AS. Tatham, P.S. Belton and H. SaitB, Biopolymers, 1997,41,289. 10. P.R. Shewry, M.J. Miles and AS. Tatham, Prog. Biophys. Molec. Biol., 1994, 61, 37. Acknowledgements

This work was partially funded by the EU FAIR project “Improved EU Wheats for Food” CT96-1170.

The Application of Electron Spin Resonance Spectroscopy to the Detection and Transfer of Free Radicals in Protein-Lipid Systems Nazlin K. Howell and Suhur Saeed SCHOOL OF BIOLOGICAL SCIENCES, UNIVERSITY OF SURREY, GUILDFORD, SURREY GU2 5XH. UK

1. INTRODUCTION

The presence of free radicals is common in foods and arises from processing, including heating and radiation as well as oxidation during storage. Free radicals are reported to damage proteins and DNA (1); this results in the unavailability of essential amino acids and induces cross-linking of proteins thereby affecting the nutritional and hctional properties such as texture. In addition, the new products generated may be toxic. Although most investigations have been undertaken in the clinical field, similar reactions occur in food systems; however, these have been studied to a limited extent hitherto. In this paper we summarise extensive studies undertaken by the authors on the production of free radicals in oxidised lipids including fish oil; the subsequent transfer of the radicals to amino acids and proteins and the resultant cross-linking. The efficacy of synthetic and natural antioxidants has also been examined with a view to reducing the damaging effect of lipid oxidation. Electron spin resonance (ESR) spectroscopy is the most direct method for detecting and measuring free radicals. The use of freeze-dried samples, as opposed to the use of spin-trapping techniques, allowed the radicals to be stabilised and localised, thus facilitating the identification of the atoms on which the radical resides. The g value provided enough information to distinguish between the carbon, nitrogen and sulphur centred radical. In addition, a number of other techniques including fluorescence spectroscopy, nuclear magnetic resonance (NMR) spectroscopy and gas chromatographymass spectroscopy (GC-MS) have been employed in our studies to provide a detailed picture of protein-lipid interactions. 1.1. Lipid oxidation mechanisms

Lipid oxidation is a major problem in food manufacture and storage causing rancidity and off-flavours as well as undesirable interaction with other food components. There are three main stages in the lipid oxidation process which can be outlined briefly as follows: 1.1.1. Initiation stage. In the initiation stage a hydrogen atom is removed from a methylene group in the unsaturated lipid, by a reactive group e.g. hydroxyl radical (OH') producing an unpaired electron on the carbon (lipid radical). The carbon radical is

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stabilised to form a conjugated diene which can interact with oxygen to form a peroxy radicalRO, .

RH

-+

R + H

1.1.2. Propagation stage. The peroxy radical can abstract another H from another lipid molecule which leads to an autocatalytic chain reaction by which lipid oxidation proceeds. The peroxy radicals can combine with the H which they abstract to form lipid hydroperoxides and cyclic peroxides.

R RO,

+ +

0 2

RH

-+

-+

R0; ROOH + R

Hydroperoxides are the primary oxidation products which readily degrade to secondary products including hydroxy-fatty acids, epoxides and scission products such as aldehydes (including malondialdehyde), ketones and lactones, many of which are toxic (1). The degradation of lipid hydroperoxides is initiated by the presence of transition metal ions e.g. iron and copper which cause fission of an 0-0bond to form an alkoxy radical RO' as well as peroxy radicals R02'. In the presence of thiols or other reducing agents such as ascorbic acid, O2 is reduced to superoxide anion (0, ), which dismutates to H202 or reduces Fe3' to Fe2+. The Fenton reaction between the Fe2+ and H202 results in the production of the hydroxy radical (OH') which can initiate further chain reactions. '

1.1.3. Termination stage. The free radicals produced can combine with each other or with the protein molecules to end the chain reaction. The latter reaction may cause cross-linking and severe damage to proteins.

R + R nR02' RO2'+ R

-

+ ---+

R-R (R02)n RO2R

Antioxidants can retard lipid oxidation in a number of ways including the binding of oxygen or free radicals; retardation of the initiation step, blocking of the propagation step or stabilisation of hydroperoxides. As a result, stable antioxidant-radicals are formed which are either too unreactive for further reactions or form non-radical products.

1.3. Lipid-protein interactions Interaction of lipids and lipid oxidation products may result in a loss of specific amino acids such as cysteine, lysine, histidine and methionine, as well as damage to other pigmented proteins such as cytochrome C and hemoglobin. These interactions have implications for many diseases including atherosclerosis and for food quality. The reaction of lipid oxidation products including free radicals with proteins (Pr) may result in cross-linking and polymerisation as shown below. H O + PrH Pr' + H20 Pr' + Pr' Pr -Pr Pr' + Pr - Pr Pr - Pr - P i andsoon

-

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An increase in fluorescence has been reported which is attributed to the formation of certain oxidised lipid-protein complexes; for example, fluorescent compounds have been isolated from the oxidation reaction of linoleate and myosin in frozen Coho salmon (5).

2. MATERIALS AND METHODS

2.1. Materials

Lipids: methyl linoleate and fish lipid extracted from freshly caught Atlantic mackerel (Scomber scombrus). Amino acids: arginine, lysine and histidine Proteins: lysozyme, ovalbumin and myosin (extracted from freshly caught Atlantic mackerel (Scomberscombrus) according to Saeed and Howell (2). Antioxidants: synthetic antioxidants butylated hydroxytoluene (BHT) and butylated hydroxyanisole (BHA) and natural antioxidants Vitamin C and E. 2.2. Methods 2.2.1. Preparation of oxidised lipids. Methyl linoleate and fish oil (lml each) were oxidised with oxygen gas at 37°C . The level of oxidation was monitored by traditional methods e.g. peroxide value (3) as well as an HPLC method developed by the authors, 13 C NMR and GC-MS (4).

2.2.2. ESR spectroscopy. An emulsion consisting of oxidised methyl linoleate (ML) or extracted oxidised fish lipid and either amino acids (arginine and lysine) mixed in the ratio 1:7; or proteins (lysozyme, ovalbumin and myosin) mixed in the ratio 1 5 , were prepared, quick-frozen and freeze-dried. Control systems of either amino acids or proteins in the absence of lipid were similarly prepared. The effect of antioxidants was also tested using BHT (200 ppm), Vitamin C (500 ppm), and Vitamin E (500 pprn). After freeze-drying, both experimental and control systems were oxidised in dry air at 37°C over CaS04. Samples (1 00-200 mg) were analysed periodically in a Jeol RE IX X-band ESR spectrometer with 100 kHz modulation. First derivative spectra were recorded everyday for the first week and once a week subsequently for five weeks. Manganese oxide was used as a reference marker to calculate the g value (2).

2.2.3. Protein cross-linking and polymerisation measurements by fluorescence spectroscopy. Organic solvent-soluble fluorescent products were extracted from freezedried samples of amino acids and proteins, incubated with oxidised lipids, and in the presence or absence of antioxidants, using a chloroform-methanol mixture at 45°C. The samples were centrifuged and the chloroform-rich layer was used for subsequent fluorescence measurements (2). Fluorescence spectra were obtained on a Perkin Elmer, model 3000 fluorescence spectrophotometer (emission maximum at 457 nm and excitation at 360 nm). The

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fluorescence intensity of 1 p g h l quinine sulphate in 0.1 M H2SO4 solution was used as a standard for measuring the relative fluorescence intensities of samples (2).

3. RESULTS AND DISCUSSION 3.1. Analysis of oxidised lipids The oxidation of methyl linoleate and fish oils indicated an increase in the peroxide value as well the production of hydroxides which are the breakdown products of hydroperoxides (Fig.1). The nature of the hydroxides has been confirmed by GC-MS and 13 C NMR spectroscopy (Fig.2) by Saeed and Howell (4). 13-HODA (transcis) 3.s >

a

1

E 2.88 E % 2.37 -

1

0

5

-

3.39

9-HODA (transcis)

1

N,

10

M

IS

2s

M

RetenUon nme (rninutesJ.

Figure 1. HPLC chromatogram of hydroxides 13-HODA and 9HODA (hydroxyoctadecadienoicacid) produced in oxidisedfish oil

160

81

113

0

6 (PPm)

13 Figure 2. C NMR spectra indicating the formation of hydroxides in oxidised methyl linoleate

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3.2. ESR spectroscopy Amino acids and proteins of increasing complexity, mixed with either a simple lipid methyl linoleate or fish oil, followed by oxidation in air produced similar results. A strong central signal was detected which was attributed to the carbon radical. The g values obtained ranged from 2.0021 -2.0049 for the amino acids and proteins (Figures 3-

2.00045

n

w i t h Bmixed Lysine I - I T andT incubated with ML

I

Lysine mixed with vitamin C and incubated with ML

yv I N

20G

M

n

O marker

Lysine incubated with ML

Figure 3. ESR spectra of lysine incubated with oxidised methyl linoleate and treated either with butylated hydrovtoluene (BHT,)or Vitamin C In addition a shoulder was detected for ovalbumin and myosin proteins with a g value of 2.017 which may be attributed to the radical associated with the sulphydryl group. Control samples or amino acids or proteins treated in a similar way indicated weak signals probably due to radicals formed in the of freeze-drying process.

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2.0045 g

Lysozyme mixed with BHT and incubated

vitamin C and incubated with fish oil

Figure 4. ESR spectra of lysozyme incubated with oxidised j s h oil and treated either with butylated hydroxytoluene (BNT) or Vitamin C

2.0023

incubated with fish oil

Figure 5. ESR spectra of ovalbumin incubated with oxidisedjsh oil and treated with Vitamin C

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2.0021

2.014

Myosin incubated with fish oil for 21 days

fish oil for 7

Figure 6. ESR spectra of mackerel myosin incubated with oxidised Jish oil for either 7 or 21 days

2.0021

MnO marker

Myosin mixed with BHT and incubated

with fish oil Figure 7. ESR spectra of mackerel myosin incubated with oxidised fish oil and treated WitWwithout butylated hydroxytoluene (BHT,)

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Myosin mixed with vitamin E

with fish Figure 8. ESR spectra of myosin incubated with oxidised fish oil and treated witWwithout Vitamin E It was interesting to note that the radical signal increased upto about seven days of incubation of amino acids or proteins with the oxidised lipid. After twenty one days the signal was considerably weaker indicating the disappearance of the radical due to interaction with other radicals or components. Antioxidants reduced the radical signals by 70-90% depending on the type; BHT was the most effective followed by Vitamin E and a combination of Vitamin E and C whereas Vitamin C and BHA on their own were not as effective. 3.3. Cross-linking using fluorescence spectroscopy 80

+Myosin+fish oil

70

+blank

myosin

+MyosinHishoil+BHT

.-

60

-f

50

--ft

M

Myosin+fish oil+vitC

+Myosin+fish oil+BHT+vE

0 C

40

2

0

1

2

3

4

Storage period (weeks)

Figure 9. Fluorescenceformation in myosin incubated with oxidisedfish oil and treated either with butylated hydroxytoluene (BHT) or Vitamin C (Vit C) or a combination of BHT and Vitamin C

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Interestingly, the disappearance of the radical signal after seven days coincided with the increase in the fluorescence intensity of the extracted samples due to cross-linking of amino acids and proteins either with themselves or with lipid oxidation products (Figure 9). In concurrence with the ESR spectroscopy results, BHT and Vitamin E reduced the production of fluorescent compounds whereas Vitamin C and BHA were less effctive.

Conclusions

We have clearly shown in this paper and reported elsewhere (Saeed and Howell, 1998) that the production of free radicals in oxidised lipids are transferred to amino acids and proteins. The reactions give rise to cross-linking of proteins which can result in protein damage and aggregation for example in fish muscle which can affect the texture, organoleptic and nutritional properties as well as the safety of food products. References

1. B. Halliwell and J.M.C. Gutteridge, ‘Free radicals in Biology and Medicine’, second edition. Clarendon Press, Oxford, 1995. 2. S. Saeed, S. Fawthrop and N.K. Howell, Electron spin resonance studies on free radical transfer in fish lipid-protein interactions. 1998. Submitted. 3. W. Jessup, R.T. Dean and J. M. Gebicki. Iodometric determination of hydroperoxides in lipids and proteins. Methods in Enzymology, 1994,233: 289 - 303;. 4. S. Saeed and N.K. Howell High performance liquid chromatography (HPLC) and NMR studies on oxidation products from extracted from Atlantic mackerel. Journal American Oil Chemists’ Society 1998b. Accepted. 5 . R. J. Braddock, and L. R. Dugan, Reaction of autoxidizing linoleate with Coho salmon myosin. Journal of the American Oil Chemists’ Society, 1973,50,343-346. Acknowledgements

The authors are very grateful to Dr. Susan Fawthrop, formerly of the Chemistry Department, University of Surrey for practical assistance with the ESR spectroscopy studies. We thank Dr. Duncan Gillies, Chemistry Department, University of Surrey and Professor Leslie Sutcliffe at the IFR Norwich for useful discussions. This research project was financed by The Commission of the European Communities within the STD Framework Contract No TS3*-CT94-0340 awarded to and co-ordinated by Dr. N.K.Howel1.

Editing the Information in Solid-state Carbon-13 NMR Spectra of Food Roger H. Newman NATURAL PRODUCTS PROCESSING, INDUSTRIAL RESEARCH LIMITED, PO BOX 31310, LOWER HUTT, NEW ZEALAND

1 INTRODUCTION

Solid foods can be studied by I3C NMR spectroscopy, but the signals are usually too broad to be clearly resolved. The results are more easily interpreted if the signals from individual chemical constituents or clusters of related components can be separated into subspectra. There are three distinct types of editing methods currently used for this purpose. Comparison of responses from single-pulse and cross-polarization excitation. Singlepulse excitation suppresses signals from relatively rigid molecules. Cross-polarization excitation suppresses signals from relatively mobile molecules. This approach is known as mobility-resolved spectroscopy. Comparison of responses from short and long cross-polarization contact times. Signals from solid-like states generally reach hll strength for contact times < 1 ms, but signals from less rigid states require contact times of several milliseconds.* Introduction of proton spin relaxation prior to the cross-polarization contact time. Small effects are accentuated by computing linear combinations of 13C N M R spectra obtained under different conditions. This approach is known as proton spin relaxation editing (PSRE). It was first used to simplify spectra of synthetic plastic^,^ then extended to studies of wood: plant cell wall^^-^ and starches.9910 Combinations of these three ideas can be used to cover all states of molecular rigidity from liquid-like to solid-like. The relatively solid-like constituents seem likely to have the greatest influence on the texture of food, and the PSRE method is most appropriate for use at this end of the scale of molecular rigidities, so this presentation is focused on the PSRE method. It is sometimes possible to study molecular rigidity by measuring the proton spin relaxation time constants Tl(H), Tl,,(H) or T2&) through proton NMR experiments. The disadvantage of this direct approach is that proton NMR chemical shifts are confined to a relatively narrow range. PSRE NMR combines the informative value of proton spin relaxation with the large chemical-shift dispersion characteristic of 13CNMR. One of the difficulties in implementing the PSRE method has been the subjective nature of criteria for separating signals into subspectra. I shall describe a more objective approach and show how it can be used to separate signals from the hnctional constituents of food.

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2 METHODS 2.1 Theory

In the simplest version of PSRE NMR the objective is to separate signals from two types of domains by exploiting differences in the values of proton spin-relaxation time constants TIM,Tl,,(H) or T2(H). Two experimental spectra are acquired. The normal cross-polarization pulse sequence is used to obtain a spectrum labelled 5: S = A+B

PI

A second spectrum S‘ is acquired with a pulse sequence that includes proton spin relaxation prior to the cross-polarization contact time, so that the component subspectra are partly suppressed:

S’ =faA+fbB PI Bold symbols are used to distinguish between a spectrum (S) and the component data points (Si, i = 1 to n}. If Tl,(H) is exploited, the suppression factors fa are likely to be exponential functions of the spin-locking pulse time t, e.g.: fa = exp[-t/Tl,,(A)]: [31 If T2(H) is exploited, the suppression factors are likely to be Gaussian functions of an interval t during which the proton transmitter output is gated OR”

~XP[-~~/{ZY~(A)~]~: 141 The subspectra A and B can be separated by generating combinations of the two experimental spectra! fa =

A =k S K S ’

mi

B = (1 4 ) s - K S ’ where:

[5b1

k =f b 4 . M a )

[6aI

K = 14fbYa) [6bI This version of the PSRE method is readily implemented if the suppression factors fa and f b can be calculated from known values of the relevant proton spin relaxation time constants, but that information is seldom available as prior knowledge. An alternative approach is to treat fa a n d h as adjustable parameters and “maximize mutual discrimination of signals without allowing any signal to become in~erted.”~These conditions can be expressed in mathematical terms. “Mutual discrimination” implies an approach towards orthogonality. If A and B are precisely orthogonal, then the dot product A.B is zero. Precise orthogonality is not always possible because both A and B might include coincident signals, so a more general condition for an acceptable solution is that A.B is minimized. “Without allowing any signal to become inverted might be translated to conditions Aj > 0 and Bj > 0. Those conditions would be acceptable in the absence of noise, but more realistic conditions allow for negative signals within reasonable limits, i.e., Ai > -E and Bi > -E where E is several times larger than the root-mean-square noise excursion 0. The PSRE method has been extended to systems with three distinguishable types of‘ domains. Subspectra A, B and C are formed from combinations of experimental spectra

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S, S’ and S”, and the 9 coefficients are calculated fiom 6 suppression factors. If all 6 suppression factors have to be treated as adjustable parameters, the optimization process is much more difficult than in the case discussed above. Electronic noise can become highly amplified in the edited subspectra, but it is possible to minimize the interference by carefil choice of proton spin-relaxation time intervals.l2 2.2 A worked example

The mathematical criteria have been incorporated in computer software which searches through trial values offa andh, uses Equations [5] and [6] to generate A and B, tests for inverted signals, computes the dot product A.B, and reports the “best” result. Figures 1 and 2 illustrate the PSRE method for a sample of spinach cut from a block of frozen leaves, thawed, washed and part-dried. The normal solid-state NMR spectrum (S) shows many overlapping signals. Some of those signals are relatively strongly suppressed in a spectrum S’ affected by T&) relaxation for a period of 4 ms between the proton preparation pulse and the beginning of data acquisition. It was possible to chose combinationsoffa a n d h that generated orthogonal spectra (A.B = 0) for E > 80, where the root-mean-square noise excursion 0 was evaluated across signal-free regions at the two ends of the spectrum. The solid line in Figure 2 traces all such combinationsfor E = 120. The broken l i e in Figure 2 traces combinationsoffs andh that minimised A.B for E < 80, while rejecting spectra with inverted signals. No solution could be found for E < 2.50, because noise excursions were identified as inverted signals. Points plotted for E = 60 and 30 show that the results are not particularly sensitive to the choice of E, provided that E is not too much larger than typical noise excursions. The solution for E = 30 isfa = 0.54 andh = 0.18, correspondingto: A = -0.50S+2.78S’

[5a1

B = 1.50s-2.78s’

r5b1 These linear combinations are shown in Figure 1. Signals in subspectrum A are assigned to cellulose (60 to 108 ppm) and crystalline waxes (33 ppm) with TI#) = 8 ms. Signals in subspectrum B are assigned to proteins (1 0 to 60 ppm, 173 ppm), polysaccharides (60 to 105 ppm) and noncrystalline-waxes (30 ppm) with TI,,@) = 3 ms. The objective PSRE method is clearly capable of achieving a good level of mutual discrimination, i.e., if a signal can be seen in one subspectrum there is little or no signal strength at the same chemical shift in the other subspectrum. 2.3 Reliability

The reliability was tested by mixing cellulose with non-cellulosic polysaccharides and trying to recover the subspectrum of cellulose from the spectrum of the mixture. Cellulose (Whatman CFI l), arabinogalactan (Aldrich 85,136-1) and pectin (Sigma P-9596) were vacuum-dried as powders, mixed in proportions 1:2:1 and moistened to 21% moisture to introduce sufficient molecular mobility for differentiation of proton spin relaxation time constants. The normal spectrum S is shown in Figure 3a. Differences in rotating-frame relaxation were exploited by acquiring a spectrum S’ affected by Tl,(H) relaxation for a period of 4 ms. The dot product A.B was minimized for E = 20 with fa = 0.79 a n d h = 0.29, corresponding to TI&) = 17 ms and T I @ ) = 3 ms. Crystalline constituents are expected to be associated with relatively slow rotating-

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1

1

1

1

1

200

1

1

1

150

1

1

'

1

1

'

1

1

100 Chemical shift (ppm)

1

'

1

50

1

1

1

1

0

Figure 1 Carbon-I3 NMR spectra of spinach leaves, thawedfrom afrozen block, washed and partdied to 47% moisture content: experimental spectra S and S' described in Section 2.2, PSRE subgectra A and B constructed as specified in Eq. 15J.

0'301 0.25

fb

0.20 -

0.45

0.50

0.55

0.60

0.65

fa

Figure 2 Combinationsof fa leaves.

d hwhich meet the criteriafor editing spectra of spinach

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frame relaxation, as in the example discussed in Section 2.2, so subspectrum A was selected (Figure 3b). Differences in spin-spin relaxation were exploited by acquiring a spectrum S“ affected by I;(H) relaxation for a period of 15 ps. The dot product A.B was minimized for E = 2 0 with& = 0.58 a n d h = 0.25, corresponding to 22(A) = 14 ws and T2(B) = 8 ps. No solution could be found for E < 20. Crystalline constituents are expected to be associated with relatively rapid spin-spin relaxation,” so subspectrum B was selected (Figure 3c) The edited subspectra (Figures 3b and c) are both similar to the spectrum of Whatman CF11 cellulose (Figure 3d), so the editing procedure was successful. Cellulose accounted for 25% (by weight) of the mixture and about 40% of total signal strength in the NMR spectrum. The discrepancy is attributed to molecular mobility in the non-cellulosic polymers, weakening the response from the cross-polarization pulse sequence. Separation of a spectrum into components provides evidence for heterogeneity, but failure to separate components does not necessarily mean that the sample is homogeneous. The proton spin relaxation parameters might be coincidentally similar. In this case the arabinogalactan and the pectin were contained in separate particles, so similarities in ZlP(H) and 73H) are coincidental.

A

C-2,3,5

d A 120

110

100

GalA C-2

90

80

70

60

50

Chemical shift (ppm) Figure 3 Carbon-I3 NMR spectra 08 (a) a mixtiire of arabinogalactan, pectin ma’ whatman CFii celldose, @,cj PSRE suh.spectra of the crystalline component separated by exploiting differences in Tl,(Hj and T2(Hj respectively, (4 W%afmunCFli cellulose alone.

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3. APPLICATIONS 3.1 Characterizationof cellulose

Plant cell walls are reinforced by cellulose. The chains are gathered in bundles or “crystallites” with molecular ordering described by two crystal structures, designated Ia and IP.13 The differences are most clearly seen in the C-4 NMR signals at 90.2 and 89.4 ppm (Ia) and 89.4 and 88.5 ppm (18),’3,’4 The C-4 NMR signals can also be used to characterize the cross-sectional dimensions of a typical bundle, because chains exposed on the surface of the bundle contribute signals at 84.9 and 84.0 ppm.’’ PSRE NMR can separate these signals from interfering signals, so that the nature of cellulose molecular ordering can be studied. Spinach (Figure 1) provides a challenging example, since cellulose is a relatively minor component and the signals show poor signal-to-noise ratios. Figure 4 shows more productive results for canned carrots, washed and part-dried. The normal spectrum S (Figure 4a) is dominated by signals assigned to cellulose. Spectrum S’ (Figure 4b) was obtained with T,,(H) relaxation for a period of 8 ms. The computer software indicatedf, = 0.61 and fb = 0.27 for E = 30, corresponding to TI,@) = 19 ms, TI#?) = 6 ms, and the following combinations:

@I

A = -0.79S2.94S’

B = 1.79s-2.94s’ Pbl The C-4 region of A (expanded in Figure 5) indicates a mixture of the two forms of cellulose, with more IP than Ia, and chains distributed between the interior (38%) and surfaces (62%) of bundles. A 5x5 array of chains would have a similar distribution, i.e., 9 chains (36%) in the interior and 16 (64%) exposed on surfaces. This model is consistent with cross-sectional dimensions of about 3 nm estimated by X-ray difEaction for cellulose crystallites in other samples of primary cell walls, e.g., in celery and Swiss chard.I6 PSRE experiments on other fruit and vegetables have indicated differences in the distribution of chains between the interiors and surfaces of crystallites (Table 1). The N M R results for strawberries are consistent with a model in which a typical crystallite contains just 14 chains. It has been suggested that the delicate nature of such a structure might contribute to the soft texture of strawberries.’ Table 1 Cellulose cryrtaIIite properties estimated by PSRE M R . Plant

% Interior chains

Reference

Carrot Spinach Apple Pineapple Onion Cabbage Strawberry

38 38 38 36 34 32 29

This work This work 6 17 17 17 8

Changes in cellulose crystallinity have been cited as possible reasons for deterioration of the texture of dehydrated vegetables during storage. 18-20 X-ray difiaction” and FTIRzO

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I

\ I

A

Figure 4 Carbon-I3 W R spectra of canned carrots, washed and partdried to 47% moisture content: experimental spectra S and S' described in Section 3.1, PSHE subspectra A and B constructed as spedfied in Eq. [8].

surface

94

92

90

88

86

84

82

80

Chemical shift (ppm) Figure 5 A portion of subspectrum A,from Fig 4, showing signals assigned to C-4 in cellulose cqstullites.

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studies have provided supporting evidence. It is possible that the changes involve noncellulosic polysaccharides associated with cellulose, rather than the cellulose itself. Is 3.2 Cellulose-polysaccharideinteractions

It is believed that xyloglucans are attached to the surfaces of cellulose crystallites, maintaining the ordered spacing of cell-wall components and perhaps controlling the porosity of the wall.2’ Nuclear spin exchange should ensure that values of proton spin relaxation parameters for cellulose and any attached polysaccharides are similar, so the polysaccharide signals should a pear in the same PSRE subspectrum as the cellulose. Whitney et al. have published C NMR chemical shifts for polysaccharides deposited on including a value of 99.5 ppm for xylosyl C-1 in xyloglucan.22 No such signal appears in subspectrum A for the canned carrots (Figure 4), but it is possible that xyloglucans became detached during cooking or storage. A different approach was taken to generate an illustration for this application of PSRE NMR. Microcrystalline cellulose enhances the gelling properties of galactomannan, and the composite can be used to lower the fat content of doughnuts and other foods while maintaining desirable textures.24 Figure 6 shows the results of a PSRE experiment on a composite sample prepared by mixing Avicel microcrystalline cellulose with a solution of a common galactomannan (locust bean gum) and keeping the mixture at 100 “C for 4 days. Spectrum S’ was obtained with TI,@) relaxation for a period of 10 ms. The computer software indicatedf, = 0.76 a n d h = 0.46 for E = 30, corresponding to = 36 rns, Y‘lP(B)= 13 ms and the following combinations:

2

A = -1.528+3.3 1S’

[9a1

B = 2.52s-3.31s’ The subspectra (Figure 6) are dominated by signals assigned to crystalline cellulose (A) and noncrystalline material possibly including cellulose (B). Weaker signals, assigned to the mannan backbone of the galactomannan, are seen more clearly in a plot expansion of the C-1 region (Figure 7), at 102.5 ppm in A and 101.3 ppm in B. The relatively slow relaxation associated with the peak at 102.5 ppm is consistent with a rigid structure, and the chemical-shift increment of 1.2 ppm from the peak at 101.3 ppm is consistent with a conformational change, i.e., straightening of the mannan backbone, required for compatibility with the surface of a cellulose crystallite.22 The value of TI,@) is considerably longer than a value of 2 ms measured for the peak at 101.3 ppm in a spectrum of a gel prepared without cellulose.25 This observation is consistent with assignment of the signal at 101.3 ppm to “bridges”, “loops” and “tails” extending from the crystallite surfaces, rather than an assignment to an unsupported gel. The “bridges”, “loops” and “tails” are all close enough to cellulose for partial averaging of the values of‘ TI,-@) through spin difision. The proportion of bound galactomman can be increased beyond that illustrated in Figure 7.25 3.3 Molecular rigidity

Iiker and Szczesniak reviewed the structural and chemical bases for texture in piant foodstuffs, and concluded that pectic compounds are key substances for the mechanical strength of the primary cell walls of h i t s and vegetables and for adhesion between cells.26 “Pectic compounds” include galacturonans and associated polymer chains of neutral

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c-1

1

120

"

"

"

110

"

"

"

"

100

'

80

90

50

60

70

Chemical shift (ppm) Figure 6 Carbon-I3 M R spectra of Avicel rnicrocystallzne cellulose treated with locust bean gum and part-a'ried to a moisture content of 50%: experimental spectra S and S' described in Section 3.2, PSRE subspectra A and B construced as spec$ed in Ey. (9).

I

L

110

I

108

1

I

I

106

104

102

I

I

100

98

Chemical shift (pprn) Figure 7 A portion of subspectrum APom Fig. 6, showing signals assigned to .gIi~coSyr C-1 of cellulose and mannoJyl C-1 of locust bean gum.

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sugars. Fenwick el al. studied Tl,,(H)relaxation in the pectic constituents of tomatoes and found two-component relaxation pro~esses,~’ so there are opportunities for using PSRE NMR in this area of research. Two complications must be addressed: the signals are broader than those observed for crystalline constituents, and separation of more than two subspectra requires an extension of PSRE theory. Proton decoupling can collapse dipolar interactions for liquid or solid samples, but there is an intermediate state of ri ‘dity for which decoupling is not as effective (Figure 8).” If the molecule is spherical ay3C NMR signal can be broadened to a linewidth of a few ~ H z ,but ~ ’ the anisotropic molecular motion of polymeric material blurs the distinction between liquid-lie and solid-like states and weakens the broadening observed for the intermediate state.29 The computer s o h a r e described in Section 2.2 was extended to incorporate the mathematical expressions required for separation of three subspectra.l 2 The requirement for “mutual discrimination” of signals was translated to minimization of the sum of dot products A.B+B.C+C.A.

-7

-6

-5

-3

-4

log10(.c/s) Figure 8 Theoreticallinewidths (at half mmcimum height)for I3C M R signalsfrom CH carbon.” The correlation time constant T represents a timescale .for molecular rotation of a spherical molecrrle. The decouplingfield strength is shown beside each curve. A sample of dried carrots was chosen to illustrate both signal broadening and separation of three subspectra. A bag of dried vegetables was purchased at a supermarket and fragments of carrots were selected. The moisture content (13%) was not altered for the NMR experiment. Figure 9 shows the normal spectrum S , a spectrum S‘ affected by T,,,(H) relaxation for a period of 4 ms, and a spectrum S” affected by T2(H) relaxation for a period of 15 p. This condition was satisfied by the following combinations (Figure 10):

A = -3.73S-2.59S’-7.35S’‘

[ 1oa1

B = -1.09S+0.33S’+4.17S”

[ 1Obl

C = - 1.63S-2.25S‘+3,l7Sf’

[ 1OCI

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"

120

"

:

:

110

l

l

l

l

90

100

l

l

t

l

70

80

l

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60

Chemical shift (ppm) Figure 9 Carbon-13 M R spectra of dried carrots acqriired.forYSRE NMR,

C- 1

GalA C-2

1

120

1

1

110

1

1

100

1

1

90

*

1

1

1

80 70 Chemical shift (ppm)

1

1

60

1

1

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50

Figure 10 Carbon-13 PSRE NMR subspeclra separated from the spectrum qf dried carrots. Carbon numbers in A and C refer to cellufosearid a-D-gficose,rqpectively.

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The signal-to-noise ratio in A is poor, but the signals are recognizable as those expected for cellulose. Signals in subspectrum B indicate the presence of galacturonic acid residues (C-1 at 100 ppm) and galactosyl residues (C-1 at 104 ppm). Signals in C are consistent with crystalline CY-D-gh~coSe.~~ The objective approach to PSRE was clearly successhl in achieving a good degree of separation of signals. Broadening in subspectrum A is attributed to unresolved contributions from the ICY and ID crystalline forms of cellulose, as described in Section 3.1. The large width of the signal at 100 ppm in B indicates that the galacturonic acid residues are in a state intermediate between solid-like and liquid-like. The sharp signals in subspectrum C indicate a crystalline state. Spin relaxation time constants (Table 2) are consistent with these conclusions. Values of T2(A) and T2(C) are characteristic of crystalline material," including the Whatman CFll cellulose discussed in Section 2.3. Theoretical values of TI,@) are longest for solid-like and liquid-like states, passing through a minimum for intermediate states involving molecular motion on timescales of the order of 1 P S . ~ ' The value of Tl,(B) is close to the value expected for an intermediate state,31and so short that most of the proton magnetization would have decayed during the 1 ms cross-polarization contact time.

Table 2 Proton spin r e l a t i o n time constantsfor dried carrots SubTectrum

A

B

C

T&-I) / ms Tz(H) f PS

4.4

1.o 12.7

38 8.2

8.7

The linewidths of edited signals have been helphl in constructing a ranking scheme for molecular rigidity in cabbage cell walls: cellulose > galacturonans > arabinans." Cellulose responded to cross-polarization excitation, indicating a solid-like state. Arabinans responded to single-pulse excitation only, indicating a liquid-like state. Galacturonic acid residues responded to both cross-polarization and single-pulse excitation, but the signals were broadened to linewidths >lo0 Hz. This was attributed to a state of intermediate rigidity. PSRE N M R was used to distinguish two categories of galacturonans, i.e., relatively rigid polymers with a low degree of methyl esterification and more mobile polymers with a higher degree of methyl esterification. PSRE Nh4R has also been used to distinguish two categories of galacturonan signals in I3C NMR spectra of strawberry cell walls.' Values of T 2 0 for the relatively highly methyl-esterified galacturonans became lengthened, and the response to cross-polarization weakened, as the h i t ripened.* These observations indicated a transition to a more liquidlike state of molecular mobility, helping to explain the softer texture of the ripe h i t .

4.CONCLUSIONS The objective approach to PSRE N M R was successll in distinguishing two or three categories of molecular rigidity, in all of the cases studied in this assessment. Cellulose and other crystalline constituents showed the best signal strength and sharpest peaks. Noncrystalline components showed signals that were weakened by inefficient crosspolarization processes. Values of TI@), T 2 0 , linewidths and signal strengths all provided information about molecular rigidity in the noncrystalline constituents. Published studies based on PSRE NMR have shown that the method can provide insights into the

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molecular origins of textural variations in fruit, vegetables, and cereal products. The new objective approach might be helpfd in extending applications to other foods. 5. EXPERMENTAL

Samples were packed in 7 mm diameter cylindrical silicon nitride or sapphire rotors and retained with end caps machined from Vespel or Kel-F. Poly(chlorotrifluoroethy1ene) grease (Halocarbon 25-58) was used to ensure a water-tight seal without contributing to I3C NMR spectra obtained with cross-polarization excitation. Samples were spun at about 4 kHz in a magic-angle spinning probe @oty Scientific Inc.) for N M R at 50.3 M H z in Varian XL-200 or Inova-200 spectrometers. Typical operating parameters were: proton preparation pulse 6 ps, cross-polarization contact time 1 ms, data acquisition time 30 ms, signal recovery delay between 1 and 2 s. The proton transmitter output was increased to yB~l(2n)> 60 kHz for proton spin decoupling during data acquisition. Experiments were left running for periods between several hours and 2 days in order to achieve the high signal-to-noise ratios required for PSRE NMR. Acknowledgement

The author thanks Dr J. A. Hemmingson for preparing the cellulose-galactomannan complex. References 1. T. J. Foster, S. Ablett, M. C. McCann and M. J. Gidley, Biopolymers, 1996,39, 51. 2. M.-A. Ha, B. W. Evans, M. C. Jarvis, D. C. Apperley and A. M. Kenwright, Carbohydr. Rex, 1996, 288, 15. 3. D. L. VanderHart and E. Perez, Macromolecules, 1986,19, 1902. 4. R. H. Newman and J. A. Hemmingson, Holzforschung, 1990,44,351. 5. C. M. Preston and R. H. Newman, Can. J. Soil Sci., 1992, 72, 13. 6. R. H. Newman, M.-A. Ha and L. D. Melton, J. Agric. FoodSci., 1994,42, 1402. 7. R. H. Newman, L. M. Davies and P. J. Harris, Plant Physiology, 1996,111,475. 8 . T. H. Koh, L. D. Melton and R. H. Newman, Can. J. Botany, 1997,75, 1957. 9. K. R. Morgan, R. H. Furneaux and R. A. Stanley, Curbohydr.Res., 1992,235, 15. 10. K. R. Morgan, R. H. Furneaux and N. G. Larsen, Carbohydr. Res., 1995,276,387. 11. T. T. P. Cheung and B. C. Gerstein, J. Appl. Phys., 1981,52, 5517. 12. R. H. Newman and L. M. Condron, Solid State M R , 1995, 4, 259. 13. R. H. Atalla and D. L. VanderHart, Science, 1984,223,283. 14. R. H. Newman and J. A. Hemmingson, Cellulose, 1995, 2, 95. 15. R. H. Newman, Holzforschung, 1998, 52, 157. 16. J. F. Revol, A. Dietrich and D. A. I. Goring, Can. J. Chem., 1987,65, 1724. 17. B. G. Smith, P. J. Harris, L. D. Melton and R.H. Newman, Plant Cell Physiol., 1998 (in press). 18. C. Sterling and F. Shimazu, J. Food. Sci., 1961, 28,479. 19. C. A. Willis and A. A. Teixeira, J. FoodSci., 1988, 53, 111. 20. E. Garcia, T. M. C. C. Filisetti, J. E. M. Udaeta and F. M. Lajolo, J. Agric. Food Chem., 1998,46,2110. 21. M. C. McCann, B. Wells and K. Roberts, J. Cell Sci., 1990, 96, 323.

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22. S. E. C. Whitney, J. E. Brigham, A. H. Darke, J. S. G. Reid and M. J. Gidley, Plant J., 1995, 8, 491. 23. S. E. C. Whitney, J. E. Brigham, A. H. Darke, J. S . G. Reid and M. J. Gidley, Carbohydr.Hex, 1998,307, 299. 24. J. F. Ang and W. B. Miller, CerealFoods World, 1991, 36, 558. 25. R. H. Newman and J. A. Hemmingson, Carbohydr.Polym., 1998, in press. 26. R. Ilker and A. S. Szczesniak, . I Texture Studies, 1990, 21, 1. 27. K. M. Fenwick, M. C. Jarvis, D. C. Apperley, G. B. Seymour and C. R. Bird, Phytochem., 1996,42,301. 28. W. P. Rothwell and J. S. Waugh, J. Chem. Phys., 1981, 74,2721. 29. D. L. VanderHart, W. L. Earl and A. N. Garroway, J. M a p . Heson., 1981,44,361. 30. W. L. Earl and F. W. Parrish, Carbohydr.Res., 1983,115,23. 3 1. R. K. Harris, ‘Nuclear Magnetic Resonance Spectroscopy’, Pitman, London, 1983, p 87. 32. B. G. Smith, P. J. Harris, L. D. Melton and R. H. Newman, Physiologia Plantarum, 1998, in press.

Cross-polarisation Kinetics and the Determination of Proton Mobility in Hydrated Plant Cell Walls M. C. Jarvis, M. A. Ha and R. J. Vietor CHEMISTRY DEPARTMENT, GLASGOW UNIVERSITY, GLASGOW G12 SQQ, SCOTLAND, UK

1. INTRODUCTION Plant cell walls are complex nanostructures which, in their natural hydrated form, have remarkable properties of strength, resilience and controlled flexibility'. Their mechanical properties, when they are under tension from the internal turgor pressure of the cell, define the textural properties of fresh fruit and salad crops. When distended by starch swelling pressure instead of turgor the cell walls control the textural quality of cooked and processed starch-containing vegetables like potatoes, peas and beans. In cereal products their influence on texture is less central but they modulate the availability and movement of water during processing. The constituent polymers of hydrated cell walls exhibit an extraordinary range of physical properties, despite the small scale of their internal structuring (nanometers or tens of nanometers)'. Cellulose microfibrils are solids of moderate crystallinity, although less than 10 nm in diameter. In contrast the p( 1,4')-linked D-galactan and a(1,5')-linked Larabinan side-chains of pectin behave like tethered liquids with respect to their chain mobility. These properties are reflected in 'H T2 values of ca. 10 p for cellulose and up to 1 ms for the pectic galactan?. A wide range of motional and relaxation behaviour is represented in other polymers between these two extremes. It follows that NMR relaxation experiments on hydrated plant cell walls are making a valuable contribution to our understanding of their influence on food quality. We are only now beginning to see how the essential viscoelastic properties of the cell wall emerge from the properties of rigid and flexible polymer chains, but NMR methods have been responsible for much of the progress that has been made. Such methods probably are the key to future developments in this area. In a typical cross-polarisation, magic-angle spinning (CP-MAS) NMR experiment it is not only the 'H and I3C relaxation processes that are sensitive to thermal motion. The CP process itself is capable of being disrupted by motional effects, and it is well known that

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between the ranges of polymer mobility accessible in CP-MAS and conventional solutionstate 13C NMR experiments their lies a region in which polymers are 'invisible' because they cross-polarise too slowly to give a detectable 13C signal before the 'H magnetisation is lost by the T I , process3. This paper deals with the use of CP kinetics as an alternative probe of molecular mobility, and specifically with the potential of slow-CP measurements as a way to explore the mobility of hydrated polymers in the 'invisible' range of mobility.

2. THEORY OF HARTMA"-HAHN CROSS-POLARISATION TheoreticaI descriptions of Hartmann-Hahn CP are available in the l i t e r a t ~ r e ~Here - ~ . we are concerned only with the kinetics of the process from a practical, descriptive viewpoint. A typical time-course for Hartmann-Hahn CP in a dry polysaccharide material is shown in Figure 1. Polarisation in 13C builds up in two phases before decaying through the Tlp process in the proton spin reservoir with which it is, by that time, in equilibrium. The initial CP phase is a flip-flop process between the I3C nucleus and the proton(s) with which it is most closely associated - normally by covalent bonding, although this is not essential and only spatial proximity and a heteronuclear dipolar interaction are required. The second, slower CP phase involves spin diffusion from more distant protons.

0

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,

I

1

2

I

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T,ms

Figure 1 Evolution, with increasing Hartmann-Hahn contact time, of I3C signal intensity at 105 ppm (cellulose C - I )from dry citrus cell walls

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For static single crystals the kinetics of the initial phase can be described by a damped oscillating function':

S = Sd2 (1 - exp(-3/2Rz) cos b2/2)..........................................................

(1)

Where So is the theoretical maximum signal intensity, b is the dipolar coupling between the I3C nucleus and the covalently bonded proton, and R is the rate of spin diffusion within the surrounding shell of nonbonded protons. Hediger' has re-examined this relationship and found that an exponent of (-3Rz) instead of (-3/2R.t) gave better fits to the experimental data. In the case of powder samples, integrating over all orientations allows (1) to be approximated by an exponential function', which with the addition of terms for the second, slower CP phase and for proton spin-lattice relaxation in the rotating frame gives:

where TCHRand TCHDare time constants related inversely to R and b respectively". For a I3C nucleus with a single covalently bonded proton s = 0.5 since, in the approximation of an isolated two-spin system, the equilibrium polarisation is shared equally by the 'H and 13C nuclei. Thus the fast and slow components of cross-polarisation are predicted to be equal in magnitude. For CH2 and CH3 groups the directly bonded protons can, in principle, make a larger contribution, i.e. s < 0.5. Figure 1 shows that the progress of CP can be fitted by a biexponential curve as predicted by equation (2). Substituting experimentally estimated, rather than fitted, values for TCHR gives CP rates that are not as close to the observed values but are of the correct order of magnitude (data not shown). It should be emphasised that equation (2) refers to the static case. The effect of MAS, with a centreband Hartmann-Hahn matching condition, is to reduce the efficiency of the initial, rapid CP phase which depends on the C-H dipolar interaction. Under high-speed MAS conditions the contribution of this phase is expected' to diminish to zero i.e. s + 1, but at MAS rates of <5 kHz the behaviour of dry samples (Figure 1) is close to the predictions for the static case, with s- 0.55. The rate of each phase of the CP process is, therefore, predicted to be affected by the number of protons bonded to each I3C nucleus and by the local rate of proton spin diffusion, a function of molecular mobility. Figure 2 shows that, in the absence of added water, CP is slower for nonprotonated I3C nuclei and faster for I3CH2 groups. It would be predicted that for methyl groups CP would be faster still, but Figure 2 shows that,this was not the case: CP was slower for "CH3 than for "CH or 13CH2groups. This effect may be ascribed to methyl group rotations which are known from proton relaxation experiments to exist in these materials'. This shows that CP kinetics can be used, at least in some circumstances, as a probe of molecular mobility.

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Figure 2. CP-MAS I3C spectrum of dry onion cell walls and (below) spectral variation in the fraction of the maximum signal intensity attained within the initial CP phase. 3. CROSS-POLARISATION IN HYDRATED SYSTEMS In plant cell walls, the range of molecular mobility that can be ascribed to different polymers is much wider in the hydrated than in the dry state". That is, cellulose is affected only a little by hydration whereas some of the pectic polymers approach liquidlike motional characteristics in the presence of water. It would therefore be expected that in hydrated cell walls, CP would proceed more slowly in these more mobile pol ysaccharides than in cellulose. Figures 3 and 4, comparing CP kinetics in dry and hydrated onion cell walls, show that in the presence of water the CP process did proceed more slowly. The efficiency of the initial phase - that is, the proportion of the total polarisation transfer accomplished during that phase - was reduced in the hydrated cell walls. Likewise the principal effect of methyl group rotation in both dry and hydrated cell walls was to reduce the efficiency of the initial phase. The rate of the slower second phase was also reduced by hydration, as Equation ( 2 ) does not suggest a mechanism predicted from the effect of mobility on TCHR. for the decrease in the efficiency of the initial phase (increase in s), but it is possible that the approximation to static conditions is less satisfactory in the more mobile, hydrated samples.

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1.2

I

--c105ppmWe

1

-t-89 ppm Wet

-+- 84 ppm Wet

0.8 0.6

--t 62 ppm Wet

0.4

-a- 89 ppm Dry -0- 04

ppm Dry

u 62 ppm Dry 0

0.2

0.4

0.6

1

0.8

contact time, rns

Figure 3. Cross-polarisation kinetics in dry and hydrated onion cell walls: cellulose and galactan signals. Assignments: 105 pprn, cellulose/galactan C-1 (CH); 89 pprn, crystalinterior cellulose C-4 (CH); 84 ppm, crystal-surface cellulose C-4 (CH); 62 pprn, crystalsurface cellulose C-6 (CH,).

1.2

,

I

-171

pprn Wet

1

-m-

0.8

-69

pprn Wet

-54

pprn Wet

101 pprn Wet

0.6

u 171 pprn Dry

0.4

u l O l pprn Dry

0.2

+69

pprn Dry

-0-54

pprn Dly

0 0

0.2

0.4

0.6

0.8

1

contact time, rns

Figure 4. Cross-polarisation kinetics in dry and hydrated onion cell walls: pectic galacturonan signals. Assignments: 171 pprn, C-6 (COOH); 101 pprn, C-1 (CH); 69 pprn, C-3/C-5 (CH); 54 pprn, methoxyl (CHj). Figures 3 and 4 show some degree of discrimination between rigid and more mobile polymers of hydrated cell walls, with respect to their CP kinetics. For example hydration brought about a substantial reduction in the efficiency of the intial CP phase for the signal at 62 ppm, which contains a major contribution from the methylene C-6 of highly mobile pectic galactans. Variation between polymers in the rate of the second CP phase was relatively limited, and the amount of motional information on these polymers that can be

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derived from this experiment appears to be less than can be obtained by a straightforward measurement of the proton T I ,or by other relaxation experimentsZz1I.

4 SLOW CROSS-POLARIS ATION OF HIGHLY MOBILE POLYSACCHARIDES CP spectra determined under normal conditions with spin rates of 3-4 kHz and contact times of 0.5 - 1.0 ms, do not represent all of the hydrated cell wall. In those materials" and in artificial polysaccharide gels3, the most mobile components are invisible under these conditions. This is because the contact time is shorter than the timescale for CP in these components and there is not enough time for polarisation to be transferred by the slow spin-diffusion process. Their proton T I , is comparable with, or shorter than, the timescale of CP and proton magnetisation is dissipated before it can be transferred to 13C. The spectral contributions of these mobile components can be recovered, in part, by extending the contact time". This brings two problems, however. First, if their proton Tlp is comparable with, or shorter than, the timescale of CP then proton magnetisation will be dissipated before it can be transferred to 13C. Secondly, many types of pectic polymer chain exhibit a range of mobility even within a single cell wall. Thus the spectral contribution of the most mobile components, even if it can be revealed at long contact times, will overlie signals derived from less mobile chains of the same chemical type, which are capable of CP even at normal contact times. These problems can be alleviated by using a delayed-contact experiment to correct a longcontact experiment for proton T I , decayI3. The pulse sequences for this combination of experiments are shown in Figure 5.

A. 'H

1-1

r"-*i

l3c

B. 'H

1-1

Figure 5. Pulse sequences for obtaining a difference spectrum (A-B) of highly mobile hydrated polymers by a combination of long-contact and delayed-contact CP. The difference spectrum (A-B) corresponds to mobile material cross-polarising during the longer Hartmann-Hahn contact in A. The extent of proton T I , decay is the same in both A and B. The uppermost spectrum in Figure 6 is a difference spectrum determined in this way for highly mobile pectic components of hydrated onion cell walls.

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0

9P

20ps 2ms 180170160150140130120110100 90 80 70 60 50 40 30 20 10

0

PPm

Figure 6 . Difference spectra of mobile components of hydrated onion cell walls, derived by comparing long-contact with delayed-contact spectra. The three lower spectra were obtained with a variable delay of 9 p - 2 m s insertedfor 'H T2 decay. Spectra from this type of material can, alternatively, be obtained by single-pulse (directexcitation) I3C experiments using MAS and either high-power or multiple-pulse proton decoupling, and if necessary the rigid components may be edited out by restricting the recycle time so that resonances with longer 13C TI values remain saturated14. Spectra very similar to Figure 6 have indeed been obtained from onion cell walls by this type of approach14, although because the polymers concerned show a range of mobilities it is not certain that the motional level sampled is exactly the same. These two approaches are complementary. For any attempt at quantitation the SP-MAS experiment is superior, because the spectral intensity observed in the long-contact experiment is very sensitive to the balance between CP and proton T I , kinetics. Indeed to observe a difference spectrum at all by this approach, from at least some samples, it may be necessary to have polymer components on the liquid side of the proton Tlpminimum so that this balance is more favourable. Considerable line-narrowing is frequently found in the difference spectra, consistent with such a level of motion. If the most mobile constituents present are close to the proton T I , minimum they may not be observed, and may in any case show excessive linewidth as suggested by Newman (this volume). The long-contact difference spectrum has an advantage over SP-MAS experiments, however. The latter obviously cannot be used to measure proton mobility, and this can be done effectively by combining a long-contact/delayed-contact experiment with an additional, variable, proton T2 delay16 inserted immediately after the proton 90" pulse in the pulse sequences shown in Figure 5. An example of such an experiment is shown in the lower spectra in Figure 6, where the mobile material in the difference spectrum is shown

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to have a proton T2 of somewhat less than 1 ms. This type of experiment is of particular interest because the motional range that it can probe overlaps with that at which proton NMR imaging is possible. The potential for making quantitative links between imaging experiments and high-resolution I3C spectra has intriguing possibilities not only for food science, in which polymers with this level of mobility are widespread and important, but also for biology in general.

5. EXPERIMENTAL Cell walls were isolated as described elsewhere'. NMR experiments were carried out on a Varian VXR-300 spectrometer operating at 75.34 MHz for "C. MAS rates varied between 3.5 and 4.3 kHz. For hydrated samples the proton spin-lock field was set at maximum power (60kHz) and the 13C spin-lock field was adjusted to optimise the Hartmann-Hahn match for each sample individually. The initial proton 90' pulse was then re-optimised. These precautions were necessary to allow for variation in radiofrequency absorption by water protons. From the intensity of the I3C field necessary to optimise the HartmannHahn condition it appeared that the proton field strength reaching the cell-wall polysaccharides was less than that applied in the case of hydrated, but not dry, samples. The same proton spin-lock field strength was maintained during decoupling. Spectra from polymers of high mobility were obtained from an experiment in which the duration z of the Hartmann-Hahn contact was varied from 25 ps to 12 msI3. The proton spin-spin relaxation time T2 was measured through the I3C spectrum by inserting a delay of 0-30 ps between the proton preparation pulse and the Hartmann-Hahn ~ o n t a c t ' ~Signal . assignments were based on published data2v10-12 Acknowledgements. We thank EPSRC for spectrometer time, BBSRC for financial support and Dr D.C. Apperley for executing most of the experiments reported here. We are grateful to Dr S. Hediger, Prof. R. Harris and Dr A. Kenwright for useful advice. References 1. N.C. Carpita and D.M. Gibeaut, Plant J. 1993,3, 1. 2. M.A. Ha, D.C. Apperley and M.C. Jarvis, Plant Physiol., 1997,115,593. 3. M.C. Jarvis, K.M. Fenwick and D.C. Apperley, Carbohydr. Res., 1996,288, 1. 4. S.R. Hartmann and E.L. Hahn, Phys. Rev., 1962, 128,2042. 5. B.H. Meier, Adv. Magn. Opt. Reson., 1994, 18, 1. 6. D. Marks and S. Vega, J. Magn. Reson. A., 1996, 1 18, 157. 7. L. Muller, A. Kumar, T. Baumann and R.R. Ernst, Phys. Rev. Letts. 1974 32, 1402. 8. S. Hediger, Doctoral thesis, Eidgenossische Technische Hochschule Zurich, 1997. 9. X. Wu, S. Zhang and X. Wu, Phys. Rev. B., 1988,37,9827. 10. M.C. Jarvis, K.M. Fenwick and D.C. Apperley, Carbohydr. Res., 1996,288, 1. 11. R.H. Newman, L.M. Davies, and P.J. Harris, Plant Physiol., 1996,111,475. 12. M.A. Ha, B.W. Evans, M.C. Jarvis, D.C. Apperley and A.M. Kenwright, Carbohydr. Res., 1996,288, 15. 13. T.J. Foster, S. Ablett, M.C. McCann and M.J. Gidley, Biopolymers, 1996, 39, 5 1. 14. P. Tekely and M.R. Vignon, J. Polym. Sci. Part C: Polym. Lett., 1987,25,257.

Proton Relaxation in Plant Cell Walls and Model Systems Huiru Tang* and Peter S. Belton INSTITUTE OF FOOD RESEARCH, NORWICH RESEARCH PARK, COLNEY LANE, NORWICH NR4 7UA, UK

1. INTRODUCTION Plant cell walls are of vital importance to activity of cells by providing the rigidity to keep cells intact and the mobility to enable cells to communicate and reproduce’. They are also one of the most important components in many foods. For instance, dietary fibres2, which are believed to have a number of beneficial functions as food, are primarily cell wall materials. Texture of foods is probably also dependent on the state of cell wall materials”! However, many aspects of cell walls remain poorly understood, for example, molecular structure and dynamics, and their relationships to the mechanical, biochemical and biological properties of cell walls. The complexity of the assembly of cell ~alls’.~.’and the structure of each component have been some of the major difficulties in attempts to understand the behaviour of cell walls at the molecular level. It is well known that the chemical composition of cell walls varies from source to source. Nevertheless, they are mostly composed of cellulose, pectin, hemicellulose, proteins and polyphenols’. Cellulose is an unbranched p-1,Cglucan present mostly in the form of crystalline microfibrils. Pectin generally consists of a group of polysaccharides rich in galacturonic acid, rhamnose, arabinose and galactose. These polysaccharides are present in the form of galactan, galacturonan, rhamnogalacturonan, arabinogalactan and arabinan’. Hemicellulose is often used as a convenient term for xylan, glucomannans and xyloglucans and is thought to interact with cellulose strongly through a network of hydrogen bonding’. Current models of cell wall assembly propose that cellulose microfibrils form the framework which hemicellulose and pectin hold together by formation of a matrix’. The microfibrils are responsible for strength whereas the matrix components are responsible for the charge, porosity and hydrophobiclhydrophilic characters of cell walls’. Although these models reflect some of the most important aspects of our understanding of cell walls and can explain a number of observations adequately, they represent, to large extent, static models since they have been deduced largely from fractionation of plant cell wall materials and microscopic investigations. Therefore, the current models do not carry indications about the molecular mobility, which is one of the most influential factors for the mechanical properties of cell walls. ‘H NMR relaxation measurements were first carried out about fifty years ago” to probe the molecular dynamics and structural characteristics. The first ‘H NMR relaxation

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work was reported” on plant cell walls in 1982 and a number of reports”-’’ have been published ever since. The earlier work were mainly concentrated on validate the method and using relaxation data to tentatively assign the chemical nature of cell wall components. More recently, NMR relaxation studies have indicated that the effects of hydration are most noticeable on the low frequency motionsI6 which have been found to be closely related to the mechanical properties on artificial polymers1’. Reports on proton relaxation data were also measured via I3C CPMAS NMR”-”. Although such measurements possess the distinct advantage of high resolution in I3C spectra, interpretation of the data obtained in such a manner remains a challenging task owing to the complication of spin diffusion. Nevertheless, I3C MAS NMR remains advantageous in revealing the chemical structure of the wall components and their properties under different conditions. Recent I3C MAS studies showed” that, even when dry, cell walls of both potatoes and Chinese water chestnuts had considerable signal intensities in the so called single-pulse-excitation magic angle spinning (SPEMAS) spectra from galacturonan and galactan, indicating the mobility of some of the pectin components. It has been also revealed that hydration has led to substantial mobilisation of both galacturonan and galactan in the pectin”. However, the hydration effect appeared to be conformation dependent; galacturonan in the 3, helix configuration is less sensitive to water plasticisation”. In contrast, cellulose was hardly affected by hydration. These observations were consistent with non-freezing water measurements using ’H NMR16 in potato cell walls, a-cellulose and pectin which was found about 0.2, 0.04 and 0.18 g per gram dry matter respectively. Therefore preferential hydration for pectin to cellulose was strongly inferred. In this paper, the emphasis is on the detailed molecular motions in cell walls and the important parameters affecting them. We use a systematic approach to probe the effects of temperature in order to understand the behaviour of the important functional groups in cell wall biopolymers. We also use galacturonic acid and its methyl ester as structural model systems for pectic materials to build an understanding of the detailed mechanisms. 2. PROTON RELAXATION IN GALACTURONIC ACID AND ITS METHYL ESTER As the monomeric species of polygalacturonic acid and methylated ester derivatives, D-agalacturonic acid monohydrate (GA) and its derivative methyl-a-D-galacturonic acid methyl ester monohydrate (MGAM) are good models for studying details of molecular motions. Comparative studies on both protonated and deuterium exchanged samples are useful in distinguishing the motional contributions of exchangeable protons from the nonexchangeable protons. Previous work on amino acid~”-~‘ and their derivatives25.26 enables one to predict that the methyl groups undergo three-fold rotating motions at relatively low temperature whilst hydroxyl groups and water molecules re-orientates at high temperature. This can be readily probed by measuring spin-lattice relaxation times in the laboratory (TI) and rotating frames (TIP)as well as second moments (M2J as a function of temperature. Analysis of the results yields relaxation parameters, such as activation energy, relaxation constants and activation energy (see eqn. 1-2).

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1

0.8

*?

0.6

k-

0.4

*

0.2

0 100

300

200

400

T/K Figure 1. Spin-lattice relaxation time in laboratory fYame, Ti, as a function of temperature for galacturonic acid. Solid circles: protonated sample; empty circles: D20 exchanged sample; solid line: fitted result for protonated GA. 2.1. Spin-lattice Relaxation in Laboratory Frame

The spin lattice relaxation process2’ of GA and MGAM in both protonated and D,O exchanged forms can be approximated satisfactorily to a single exponential decay function. The relaxation time, TI, for protonated GA fell between 1 and 25 s whereas for D,O exchanged one the values were 30-100 s (Fig. 1). This shows that D,O exchange leads to a substantial slowdown of the relaxation. These values for MGAM were 0.3-50 s. D,O exchange increased the T, values to some extent in the high temperature region but did not result in substantial changes in the low temperature region (Fig. 2). Therefore, in both cases D20 exchangeable protons are inferred as relaxation contributors in the high temperature regions (>300 K) but not so much in the low temperature regions (450 K). For protonated GA, the reciprocal of spin-lattice relaxation time, TI-’,plotted against temperature (Figure 1) showed that TI decreases as temperature rises, especially in the high temperature region. The D,O exchanged sample, however, did not show a similar trend, it changed little throughout the whole range of temperature. This implies that there is an effective relaxation process attributable to exchangeable protons which are in hydroxyl groups and water of crystallization. Assuming exponential correlation functions and that the different hydroxyl groups present have similar dynamic behavior, the experimental data for the protonated sample were fitted to the two-component form of the well known BPP theory“ or Kubo-Tomita expression”: 47 a ‘b 4rb T[’=C,[ ~ + ” o , ~ T+ l, +~4 0 , 2 ~ , 21 + C b [ l + o , 2 t+b12 + 4 ~ 2, 1~ ~ (1) ~

where o, is the proton Larmor frequency and z the rotational correlation time of the motion responsible for spin lattice relaxation, which can be written as:

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Table 1 Fitted relaxation parameters for protonated D-a-galacturonic acid from spinlattice relaxation in the laboratory and rotatingframes27

TI

TIP

Relaxing groups

H20

H20

OHs

Calculated values C (10') rad .s-2

3.8(27')

3.8 (27')

5.4(38")

30.16f 1.72

20.46f 0.23 20.19f 9.86b 2.44f 0.30 1.36f 9.46b 3.64f 0.03 6.56f 1.19b 1.3f 0.3'

25.92f 0.55 28.10f 12.67b 30.4f 6.37 7.28f 39.2Sb 5.83f 0.09 7.93f 1.9Ib 1.52 0.4b

Experimental values E, (kJ/mol) 7, (x

lo-" s)

0.0144+ 0.0091

C (10') rad .s-2

4.79f 0.43

AM2 (G')

TI-'at maximum (d)

1.32'

287'

(RIP,",) 230

Temp. at maximum (K)

420'

472'

38s

awhen undergoing isotropic motion; bvalues obtained from curve-fitting spin-spin relaxation data; CValues predicted from curve-fitting spin lattice relaxation data.

z =

'to

exp(-)

Ea RT

where z, is the pre-exponential factor corresponding to the rotational correlation time at infinite temperature, E, the activation energy and R the gas constant. C is the relaxation constant which can be expressed22-24*29-30 as in eqn 3, assuming that the motion occurring is fast on the NMR relaxation timescale:

where y is the proton magnetogyric ratio, A Planck's constant, n, the number of protons involved in the motion, N, the total number of protons to be relaxed and rjkthe interproton distances between protonj and proton k. TI data of GA cannot be satisfactorily approximated into a single-process model but can be by a two-process However, the values estimated for the second set of parameters from the two-process-model fit showed large uncertainties (data not shown). The results of the curve-fitting are summarized in Table 1. The relaxation constant C can be estimated using the second part of the eqn. 3. Although a crystal structure for GA is not available it is reasonable to assume that the inter-proton distances in CH's and OH'S or between them in GA are similar to those in calcium sodium a-D-galacturonate he~ahydrate~l-~~. Assuming the interproton distance in water of crystallization is similar to that in methyl galacturonic acid methyl ester'4 (1.414 A), calculated values of C for

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intramolecular dipolar interactions for a water molecule undergoing isotropic motion and 1SOoJ2ippingmotion are 2 . 6 7 ~ 1 0and ~ 3 . 7 6 ~ 1 0rad/s2 ~ respectively. Although there are a number of possible motions for the water of crystallisation the good agreement between the fitted C value and calculated one once again implies that the high temperature motion is related to a flipping motion of water molecule. The variation of spin lattice relaxation rate with temperature*' for MGAM is shown in Fig. 2. Both protonated and D,O exchanged samples showed a very sharp decrease of TI-' with increase of temperature at low temperatures. However, a high temperature rise can be clearly seen for protonated MGAM but not for D,O exchanged one (see insert in Fig. 2a). It is therefore apparent that there are at least two relaxation peaks, one at T<90 K and, one at P 3 7 0 K, neither peak is fully observed under the experimental conditions used here. The likely cause of the low temperature maximum is rotation of methyl groups as in the case of fully methylated pectin3', at high temperature the relaxation is due to motions of exchangeable protons in water of hydration and hydroxyl groups. As in the case of GA, the experimental data were fitted to the Kubo-Tomita expression (eqn.1) to evaluate the relaxation processes quantitatively. The results of the curve-fitting are summarized in Table 2. Since the high temperature process, corresponding to the motion of exchangeable protons, is only described by a limited number of data points, relaxation parameters (Ea, C, T,J obtained for this process have large uncertainties.

Figure 2. Spin-lattice relaxation time in laboratory kame, Ti, as a function of temperature for methyl galacturonic acid methyl ester. Solid circles: protonated sample; empty circles: 0 2 0 exchanged sample; solid line: fitted result for protonated MGAM; dashed line: fitted result for 0 2 0 exchanged sample. (a) Fitting fiee fiom constrains; (b) Fitting constrained with calculated C values

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Table 2 Fitted relaxation parameters for methyl-a-D-galacturonic acid methyl esters jiom spin-lattice relaxation in the laboratory and rotatingfiames27

Relaxing groups

MGAM CH,O-

OH + H,O

MGAM(D) CH3O-

3.16

0.34(2.42”)

4.57

6.43f0.29 (5.51f0.17)” 1.93f0.68 (1.15f0.24)” 1.30f0.13 3.16 7.2’

34.00f0.77b

7.3850.35 (5.23f0.0.20)” 0.37k0.1 (1.5 1f0.37)” 1.49k0.27 4.57 10.5’

~~

Calculated values C (107rad *s-, Experimental values E, (kJ/mol) ( x i 0 4 s)

C (1 09)rad .s-’ Rlmax (s-’)

4.60k1.51b 0.250+0.004b (RlpmJ 120b

75” (R,pmJ280b 74“ Temp- at Rlmax 00 ”when undergoing isotropic motion; “values obtained from T,, data; ‘Values from constrained curve-fitting (see text for details) The relaxation constant C again can be estimated using eqn. 3. Taking the interproton distances of these two methyl groups from the X-ray ~tructure’~, calculated values of C for protonated and completely D,O exchanged MGAM are 3.19~lo9 and 4.57~lo9 rad/s2 respectively which include both intra- and inter-molecular contributions. At the first glimpse, this appears to be not in good agreement with the fitted data (Fig. 2a) (1.30+0.13 and 1.49+0.27x lo9 radis’). However, a number of considerations have to be taken into consideraiion when interpreting this discrepancy. First, the inter-proton distances in X-ray structure are relatively inaccurate. In the case of the data used here the whole relaxation rate peak is not observed. It is not certain whether these two types of methyl groups have the same relaxation parameters. Nevertheless, when the curve fitting is repeated constraining the C values to that calculated using eqn. 3 (Fig. 2b), curve-fitting still results in very good agreement between experimental data and calculated ones” although this yields somewhat different values for activation energy and pre-exponential factor. The E, values obtained in both cases are in reasonable agreement with that of methylpyrano~ides’~*~~ (4-10kJ/mol). T~ values obtained for the low temperature relaxation process from both curve-fitting processes are also in good agreement with those obtained for methylpyranosides (1-4.5~ l O I 3 s), where methyl groups are present in the form of ether and have no immediate neighbor protons. Therefore, it is still reasonable to conclude that the low temperature process is the result of three-fold methyl rotation. The high temperature relaxation process is expected to be related to exchangeable protons since D,O exchange caused an obvious reduction of R,. However, this process cannot be quantitatively evaluated using T, data but it is expected to be accessible by spin lattice relaxation measurements in the rotating frame.

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2.2. Spin lattice relaxation in the rotating frame:

TI, of GA and MGAM in both protonated and D,O exchanged forms were measured over a wide range of temperature and they were all single exponential. The dependence of T,,, as defined in eqn. 4, on temperature showed two peaks at about 230 and 380 K for GA (Fig. 3) although the high temperature peak has not yet reached its maximum and has only been obtained in curve-fitting, whereas a single peak at about 280 K was observed for MGAM (Fig. 4). For both samples, however, D,O exchange removed the relaxation peaks almost completely. This implies that the relaxation processes are related to motions of exchangeable protons, these must be the protons of water and hydroxyl groups. The TI, data was fitted to an expression3' similar to that used in T, analysis:

-=-c 1

3

T,p 2 iz,

Ci[

'

'

ci 5 ci 2 'ci +-X +-x 1+40ei2zci2 3 1+oo2zci 3 1+4oO2zci2

(4)

where z, and C are rotational correlation time and relaxation constant as described previously. z, follows the Arrhenius equation as given in eqn. 2. C can be calculated as in equation 3. o, is proton Larmor frequncy and o,is the effective field for relaxation in frequency units. It is well-known that when z,(rZ and B,$>B, (BSPis the spin lock field and B, the local dipolar field), o,can be replaced with asp(spin-lock field in frequency units). However, in the present case, B,, is of the same order of magnitude as B,, the relaxation maximum appeared at 280 K, therefore, at this point 2, has about the same value as the measured T,. The relaxation is not in the so-called Slichter-Ailion region either(where z,>>TJ3*. Nevertheless, in our cases, the effects of local dipolar field can be taken into consideration by the McCall-Douglass equation3g

where oL is the local dipolar field in frequency units. In practice, wL is temperature dependent and was modeled by a double Sigmoid to give closest possible values to that of

J

1W

r?

200

u)o

400

TK

Figure 3. Spin-lattice relaxation time in rotatingpame, TI, as ahnction of temperature for galacturonic acid.

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the experimental ones within the temperature range we used2'. Parameters there were obtained from the best fit. For GA, a, was modeled as shown in eqn.6. o,=3.8x1O4+

1.53 l o 3 T - 190.8 1 + exp( 5.86

+

2.01 l o 3 T - 291.8 1+exp( 11.53

(6)

In contrast, the temperature dependence for MGAM was modeled2' satisfactorily by a single Sigmoid model: o,=3.7x1O4+

4.2 lo3 T - 259.1 1+ exp(29.37

(7)

Ti, data of protonated GA and MGAM were fitted to eqn.4 as two processes for the former and a single process for the latter. It is apparent that fitting is reasonably satisfactory for both samples and results are summarized in Table 1-2. For GA, two fitted values for C were 3.64~10'and 5 . 8 3 ~ 1 0radk2 ~ (Table 1). Since no x-ray structure of GA is currently available, interproton distances were assumed to be same as in sodium calcium a-D-galactopyranuronate he~ahydrate~~. Interproton distances in the water molecule, and between them and other protons in galacturonic acid were assumed to be same as in the case of MGAM34.Assuming similar dynamic behavior for all the OH'S calculated C values for water of hydration and hydroxyl groups undergoing flipping motions are 3.8~10'and 5.4~10'radK2 respectively. It is apparent that there is a good agreement between fitted and calculated values. Therefore, the two motions observed in Ti, were attributable to restricted motion of water and hydroxyl groups. For MGAM, if it is assumed that two processes occur, the calculated relaxation constants C are about 2.8~10'and 0.6~10'rad/sJ for water of crystallisation and hydroxyl groups respectively. The fitted value from a single relaxation process is about 2.5~10' rad/ss2.Although fitted value, for a single process, is closer to that calculated for water

100

200

TK

300

400

Figure 4. Spin-lattice relaxation rate in rotating frame, TI, as a finction of temperature for methyl galacturonic acid methyl ester.

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rather than the sum of both water and hydroxyl groups, the peak may be attributed to both motions. Given the approximations used in the calculation of C, an error is not unreasonable. In addition, it should be kept in mind that hydrogen positions determined by means of X-ray diffraction are relatively inaccurate.

2.3. Spin-spin relaxation Figure 5 shows the transverse relaxation curve of GA at 300 K obtained using a solid echo sequence. It has been discovered that the transverse relaxation process for both GA and MGAM cannot be approximated to a single unmodified Gaussian function but fits nicely to a modified Gaussian function27329 as shown in eqn.8:

I(t) = Ioexp(-)

-a2t2 2

sinbt *bt

There are two terms here, a Gaussian relaxation function and a sinusoidal term, this is typical of the transverse relaxation of protons in a rigid solid. A useful way of characterising the properties of the FID is by the residual second moment, Mzr,which can be calculated from the value of two constants, a and b, according M2,=a2-tb2/3.M,, is expected to decrease when the motional correlation time 2, is comparable to the inverse of line-width. i.e..

For GA (Fig. 6), three plateaux can be observed in the M,, curve against temperature between 100-380 K. The first one appears at 100-165 K with a value of 17.3 G2. This compares with a value of rigid lattice second moment calculated using lattice summation4' of 17.2 G2. The second and third ones were at about 210-240 and P 3 5 0 K with M,, values of about 15.9 and 14.2 G2.Since the relaxation constant, C, bears a relationship to

-5w O.OE+#I

4.0~45

8.oE45

13E-oI

Figure 5. Spin-spin relaxation curve of GA at 300 K obtained from a solid echo sequence Prst delay 12 us, second delay, 8 us, only the decay part is shown). Solid line is the best $t to eqn. 8

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the second moment reduction modulated by motions observed in TI and TI,, it is possible to predict values for M,, using the C values obtained in TI and TI, data according to eqn. 10: 3c A M 2 = 7 2Y

The second plateau corresponds to the M,, following the flipping motion of H,O (the calculated value is 16.34 G2 ). A constrained fit of the experimental values of M,, using the calculated values is shown in Fig. 7. It is clear that the final plateau predicted (15.7 G2) is higher than the observed values and that the observed data appeared to decrease with temperature. It is therefore inferred that there may be a third motional process overlapping with that of hydroxyl groups at P 3 0 0 ' K which could be whole molecule motion. The second moment can be e x p r e ~ s e d ' ~as~ in ~ .eqn. ~ ~ 11, therefore the experimental values of M,, can be fitted to obtain values of C, 2, and E, independently.

Both relaxation constants and activation energies obtained from such a fitting process to M,, of GA (Table 1) are in reasonable agreement with the data obtained from spin lattice relaxation rate data fitting (Table 3). The M,, values of D,O exchanged GA showed little change27between 150 and 370 K with a value of 6-7 G2. This is also in good agreement with calculated value of 6.94 G2. For MGAM, M,, changed from 12.5 to 11.3 G2(Fig.7) The calculated rigid lattice second moment (Table 3) from rigid lattice summation4ois about 20.2 G2. The first plateau value (12.5 G2) is consistent with a calculation based on the effects of methyl rotation. Using the curve fitted values of relaxation parameters from TI and eqn. 10, M,, is

17

.........

.............

'*.4u. 100

IW

T (K)

300

400

Figure 6 . Second moment of galacturonic acid, M2,., as afirnction of temperature.

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lo-

$ .

50

150

250

350

450

T/K Figure 7 Second moment of methyl galacturonic acid methyl ester, M2r, as a function of temperature.

expected to be reduced by 6.62 G2 above 45 K. The calculated value for M,, above 100 K is thus 13.6 G2 which is larger than experimental value. A second reduction in M,, is expected at about 255 K, however, the change observed is 1.2 G2 which is larger than a calculated value of 0.5 G2. This probably implies the presence of a third motion which could be whole molecule motions as similarly observed in the case of GA. The M,, of D,O exchanged MGAM had values at 7-8G2 which concurred with a calculated value (7.6G2)when the effect of methyl rotation is taken into account.

Table 3 Data for second moment M2r of galacturonic acid and methyl galacturonic acid methyl ester2 MGAM Measured plateau values First Plateau (G’) Second Plateau (G2)

MGAM(D)

GA

GAm

12.5k0.2 7.5k0.5 (100-180 K) (100-370 K) 11.3k0.2 ( P 3 5 0 K)

17.3k0.1 (100-150 K) 15.9k0.4 (210-240 K) 14.2k0.8 ( P 3 5 0 K)

7.5k0.5 (100-370 K)

20.2 13.2 12.6 12.4

19.6 7.6

17.2

7.0

45 K 255 K 255 K

45 K

Third Plateau (G’) Calculated M2 (Gl) Rigid lattice M, (G2) After CH, rotation (G’) H,O flimine motion (G2) OHs flipping motion (G2) T,,,,, (for M, reduction) CH,

H2O OHs

16.3 15.7

168 K 256 K

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To sum up, the major spin lattice relaxation pathways for GA are reorientation of water molecules and hydroxyl groups. For MGAM, methyl groups in both forms of ether and ester are the major contributors to the high frequency relaxation at low temperature. Reorientation of the hydroxyl groups and water molecules are less effective at spin lattice relaxation in the laboratory frame at low temperature but are the major contributors to the spin lattice relaxation in laboratory frame at high temperature or in spin-lattice relaxation in the rotating frame. These motions also cause the largest changes in rigid lattice second moment at high temperatures. The three types of motions give a complete picture of the spin-spin as well as spin lattice relaxation in both the laboratory and rotating frames in these systems. These relaxation pathways of GA and MGAM are also expected to contribute in the forms of their polymers in cell walls. Quantitative characterization of the GA and MGAM motions provides a foundation for tackling the motional characteristics of the cell walls.

3. PROTON RELAXATION IN PLANT CELL WALLS In this section, three materials are described. Two potato cell walls prepared by Ryden method42and Waldron method' respectively and are referred to as PA and PB in the text. A Chinese water chestnut sample (CWC) has also been used to serve as a comparison to potato samples. CWC was three-time exchanged with D,O and dried before NMR measurements. NMR relaxation properties were focused on TI, T,, and T, as discussed in the model systems.

3.1. Spin-lattice relaxation in laboratory frame The spin-lattice relaxation in the laboratory frame of vacuum dried potato cell walls, PA and PBH (dried from HZO), showed bi-exponential decays over the temperature range studiedI6(100-360 K). The long component is designated T,, and short one TIs. The rates of these two relaxation components were defined as R,, (=TI;') and R,, (TI;') respectively. The relative fraction of TI, is referred to as FTIL.For PB, neither D,O exchange nor substantial D,O hydration (up to h- 4.10) changed the bi-exponential behaviour. If a common spin temperature is maintained throughout a solid by rapid spindiffusion, a single proton T, would be observed. Bi-exponential relaxation behaviour of cell wall materials implies the existence of at least two separate domains whose size is such that spin diffusion exchange is not fast enough even on the time scale of TIL. Figure 8 shows the proton spin-lattice relaxation time as a function of temperature for both cell wall materials of potatoesI6,PA and PB, and Chinese water chestnuts. A number of observations can be made here. First, Protonated PA and PB have very different response to temperature changes. There is some convergence of behaviour at the high temperature end of the graph but increasing divergence as the temperature is lowered. Below 300 K the rate of relaxation of PA starts to show an increasing trend. There appears to be a levelling off around 150-200 K, followed by another very rapid increase. According to eqn. 1, a motion is characterised by a relaxation peak in R, curve as a function of temperature where o,zc is approximately 0.6229.Low temperature maxima in

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Figure 8. Proton spin-lattice relaxation time as a function of temperature.

spin lattice relaxation are often associated with methyl group rotation and it is likely that the low temperature peak is due to residual DMSO used in the extraction process4'.The possibility that extraction with DMSO has in some way mobilised pectin methoxy groups cannot be discounted but evidence for residual DMSO has been obtained in both proton43 and carbon magic angle spinning spectra*'. Therefore, the results indicate that great care must be taken in the choice of preparation method and comparison between samples. It is also worth-noting that the CH, groups in the form of esters in pectin might be expected to show low temperature methyl maxima as in the cases of MGAM27and pectin3', however, these are not observed for PBH and PB1 samples. The expected contribution of methyl group rotation can be calculated according to Kubo and Tomita 28, for dipolar relaxation characterised by an exponential correlation function. The expression as given in eqn. 1 assumes only one correlation time and it would be expected that there would be distribution of correlation times in such a complex system as plant cell walls. However, the single correlation time assumption allows an estimate of the maximum contribution of methyl group rotation to relaxation. Chemical analysis of the potato cell wall materials suggests that only 3% of the protons are present in methoxy groups. Rotational motion of this type of methyl groups often lead to a R, maximum at 80-100 ~27.44 . If interproton distances (r) in methoxy groups of cell walls are similar to that in methyl-a-D-galacturonic acid methyl esteP4which is 1.77 A on average, then the value of C can be estimated to be 1 . 6 7 ~ 1 0s-*. ~ Assuming that no other groups contribute to relaxation the predicted value of R, at the maximum is about 0.4 s-'. The motion of methyl groups in rhamnose/fucose often results in a R, maximum at higher temperature (120-200 K) because the protons there have immediate neighbours. However, the proportion of this type of protons accounts for only less than 0.4% of all protons. Given the same assumption about interproton distances and single correlation time, the relaxation constant for them would be 3x10' s9 and their predicted contribution to R, is less than 0.1 s-l. Moreover the value of 0.4and 0.1 s-*are both over-estimates. Both types of methyl groups probably will experience a variety of environments. The contribution to relaxation will thus be smeared out over a considerable temperature range. Given the

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weakness of the contribution to relaxation, it is not surprising that no maximum due to methyl group rotation is observed. At high temperatures there is a clear tendency towards an increase in relaxation rate in both PB1 and PBH. The behaviours of PB1 and PBH do however differ slightly. At low temperatures PB1 is more slowly relaxing than PBH, but this is reversed at high temperature. This can arise from rapidly relaxing groups, containing non-exchangeable protons, acting as a relaxation sink for groups containing exchangeable protons. The actual situation in cell walls is complex, but if only two contributing groups are considered the behaviour may be illustrated by considering the equation: RIL=PERE+ PNRN,where the subscripts refer to exchangeable and non-exchangeable protons, P and R are the relative populations and rates respectively. The sum of P, and PN is unity. If R$R, and PN 3s'). In practice, hydroxymethyl groups probably will experience a variety of environments, thus the contribution to relaxation will appear over a considerable temperature range this will result in a smaller contribution to relaxation at the maximum. The behaviour of short component, T,,, follows the general trends of the long relaxation time process. Above 300 K the data is very scattered. This is because the Eraction of the short component is small and fitting error becomes large. Nevertheless the overall trends are clear. Compared to PBI, CWC shows much more efficient relaxation at high temperature; the difference in relaxation rate is an order of magnitude. This is rather surprising since CWC has only 10% of its protons in hydroxymethyl groups, this is lower than in PB. However, observation of a signal in ESR spectra of CWC" offers a good explanation: the presence of free radicals in CWC provides an extra and powerful relaxation pathway. There was no obvious maxima at low temperature regions, since the percentage of methyl protons is even less than that of PB, a weaker contributionto R1 would be expected.

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For PBH, F,,, accounted for 70-75% of the population below 200 K and 85% above 300 K. Since strong dipole-dipole coupling results in faster spin diffusion, it would be expected to be faster at lower temperature than at high temperature where occurrence of molecular motions often weakens dipolar coupling. However, all three cell wall materials showed more clearly defined bi-exponential relaxation behaviour for TI at low temperature rather than at high temperature region. This implies that uniformity of TI at higher temperature arises from a convergence of the relaxation times rather than from mixing by spin diffusion.

3.2. Spin-lattice relaxation in the rotating frame Spin-lattice relaxation in the rotating frame (TIP) is sensitive to the motions comparable to the frequency related to the spin-lock field46 (104-105 Hz). Two TIP components can be detected for all PB samples with the longer one designated TipLand short one TIPS.The corresponding rates are RlpLand Rips,respectively, and the fraction of magnetisation relaxing with a time constant TIP, is FTlpL.Figure 9 shows RipLof dry PB1 as a function of temperature and spin-lock field. A single broad relaxation peak is observable with all three spin-lock frequencies. One of the possibility is that the relaxation peak is related to the motions of CH,OH/D g r o ~ p sin~ cell ~ , ~walls. ~ RIPcan be calculated according to eqn. 4. If hydroxymethyl groups are the only TIPrelaxation source and they have a single correlation time, RIPcan be estimated using equation 4. Assuming the same values as used for the TI calculations, the maxima are estimated to be 400-650 s" at 300-370 K, 300-450 s-I at 300-370 K and 150-300 s-I at 320-380 K when aspare 40,67 and 125 kHz respectively. It is apparent that the experimental rates are less than half of that estimated and the peaks appear at lower temperatures than predicted. As the hydroxymethyl groups are experiencing a range of environments they will have a distribution of correlation times, the curves will therefore be broader and with a less intense maximum than

0

0"'

Figure 9. Spin-luttice relaxation time in the rotatingframe for dry PB (2% D 2 0 wt/wt) as afunction of bo fh spin-lock field and temperature.

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predicted from the single correlation time model, some support for the distribution of correlation times can obtained from the frequency dependence of the relaxation rates, since there is a field dependence on both sides of the relaxation maxima, which would only be expected if there were more than one correlation time contributing to motion. Given a distribution of correlation times, the discrepancy between estimated maxima and experimental ones is not a unreasonable outcome. It is expected that the hard-to-exchange OH groups may contribute to some extent in this region. There may also be contributions from sugar ring puckering. Solution state indicate that in solution rates are of the correct order of magnitude. But no direct evidence yet exists to indicate that such motions are retained in the solid state. 3.3. Spin-spin relaxation

Spin-spin relaxation curves (i.e., FID) of cell walls generally consists of at least two distinct parts and the baseline corrected FID can be fitted with the f u n ~ t i o n ~in~eqn.5 " ~ as shown in Figure 10:

I (t)

=

I,exp(-)

-a2t2

2

sinbt

t

bt

T2e

* -+ 12exp(--)

Two components are assumed with 11 and I2 representing their signal intensities. The first term consists of a Gaussian relaxation term modulated by a sinusoidal term with a and b representing relaxation parameters just as in the cases of model systems above. The second component is an exponential term and is typical of protons in the motionally narrowed regime of motion, it is characterised by a time constant T2e. One of the most convenient way of characterising the Gaussian term is by the second moment M,, which can be caiculated from the value of a and b as mentioned earlier. The relative proportion of the first component, F,, can be calculated as FG=Il/(Il+12).

Figure 10. Spin-spin relaxation curves of the potato cell walls obtainedfiom a solid echo sequence.

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q c

w

"1 80

Figure 11. Temperature dependence of spin-spin relaxation properties of dry PB (2% D20).(a) M2r as a function of temperature; (b) percentage proportion of the Gaussian component, FG.

At a constant proton density, the value of M,, depends on the degree to which motion in the system results in reducing static dipolar interactions. Typically the value of M,, reduces as temperature increases. PB dried from D,O (Fig. 1 1 ) and H,O (data not shown) showed slightly different Mar reductions, of about 4 @ (from 14 to 10) and 3 @ (from 14 to 11) respectively over the temperature range of 100-360 K. As coqelation times for motion move from the rigid lattice regime into the motionally narrowed regime they begin to contribute to spin lattice relaxation processes. M,, of PB as a function of temperature and hydration levelsL6showed a 3 G2 difference between dry PB and wet PB samples (>22% D,O, wt/wt) at very low temperature (100-200 K). For the most hydrated sample (1 10% D,O) there is a very strong reduction in the second moment. This may well be associated with the partial dissolution and or chemical degradation of the cell wall components disrupting the structure of the wall. The proportion of the Gaussian component is shown (figure 11b) a modest effect for dry PB at about 250-280 K. However, increasing hydration led to much greater effectsI6. For the more hydrated materials there is a sharp effect at the freezing point. Below this temperature, as expected, all the samples behave in roughly the same way due to their similar unfrozen water contentsL6.Above the freezing point increasing hydration led to progressively smaller proportions of the Gaussian component. When h was about 4.10, F, dropped to about 30% of the magnetisationL6,this is equivalent to the amount of cellulose

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protons in the system and may indicate that partial dissolution and disruption of the cell walls leaves only the cellulose component unaffected. The relaxation constant, T2e,of the exponential component showed a consistent increase, with temperature and hydration above 270 K, suggesting the motion of the polymers is enhanced when hydration level or temperature increasedI6. Acknowledgement

This work was supported by a Competitive Strategic Grant of the BBSRC. We also

thank Dr P. Needs (Institute of Food Research, Nonvich) for the MGAh4 sample. References

1. 2. 3. 4. 5. 6. 7.

8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

C. T. Brett and K. W. Waldron, 'The Physiology and Biochemistry of Plant Cell Walls', Unwin Hyman, London, 1990 G. J. McDougall, I. M. Morrison, D. Stewart and J. R. Hillman, J.Sci.FoodAgri., 1996,70, 133. D. M. Klockeman, R. Pressey and J. J. Jen,J FoodBiochem., 1991,15,317. G. S . Mudahar and J. J. Jen, J. Food Sci., 1991,56,977. M. L. Parker and K. W. Waldron, JSci.FoodAgri., 1995,68,337. N. Muramatsu, T. Takahara, K. Kojima and T. Ogata, Hurtscience, 1996,31, 114. K . W. Waldron, A. C. Smith, A. J. Parr, A. Ng and M. L. Parker, Trends Food Sci. Techn., 1997,8,213. M. C. Jarvis, Plant Cell Environm., 1992,15, 1. S . E. Whitney, J. E. Brigham, A. H. Darke, J. S. Reid and M. J. Gidley, Plant J , 1995,8,491. N. Bloembergen, E. M. Purcell and R. V. Pound, Phys.Rev., 1948,73,679. A. L. Mackay, M. Bloom, M. Tepfer and I. E. Taylor, Biopolymers, 1982,21, 1521. A. L. Mackay, M. Tepfer, I. E. Taylor and F . Volke, Macromolecules, 1985,18, 1124. A. L. Mackay, J. C. Wallace, K. Sasaki and I. E. Taylor, Biochem., 1988,27, 1467. I . E. Taylor, J. C. Wallace, A. L. Mackay and F . Volke, Plant Physiol., 1990,94, 174. J. C. Wallace, A. L. Mackay, K. Sasaki and I. E. Taylor, Plantu, 1993,190,227. H. R. Tang, P. S. Belton, A. Ng, K. W. Waldron and P. Ryden, Specirochim. Acta A, 1998, In Press K. Schmidtrohr and H. W. Spiess, 'Multidimensional solid state NMR and Polymers', Academic Press, London, 1994 M. A. Ha, B. W. Evans, M. C. Jarvis, D. C. Apperley and A. M. Kenwight, Carbohydr.Res., 1996,288, 15. M . A. Ha, D. C. Apperley and M. C. Jarvis, Plant Physiol., 1997,115,593. K. M. Fenwick, M. C. Jarvis and D. C. Apperley, Plant Physiol., 1997,115, 587. H . R. Tang, P. S. Belton and A. Ng, J Agri. Food Chem. (Submitted), 1998 E . R. Andrew, W. S. Hinshaw, M. G. Hutchins and R. 0. Sjoblom, Mol. Phys., 1977,34,1695. E . R. Andrew, W. S. Hinshaw, M. G. Hutchins and R. 0. Sjoblom, Mol. Phys., 1976,31,1479.

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24. E. R. Andrew, W. S. Hinshaw, M. G. Hutchins, R. 0. Sjoblom and P. C. Canepa, Mol. Phys., 1976,32,795. 25. Y . L. Wang, P. S. Belton and H. R. Tang, Chem. Phys. Lett., 1997,268,387. 26. P. Belton and Y. L. Wang, Mol. Phys., 1997,90, 119. 27. H. R. Tang and P. S. Belton, Solid State Nucl. Magn. Reson., 1998, In Press 28. R. Kubo and K. Tomita,J.Phys.Soc.Jpn., 1954,9, 888. 29. A. Abragam, 'The Principles of Nuclear Magnetism', Oxford University Press, 1961. 30. E. C. Reynhardt and L. Latanowicz, Chem. Phys. Lett., 1996,251,235. 31. S . E. B. Gould, 0. B. Gould, D. A. Rees and W. E. Scott, J. Chem.Soc.Perkin Trans.2, 1975,237. 32. J. Hjortas, B. Larsen, F. Mo and S. Thanomkul, Acta Chem.Scand.B, 1974,28, 133. 33. S . Thanomkul, J. Hjortas and H. Sorum, Acta Crystallogr., 1976,32,920. 34. D. Lamba, G. Fabrizi and B. Matsuhiro, Acta Crystallogr.Sec.C, 1994,50, 1494. 35. H. R. Tang and P. S. Belton, unpublished results. 36. L. Latanowicz, E. C. Reynhardt, R. Utrecht and W. Medycki, Ber. Bunsen-GesellPhys. Chem. Chem. Phys., 1995,99, 152. 37. R. K. Harris, 'Nuclear Magnetic Resonance Spectroscopy', Pitman Books Ltd, London, 1983 38. C. P. Slichter and D. Ailion, Phys.Rev., 1964, 135 A, 1099. 39. D. W. McCall and D. C. Douglas, Appl.Phys.Lett., 1965, 7, 12. 40. J. H. van Vleck, Phys.Rev., 1948,74, 1168. 41. L. Latanowicz, E. R. Andrew and E. C. Reynhardt, J.Magn.Reson. A , 1994,107, 194. 42. P. Ryden and R. R. Selvendran, Biochem.J., 1990,269,393. 43. P. S . Belton, R. Boetzel, A. Gil and P. Ryden, unpublished results. 44. H. R. Tang, B. L. Zhao and P. S. Belton, Poster in 4th Int. Con$ Appl. Magn. Reson. Food Sci.. 45. L. Latanowicz and E. C. Reynhardt, Ber.Bunsen-Gesell. Phys. Chem.-An Int. J. Phys. Chem., 1994,98,818. 46. V. J. McBrierty and K. J. Packer, 'Nuclear Magnetic Resonance in Solid Polymers', Cambridge Univeristy Press, 1993 47. S. Cros, A. Imberty, N. Bouchemal, C. H. Dupenhoat and S. Perez, Biopolymers, 1994,34, 1433.

Probing Molecular Motions of Low Moisture Starch Gels by Carbon-13 NMR Yael Vodovotz and Pavinee Chinachoti DEPARTMENT OF FOOD SCIENCE, UNIVERSITY OF MASSACHUSETTS, AMHERST, MA 01003, USA

1 INTRODUCTION The stability of paracrystalline polymers such as starch can be characterized at a structural level using thermal analyses in order to identify phase transition and plasticization behavior of the system. At a molecular level, changes in the polymer back bone and side chain mobility and the role of water molecular dynamics have a profound impact on such attributes.’32*’ Gelatinized starch at < 20% moisture has been characterized to show a glassyrubbery transition range of 60 - 90 “C.’ This was analyzed using Dynamic Mechanical Analysis @MA) and Differential Scanning Calorimetry (DSC). However, in some cases, a transition may be observed over a temperature ranging as much as 80 O C . ’ * * Since DSC and DMA are long range techniques, they detect structural changes in a macroscopic scale. Information obtained does not necessarily reveal the nature at the molecular level. Molecular spectroscopic techniques, such as Nuclear Magnetic Resonance (NMR), are most useful in evaluating molecular motion, such as in starch and Solid state carbon-13 CPMAS (cross-polarization magic angle spinning) NMR has been used to investigate carbon-chain motion of various polymers. In starch, investigation of amorphous and crystalline structures by carbon-13 CPMAS NMR has been rep~rted.’*~” TI, (rotating-frame relaxation time) can be used to measure molecular motions in glassy, rubbery, and crystalline solids. Tlp(’H) for starch has been found to decrease after heating to melt the starch crystals.’ Observed TI, (‘H) is influenced by a number of factors including the molecular motions of each domains, domain size, spin diffusion, cross relaxation, and dipoledipole interactions between water and starch.’ T1, (I3C) on the other hand provides information of the carbon mobility more directly and therefore changes in the polymer molecular dynamics can be monitored and related to phase tran~itions.~

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The objective of this work was to apply this method (TI, (I3c) NMR) to gelatinized starch in order to investigate the molecular mobility of the starch under going a glass transition.

2 MATERIALS AND METHODS Gelatinized wheat sample was prepared by mixing equal parts of starch starch (Gemstar 100, Manildra Milling Corporation, Minneapolis, MN) and water and heating to 90 "C between two sheets of aluminum in a hot press (F.S. Carver, Inc., Summit, NJ). The moisture of the sample was 47%. To adjust the samples to 17%moisture content, the starch gel was dried over P,O, for 5 days and then ground by hand. The sample was further dried to close to -0% moisture in a vacuum oven (for 2 days). Finally, the ground starch gel was equilibrated for 4 days at 25°C over saturated KCl solutions (0.84 a,,,). Very little starch recrystallization (
C Cross Polarization Magic Angle Spinning measurements A 200 MHz spectrometer (Bruker Instruments, Billerica, MD) equipped with an IBM solids unit was used to acquire the data. Cross -polarization (CP) and spinning at 4 kHz at magic angle spinning (MAS) were applied. Figure 1 shows the pulse sequence using the following parameters: 90" proton pulse width of 5 ps, carbon pulse width of 2 ms, a recycle delay of 3 sec, contact time of 2 ms and the decoupling field of 50 MHz. The "C signals, were acquired at variable spinlock time of 0.5-30 ms. To control the variable temperature protocol, a Bruker variable temperature controller was used. The desired temperatures (0, 10,20,30,40, 50 and 60 "C ) were reached within 5 minutes for each step. At each step, the sample was held for at least 15 minutes prior to the NMR analysis.

3 RESULTS AND DISCUSSION Figure 2 shows a typical "C CPMAS spectrum of a 17%moisture gelatinized starch sample with the corresponding carbon numbers above each

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decouple

n/2 Spin lock

1

IH

Spin lock

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Figure 1. Pulse sequence for "C CP-MAS N M R experiment ,335

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Figure 2. 'C CP-MAS NMR spectrum of gelatinized wheat starch containing 17 % moisture (wet basis).

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No drastic change in spectra was found upon heating from 0 "C to 60 "C although a gradual decrease of intensity (and carbon 1 broadening) was noted. The spectra were typical of a completely amorphous matrix and no crystalline starch present the decreased intensity observed upon heating could indicate a sign of a glassy-rubbery transition.'

4.5

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Temperature ("C) Figure 3. The change in intensity of carbon 1 of a 17 % moisture starch gel at various temperature obtained from "C CP-MAS NMR measurements Since carbon - 1 showed the most change, its intensity was plotted against temperature (Figure 3). A decrease in intensity at >20°C suggested an increase in segmental mobility resulting in less efficient cross relaxation. This increase in segmental mobility is expected during a glass transition which was earlier observed in this sample by DMA.' The glassy-rubbery transition began at 20 "C and ended at around 60 "C To better characterize the motions of the polymers, I3C TIPof the 17 % starch gel samples at different temperatures were measured. The intensities of the various carbon peaks were obtained at different spin lock times for a particular temperature as shown for carbon 1 at 20 "C (Figure 4). A typical plot of the log normalized intensity vs. spin lock time ( 7 ) is shown in Figure 5

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for carbon 1 at 20 “C. The TIPvalue was obtained by fitting this plot to a single component model described by an exponential decay equation: [M/M, = exP(-r/ TIP)I‘ The 13CTIPvalues were in the similar range in earlier studies with some discrepancies probably due to the experimental differences.’* Although increased mobility (longer TIPvalues) may be expected at higher moisture contents, Kou (1998) reported that moisture had a small impact on Tlp,13

. -40 . . . -60 . . . -80. . . . . . . . . . . . . (PPd

Figure 4 ”C CP-MAS NMR spectra of a 17% moisture starch gel at 2OoC obtained at various spin lock times (indicated above each spectra). Spin lock times are in milliseconds.

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1.o

0.8 z c rn .

5

+ c -

0.6

'0

w

-

0.4

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2 0.2

0.0 _ _

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correlation time (ms)

Figure 5 . Carbon-1 FID curve for 17% starch gel obtained at 20 "C and fitted with a single exponential equation. The calculated TIPvalues are tabulated in Table 1. A two-component model was also attempted with a small improvement in the degree of fit." Little to no change was observed in the "C TIPvalues (no change in mobility) upon heating fiom 0 to 60°C. Although this may lead to a simple conclusion that during this temperature increase (where glass transition may be observed by DMA), there was no starch mobility change. However, factors such as domain size, and individual TIPin each domain, are expected to Table 1. TIPvalues obtained for carbon 6, carbon(s) 2,35 and carbon 6 at different temperatures obtained by fitting the normalized intensity vs spin-lock time curves with a single exponential.

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influence the observed TIP.TIPminimum is indicative of mobility changes such as those associated with a glassy rubbery transition." The minimum usually reflects a change of 30 to 40 ms in the "C T1, values. In any case, it was expected that T,P should show some change during this glass transition temperature. None of these was observed during the glass transition range. For the case of proton, TIPminimum of a dry native waxy maize starch has been reported to be at 270 OK primarily due the motion of the OH groups attached to the ~arb0ns.l~ Therefore, in our case of 17% water starch it may be speculated that the onset starch-OH mobility occurs at < 0 "C which was below our experimental temperature range. Since a biphasic behavior was speculated (although recrystallization at 17% moisture may be limited), the data were fitted with a two component model. In general, the two component fit was a slightly better fit. The rigid and mobile TI, 's were in a 4 0 ms and 40-90 ms, respectively for carbon-1. However, changes over the 0-60 "C showed no particularly consistent trend. Since the starch has been gelatinized and quickly adjusted to 17% moisture, the presence of recrystalline starch, if present, was expected to be very small or relatively insignificant (although hydration normally leads to starch chain mobility increase which facilitates recry~tallization).'~ It was possible, however, that upon heating starch could change to become more heterogeneous in the carbon chain mobility due to the differences in onset mobilization temperature among various domains. If so, the data comparison simply using single or double exponential models alone may not yield useful information and more sophisticated experimental approach is needed. Although the results fiom this work is not conclusive, starch chain mobility increase was suspected upon a glass transition based on the "C CP/MAS intensity data. However, quantitative analysis of the TI, data was inconclusive possible due to limited number of experimental conditions used and inherent heterogeneous domain behavior of the material. Work is underway to further investigate starch mobility and its phase transition. Other N M R techniques, in particularly ones that address the heterogeneity issue, should be useful in future starch characterization. ACKNOWLEDGEMENT

This work was financially supported by Massachusetts Agricultural Experiment Station (MAS 000709) and the Department of Defense-ASSERT program in support of Dr. Vodovotz graduate study. Access to the N M R facility at the Polymer Science and Engineering department as well as technical assistance provided be Dr. L. C. Dickinson are highly appreciated.

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REFERENCES 1. Y. Vodovotz and P. Chinachoti, Journal of Food Science, 1996,61,932. 2. Y. Vodovotz and C. Chinachoti, Journal OfAgriculture and Food Chemistry, 1998,46,446. 3. S . Li, L.C. Dickinson, and P. Chinachoti, Cereal Chemistry. 1996, 73, 736. 4. J. Schaefer, E.O. Stejskal and R. Buchdahl, Macromolecules, 1977,10, 384.

5. J.M.V. Blanshard, E.M Jaroszkiewicz, and M.J. Gidley, ‘“MR Applications in Biopolymers. ACS Symposium Series”, Plenm Pub. Corp., New York.1990. 6. K.R. Morgan, R.H. Fumeaux, and R.A. Stanley, Carbohydrate Research, 1992 235, 15. 7. M.T. Kalichevsky, E.M. Jaroszkiewicz, S. Ablett, J.M.V. Blanshard, and P.J. Lillford, Carbohydrate Polymers, 1992, 18, 88. 8. H. Saito, and R. Tabeta, Chemical Letters, 1981, 7 13. 9. K.J. Zeleznak and R.C. Hoseney, Cereal Chemistry. 1987,64, 121. 10. L.C. Dickinson, P. Morganelli, C.W. Chu, Z. Petrovic, J. Macknight and J.C.W. Chien, Macromolecules 1988,21,346. 11. Y. Vodovotz, Ph.D. Thesis. University of Massachusetts. 1996. 12. H. Saito, H. Shimizu, T. Sakagami, S. Tuzi and A. Naito, Presented at the Second International Conference of Applications of Magnetic Resonance in Food Science, 1994, Aveiro, Portugal. 13. Y. Kou, Ph.D. Thesis. University of Illinois (Urbana-Champaign). 1998. 14. S. F. Tanner, B. P. Hills and R. Parker, Journal of Chemical Society Faraday Trans., 1991,87,2613.

Applications of ESR Imaging in Food Science D.G. Gillies DEPARTMENT OF CHEMISTRY, SCHOOL OF PHYSICAL SCIENCES,UNIVERSITY OF SURREY, GUILDFORD, SURREY GU2 5XH, UK

1 INTRODUCTION

The wide range and number of applications of NMR imaging, MRI, have not been emulated by ESR imaging, EMRI. In particular, the exciting applications of MRI in food science have resulted in a recent book by Hills'. As with MRI, a strong driving force in EMRI has been for in VIVO studies and this area has been reviewed: together with the principles of' EMFU. A general review appeared in 19903 and two journals have devoted single-subject issues to in VIVO EMRI.4*5The design criteria for spin probes for EMRI and spectroscopy have been discussed recently.6

2 PROPERTIES OF RADICALS

Nitroxyl radicals are well-suited for studies of food systems as their ESR spectra are sensitive to the local environment. The local microviscosity influences the molecular motion which affects the bandshape. Both the g factor and the proton-nitrogen hypertine coupling are influenced by the solvent polarity and the local oxygen concentration can be estimated from line broadening. The radical moieties may act as spin probes or as spin labels.' The radicals should be stable and able to survive food preparation. For food studies with spin probes it is important to control partitioning between hydrophilic and hydrophobic phases. For this work we use members of a novel class of stable nitroxyl radicals based on 1,1,3,3-tetramethylisoindolin-2-yloxyl (TMIO)(1) that we have and for which we have obtained detailed magnetic resonance spectroscopic data.'' For enhanced lipid solubility, S-(t~-A.lkyl)-l,1,3,3-tetramethylisoindolin-2-yloxyls(RTMIO) (2) are used and for enhanced aqueous solubility, sodium 1,1,3,3-tetramethylisoindolin-2-yloxyl-5-

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sulphonate ( N a M O S ) (3) is used. For the purposes of imaging the spectral lines should be as narrow and as few as possible. Thus we have deuteriated the methyl groups to remove the broadening effects of the unresolved proton coupling from the methyls and substituted with nitrogen-15.4 The increased nitrogen hyperfine coupling allows the implementation of larger gradients if only one line is to be used for imaging. The mobility of ‘ 4 ” a M O S in gelatin gels shows a strong dependence on the humidity.’’ As the humidity is reduced from 100% there is a gradual broadening of the solution-state mobile spectrum. At about 84% there is an abrupt reduction in mobility when the spectrum changes to one with typical solid-state characteristics. Broadening spectra make imaging more difficult, reducing the resolution. These spectra were obtained at the usual X-band fiequency (.: 9.1 GHz).Interestingly, calculations show that at low fiequency, 200 MHZ, the normal nitrogen-14 isotopomer has a spectrum much more favourable for imaging than one substituted with nitrogen-15.’* The spectrum is dominated by a narrow central feature whereas that of the nitrogen-15 moiety is much more spread out. The ability to study two phases simultaneously has been demonstrated with a sample of salad cream.I3 With ‘4”aTMIOS in the aqueous phase and deuteriated ‘%”MIOD in the oil, the spectrum showed the expected triplet and doublet respectively. Also as expected, in the more polar medium the hyperfine coupling was larger and the g factor smaller. In a recent study I4NTMIOwas introduced into a sample of dough. The spectrum was a superposition of two spectra, one from the hydrophobic lipid phase, the other from the aqueous phase.14 The spectra were deconvoluted using the program EWVOIGTN.” They were of about equal intensity reflecting the much greater solubility in the relatively small amount of lipid phase. Although images can be obtained at micromolar concentrations, maximisation of sensitivity is always important. Normally the maximum concentration is = 1 mM if exchange effects are to be avoided. 3 A SIMPLE 1-DIMAGING EXPERIMENT

Spatially localised spectroscopy may be carried out by moving the sample through the active region of an X-band cavity and has been used to measure the translational diffusion constant of TMIO radicals.‘‘J~-’* A 10 cm long thin-walled capillary of 1.3 mm i.d. was filled to about 4 cm with the sample, for instance an aqueous gel, on top of which was placed 3 p1 of a millimolar solution of the spin probe. The capillary was placed in a 4 mrn 0.d. ESR sample tube and over a period of several days the height of an ESR spectral line was measured as a fbnction of position as the sample tube was pushed through the cavity with the aid of a calibrated screw. The data represent the convolution of the radical distribution with both the lineshape (first-derivative Lorentzian curve) and the ESR cavity sampling function. The latter was determined for the Bruker TEIOZ cavity with the aid of a 0.5 mm single crystal of lithium phthalocyanine which has a single sharp ESR line. The resolution was increased by the insertion of a cylindrical copper foil shield with a central 4 mrn long cut-away section. The presence of the copper affects the tuning of the cavity, reduces the quality factor, Q and reduces the signal intensity. Although the experiment is slow, taking several days to obtain a set of data, each measurement only takes about 20 minutes. It has the advantage of simplicity, requiring

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only a standard spectrometer and the spectrum is observed directly at all times. It is a 1-D analogue of an early MRI method, FONAR19, where the sensitive volume is scanned through a small localised sensitive volume. It is also analogous to the 1-D STRAFI (STRAY Field Imaging) MRI technique where the sample is moved through the sensitive slice determined by the pulse bandwidth in the very strong 'stray' field gradient produced by a superconducting magnet away from its centre.20 4 PRINCIPLES OF EMRI

The basic principles are similar to those of NMR imaging but the practice is significantly different owing to the short electron relaxation times. Thus almost all studies have used continuous wave (CW) field-swept spectra rather using pulse techniques. A magnetic resonance line is broadened inhomogeneously by the application of a linear magnetic field gradient in order to 'frequency-encode' the spins in space. There is a consequent loss of sensitivity arising from having to observe the signal in the presence of the gradient. The minimum gradient required to obtain a resolution of 6x from a linewidth proportional to 1/T2is given by the imaging version of the Rayleigh criterion:21 G > l/(T2yGx) where y is defined in the usual way, o = yB0. Thus for NMR and ESR experiments of the same

Hence for Tz(NMR) = 0.1 s and T2 (ESR) = 350 ns:

Thus EMRI requires much larger gradients for the same resolution. Considerations limiting the applied gradient include gradient coil heating and the acceptable loss in signal to noise ratio. For very small samples at X-band, Ikeya and MikiU have constructed a I-D system using micro-fabricated gradient coils providing up to 200 mT cm-' at a pin-hole region and demonstrated resolution of > lpm. For larger samples up to 10 mm, using gradients an order of magnitude smaller reduces the attainable resolution to = 10 pm. Dielectric losses at X-band limit aqueous samples to < 1.5mm. Decreasing the frequency reduces the losses and allows larger samples. At L-band ( x 1.1 GHz) lossy samples up to 30 mm may be accommodated with a resolution of = 1 mm. For larger samples, we have used radiofrequencies in the range 200 to 400 MHz." In our laboratory we have obtained 2-D spatial images of spin density of nitroxyl spin probes by using two orthogonal gradient coils in order to rotate the gradient in the plane so that spectra can be obtained from typically 16 or 32 projections.20 The gradient-broadened spectra are Fourier deconvolved using the zero-gradient spectrum. 2-D images are constructed using the filtered back-projection reconstruction method25with Ram-LakZ6prefiltering. Extension to three spatial dimensions was demonstrated for a sample of irradiated

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quartz.z7 At L-band, Zweier and co-workersz8 reported 3-D images of the rat heart with a resolution of 1-2 mm using a nitroxyl radical. For situations where the spectrum varies with spatial position (other than just in amplitude) spectral-spatial imaging was developed.z9330Spectra of a pseudo object of length AJ3 in the spectral dimension and L in the spatial dimension are obtained at a series of magnetic field gradients corresponding to projections at angle a to the spectral axis. The technique was demonstrated at X-band for one spectral and one spatial dimen~ion.~' More recently Zweier and co-workers have extended the technique to four dimensions in their studies of rat hearts at L - b a ~ ~ d . ~ ' 5 INSTRUMENTAL CONSIDERATIONS AND TECHNIQUES 5.1 Resonators

For imaging purposes it is important that the radiofrequency field is as uniform as possible. The loop-gap resonator, originally developed for X-band by Hyde and F r o n ~ i s z ~ ~ has been used for larger samples at both L-band and radiofrequencies. The radiofrequency field is perpendicular to the axis of the cylinder. In our laboratory we have used birdcage resonators at radiofreq~encies.~~ These are used for MRI and have good homogeneity of the radiofrequency field which conveniently is directed along the axis of the cylinder which is co-axial with the axis of the static field. For variable-temperature operation on food systems we have built an 8 cm 0.d. birdcage on a quartz d e ~ a r Recently, .~~ a birdcage resonator operating at L-band has been reported.35 5.2 Magnets

At radiofrequencies where the magnetic field is in the order of 10 mT, it is convenient to use air-core magnets. In our earlier work we built a simple Helmholtz electromagnet similar to that of Halpern et al.36 Recently, we have built a solenoid magnet around a 40 cm diameter shim set from a horizontal-bore superconducting NMR magnet previously used for MRI at 2.3 T.34 This allows operation at up to 400 MHz on samples up to 10 cm in diameter. Conveniently the BO shim is used to produce a uniform modulation field at 10 m. 5.3 Phase noise

Larger samples require higher levels of excitation. This causes problems with phase noise from the frequency source since the signal is detected in the presence of the excitation. Random fluctuations in frequency are discriminated by the bridge and are manifest as amplitude fluctuations in the spectrum. Since our original workz4 we have reduced the level to the practical limit by using an improved signal generator37 and by increasing the modulation frequency to 10 kHz.29 Further increase in power causes thermal effects which make the tuning and matching too unstable.

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5.4 Longitudinally Detected Electron Spin Resonance

This technique, LODESR, circumvents the phase noise problem and has been demonstrated at radiofrequency by Lurie and c o - w o r k e r ~ .The ~ ~ radiofrequency excitation is modulated at a frequency which is less than the linewidth, typically about 200 kHz. Applied at a level of several watts, this takes the electron resonance into and out of saturation at the modulation frequency. A coil tuned at the modulation frequency detects the changes in magnetisation along the z axis. Not only is the phase noise problem eliminated but also there is no need for field modulation. This is a promising technique for food applications, although in the presence of different radicals or radicals in different motional environments, the response may be significantly different on account of differences in saturation characteristics. 5.5 Dynamic Nuclear Polarisation

Enhancement of NMR signals by simultaneous saturation of the electron resonance was the original Overhauser effect.3924oIn particular, a 2 mM solution of '%TMIOD in triethylene glycol dimethyl ether (triglyme) is used in magnetometers in the earth's field and provides enhancement factors of between 1000 and 2000 over a temperature range of 2 5 125 OC.'* Proton-electron double resonance imaging, PEDRI,41combines proton NMR imaging with the sensitivity enhancement arising from irradiation of the electron resonance. In a field of 10 mT conventional proton images would be obtained at about 425 kHz with the electrons being irradiated at radiofrequency. Further sensitivity improvement can be achieved by the use of a field-cycling magnet4' where the proton magnetisation is established at high field, the electron enhancement is carried out at low field followed by proton imaging at high field. 5.6 Multiple-technique Spectrometerhager

A system which combines conventional radiofrequency ESR and EMRI, LODESR and PEDRI has been described recently.43 In our laboratory, LODESR mode would be achieved by installing a solenoid coil tuned to say 425 kHz along the field axis. For PEDRI the proton signals at a similar frequency would be detected by another birdcage coil. The proton resolution would be optimised using the shim set and the gradients would be applied through the X, Y and Z shims or the supplementary gradient coils which provide up to 1 mT cm-'. 5.7 Pulse Radiofrequency EMRI Despite the problem of short electron relaxation times, Sotgiu and c o - w o r k e r ~have ~~~~ reduced the dead time associated with recovery from the pulse excitation to enable a 2-D image to be obtained from lithium phthalocyanine in a 40 cm3 phantom at 220 MHz under physiological conditions. There is a prospect of a dramatic improvement in signal to noise but at the moment the dead time is still too long for practical food samples. However, the reduction is still insufficient for practical food samples.

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6 IMAGING IN FOOD SYSTEMS

1-D imaging of diffusing mobile spin probes or spin-labelled species, performed either as discussed in Section 3 or by DID-ESR (Dynamic Imaging of Diffusion by ESR)46,47or by 2D spectral imaging provides information on transport of materials. Simultaneously the spatial dependence spectrum indicates changes in the local microviscosity and, in suitable cases, changes in the local pH and oxygen concentration. The experiments on gels in capillaries at X-band” exemplify this application. The principle of studying radicals diffusing into plant stems was established many years ago by 1-D and 2-D L-band experimentson celery stems.48 In principle hydration phenomena in large food samples could be followed by the spread of a water-soluble nitroxyl radical. Dehydration would presumably leave at least some radical behind in an immobile state with a broad spectrum which would cause problems for EMRI. The PEDRI technique would require the protons to be in a mobile state and would indicate the spatial distribution of the spin probe. Normal TI-weighted MRI could also indicate this through enhanced relaxation. As discussed in Section 2, radicals tailored to partition into hydrophilic or hydrophobic phases give detailed information on changes in molecular motion as the temperature is varied and we have built a resonator to study large food samples at radiofrequenciesover a temperature range from -100 to +150 OC. In principle the changes taking place during cooking or freezing or thawing can be monitored. Oxygen plays a key role in food systems. The technique of oximetry is well-developed for in vzvo applications and utilises the line broadening effects on nitroxyl radicals in soluti0n,4~or implanted solid particles such as lithium phthalocyanine” or carbohydrate chars.51 The use of spectral-spatial imaging was demonstrated for nitroxyl radicals and techniques for following oxygenation and deoxygenationusing nitrogen were reported.49 In conclusion, there is a range of applicationsfor EMRI in food systems, particularly in association with ESR spectroscopy. At present we are working on dough, ice cream and emulsions. The range of mobilities encountered makes this a challenging area for EMRI.

References B. Hills, ‘MagneticResonance Imaging in Food Science’, Wiley, USA, 1998. ‘EPR Imaging and In Vivo EPR’, eds. G.R. Eaton, S.S. Eaton and K. Ohno, CRC Press, USA, 1991. 3. S.A. Fairhurst, D.G. Gillies and L.H. Sutcliffe, Spectros. World, 1990, 2, 14. 4. Res. Chem. Intermed, 1996,22, No. 6. 5. Phys. Med Biol., 1998,43, No. 7. 6. L.H. Sutcliffe,Phys. Med Biol., 1998,43, 1987. 7. ‘Spin Labeling: Theory and Applications’, ed. L.J. Berliner, Academic Press, New York, 1976. 8. R. Bolton, D.G. Gillies, L.H. Sutcliffe and X. Wu, J. Chem. SOC. Perhn Trans 2, 1993,2049. 9. R. Bolton, L.H. Sutcliffe and X. Wu., J. Labelled Compounds and Radiopharmaceuticals, 1994,34, 663, 1. 2.

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10. D.G. Gillies, L.H. Sutcliffe and X. Wu, J. Chem. Soc. Farachy Trans., 1994, 90, 2345. 1 1 . D.G. Gillies, L.H. Sutcliffe and X. Wu, Food Chemistry, 1996,55,349. 12. D.G. Gillies, L.H. Sutcliffe and M.R. Symms, J. Chem. SOC.Faraahy Trans., 1994, 90,267 1. 13. D.G. Gillies, L.H. Sutcliffe and X. Wu, unpublished results. 14. P.S. Belton, A. Grant, D.G. Gillies, A.I. Smirnov, L.H. Sutcliffe and X. Wu, 1998, 4th International Conference on Applications of Magnetic Resonance to Food Science, Norwich, UK, abstract D13. 15. Scientific Software Services, Bloomington, 305 E Locust, I1 61701, USA. 16. C.A. Beadle, D.G. Gillies, L.H. Sutcliffe and X. Wu, J. Chem. SOC.Farachy Trans., 1995,91, 887. 17. S.A. Fawthrop, D.G. Gillies, L.H. Sutcliffe and M.R. Symms, Mugn. Reson. Chem., 1995,33, S107. 18. E. Belorizky, D.G. Gillies, W. Goreki, K. Lang, F. Noack, C. Roux, J. Struppe, L.H. Sutcliffe, J.P. Travers and X. Wu, J. Phys. Chem. A, 1998,102,3674. 19. R. Damadian, M. Goldsmith and L. Minkoff, Physiol. Chem. Phys., 1977,9,97. 20. P.J. McDonald, Prog. hMR Specfrosc., 1997,30,69. 21. P. Mansfield and P.G. Moms, “MR Imaging in Medicine’, Academic Press, New York, 1982. 22. D.G. Gillies, L.H. Sutcliffe and M.R. Symms, J. Chem. Soc. Farachy Trans., 1994, 90,2671. 23. M. Jkeya and T. Miki, J.Appl. Phys., 1987,26, L929. 24. N.M. Bolas, D.G. Gillies, L.H. Sutcliffe and M.R. Symms, Res. Chem. Intermed, 1996,22, 525. 25. R.A. Brooks and G. Di Chiro, Radiology, 1975,117,561. 26. G.N. Ramachandran and A.V. Lakshminarayanan,Proc. Nut. Acad Sci. USA, 1971, 68,2236. 27. R.K. Woods, G.C. Bacic, P.C. Lauterbur and H.M. Swartz, J. M a p . Reson., 1989, 84,247. 28. P. Kuppusamy, P. Wang and J. Zweier,Magn. Reson. M e d , 1995,34,99. 29. M.M. Maltempo, J. M a p . Reson., 1986,69,82. 30. M.M. Maltempo, S.S. Eaton and G.R. Eaton, J. Magn. Reson., 1986, 72,77. 3 1 . M.M. Maltempo, S.S. Eaton and G.R. Eaton, . I M a p . Reson., 1988, 77, 75 32. J.L. Zweier, M. Chzhan, P. Wang and P. Kuppusamy, Res, Chem. Intermed, 1996, 22,615. 33. W. Froncisz and J.S. Hyde, J. Magn. Reson., 1982,62,79 34. D.G. Gillies, to be published. 35. J.A.B. Lohman, M.A. Allan, W.A. Miller, A.J. Illsley and R. Ladbury, 1997, 39th Rocky Mountain Conference on Analytical Chemisriy, Denver, USA, abstract 120. 36. H.J. Halpern, D.P. Spencer, J. van Polen, M.K. Bowman, A,C. Nelson, E.M. Dowey, and B.A. Teicher, Rev. Sci. Instrum., 1989,60, 1040. 37. Marconi Type 2041, MarcoN Instruments Ltd., Stevenage, UK. 38. I. Nicholson, F.J.L. Robb and D.J. Lurie, J. Map. Reson., 1994, B104,284 39. A.W. Overhauser, Phys. Rev., 1953,92,411. 40. A. Abragam, Phys. Rev., 1955,98, 1729. 41. D.J. Lurie, D.M. Bussell, L.H. Bell and J.R. Mallard, J.Magn. Reson., 1988, 76, 366.

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42. D.J. Lurie, M.A. Foster, D. Yeung and J.H.S. Hutchison, Phys. Med. Biol., 1998, 43, 1877. 43. S.J. McCallum, I. Nicholson and D.J. Lurie, Phys. Med. Biol., 1998, 43, 1857. 44. G. Placidi, J.A. Brivati, M. Alecci and A. Sotgiu, Phys. Med Biol., 1998,43, 1845 45. M. Alecci, J.A. Brivati, G. Placidi and A. Sotgiu, J. M a p . Reson., 1998, 130,272. 46. J.K. Moscicki, Y-K. Shin and J.H. Freed, J. M a p . Reson., 1989,84,554. 47. J.K. Moscicki, Y-K. Shin and J.H. Freed, ‘EPR Imaging and In Vzvo EPR’, eds. G.R. Eaton, S.S.Eaton and K. Ohno, CRC Press, USA, 1991, Chapter 19. 48. L.J. Berliner and H. Fujii, Science, 1985, 227, 1985. 49. H.M. Swartz and J.F. Glockner, ‘EPR Imaging and In Vzvo EPR, eds. G.R. Eaton, S.S.Eaton and K. Ohno, CRC Press, USA, 1991, Chapter 24. 50. A.I. Smirnov, S-W. Norby, T. Walczak, K.J. Liu and H.M. Swartz, J. M a p . Reson., 1994, B103,95 51. R.B. Clarkson, B.M. Odintsov, P.J. Ceroke, J.H. Ardenkjaer-Larsen, M. Fruianu and R.L. Belford, Phys. Med. Biol., 1998,43, 1907.

Signal Treatment and Analysis in Magnetic Resonance

Analysis of Time Domain NMR and Other Signals D. N. Rutledge, A. S. Barros, M. C. Vackier, S. Baumberger and C. Lapierre INSTITUT NATIONAL AGRONOMIQUE, 16, RUE CLAUDE BERNARD, 75005 PARIS,FRANCE

1 INTRODUCTION

has been Until recently, Time Domain - Nuclear hhgnetic Resonance (TD-NMR) used almost exclusively to quantify major constituents in agro-food and petrochemical products or to monitor their evolution during processing. In this context the technique is often referred to as “Low Resolution NMR”. This situation has changed with the advent of more sophisticated instruments that can be used to perform NMR experiments previously only possible on much more expensive, high-field spectrometers. Time Domain - N M R is therefore now being used both for quality control in industry and for research purposes. In TD-NMR, unlike other instrumental techniques such as I&md spectroscopy, it is possible to generate a wide range of responses by using different sequences of radio fkquency pulses to excite the protons in the sample. The resulting relaxation curves may vary as a fimction of the physicochemical properties of the product. This apparently unlimited number of possible signals is both an advantage and disadvantage for TDN M R : on the one hand, it increases the range of potential applications of the technique, on the other, it complicates the development of new analyticalprocedures. Chemometric techniques, such as Analysis of Variance (ANOVA) and Partial Least Squares Regression (PLS), will be shown to be very u s e l l way of getting around this problem inprder to determine whether a particular TD-NMRor other signal contains any relevant information and to then extract and use that information. Techniques for the simultaneousanalysis of several signals will also be presented. Chemomtrics has already been applied to TD-NMR signals by Davenel et ul. who used Principal Components Analysis to study the relaxation curves of doughs during cooking, by Gerbanowski et al. applying PLS and Multiple Linear Regression (MLR) to relaxation curves and calculated relaxation parameters (TI, TZ and initial signal amplitudes) and Airmu et ul. using Principal Components Analysis and Evolving Factor Analysis to study the influence of a complexation reaction on relaxation curves and calculated relaxation parameters. Vackier el ul. 4, applied ANOVA, MLR and PLS to both relaxation curves and calculated relaxation parameters of gelatines while Clayden et ul. applied Factor Analysis to Time Domain-NMR FID signals of PTFE samples with different crystalliuities.

’

1.1 Chemometric Techniques Applied

As Analysis of Variance (ANOVA) is a univariate statistical technique it can be very rapid. It is therefore prekrable in order to have a quick indication of the information

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content of the signals. If the signal is shown to be interesting, multivariate techniques such as PCA or PLS regression may then be applied. I.1.1. ANOVA. If the samples can be classed into groups, it is possible to calculate the part of the variability of a measurement due to differences between these groups and compare it with the variability within the groups. For each measurement one calculates the Between-Group or Group Variance (VG) and Within-Group or Residual Variance (VR):

’

J=I

with : g = number of groups n = number of samples in group j = value for sample i in group j 7 - = mean value for group j 7 = grand mean value N = total number of samples J

J

When the measurements are in fact points in a signal, such as a TD-NMR relaxation curve, where the information content of successive points is strongly correlated, it is interesting to plot these variance values as a function of the position in the signal. Regions that vary systematically fiom one group to another will give high VG values. If there are no other important differencesbetween the samples, then VR will be low and will not have a structured distribution as a function of position in the signal. In this way one can not only determine whether a signal is interesting and whether all the significant sources of variability have been taken into account, but also highlight those parts of the signal which are most important. 1.1.2. Partial Least Squares Regression. PLS regression may be used to generate predictive regression models. PLS is a multivariate, least squares regression procedure where a reduced set of non-correlated, linear combinations, T, of the original independent variables, X, are regressed onto the dependent variable, Y. PLS differs fiom Principal Components Regression in that the T are not simply the Principal Components, but are calculated iteratively, maximising their covariance with Y. This predictive regression model obtained is ofthe form : Y=X*B+& (3)

*

where B, the vector of B-coefficients, is calculated fiom the loadings of the X variables on the T vectors. 1.1.3. The Durbin-Watson Statistic. The Durbin-Watson D statistic is classically used as a measure of the randomness of residuals after a regression. We propose to use it

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as a measure of the structure, non-randomness or information content of the loadings vectors and B-coefficients vectors produced by the PLS regressions. In this way, the Durbin-Watson statistic can be used to characterise the “signal f noise” ratio and thus indicate to determine the optimal number of Factors to use in the regression model. This statistic is given by:

D=

c” c

(6x,

- %I

l2

1=2

(6Xi *6 X i )

i=2

(4) . .

where “ i and “ i - 1 are the residuals for successive points in a series. For n>lOO, the distribution is random with a 95% confidence interval for D between 1.7 and 2.3. 1.1.4. Outer Product Analysis. It is often interesting to compare the simultaneous variations in two types of signals, such as spectra, relaxation curves or chromatograms, as a function of the evolution of some particular property of a set of samples. Several procedures have been proposed to highlight the covariations in two sets of si nals. Barton er al. lo used Ordinary Least Squaresto correlate variations in signals. Noda developed a means to detect correlated and anti-correlated vibrations in Infrared spectra as a fbnction of an imposed perturbation using 2D-Correlation Spectra. Devaux et al. l2 applied Canonical Correlation Analysis to highlight similar evolutions in Near and Mid-Inhred spectra of edible oils as a h c t i o n of their degree of unsaturation. Barros et al. l 3 demonstrated the utility of applying ANOVA to the Outer Product matrices of these same sets of I n h e d spectral vectors to detect correlated variations in the spectra and thereby attribute particular combinations of Mid-I&ared vibrations to given features in the Near I n h e d spectra. This idea has since been generalised as Outer Product Analysis (OPA) by applying other statistical techniques such as Principal Components Analysis, PLS Regression and Factorial CorrespondenceAnalysis. l4

’k

1.2 Samples Studied

1.2.1. “Light”and “Traditional”Butters and Margarines. The moisture contents of 12 “light” and “traditionalyy butters and margarines were determined in triplicate by the Karl-Fischer method. The values ranged fkom 12 to 60%. lOmm outer-diameter Nh4R tubes were filled up to a height of 1Omm. The tubes were thermostated at 20°C before transfer to the N M R apparatus. The measurements were performed at 20°C on a 20 M H z TD-NMR apparatus (QP20+, OXFORD INSTRUMENTS) with phase quadrature detection. A set of relaxation curves was acquired by inserting a Carr-Purcell-MeiboomGill (CPMG) sequence into an Inversion-Recovery(I-R) sequence :

I-R = [180° -- 2 *(1.55) i-l -- CPMG -- RD} N CPMG = 90” -- T -- [{ 180”, -- 2r -- } 3 -- 18OoY-- T -- measure] M withN = 20, i=l to N, T = lms, M = 100, RD = 3s, Scans= 4 The resulting signals may be represented either folded as 2-Dimensional relaxation surfaces (Figure 1) or unfolded as a series of I-R weighted CPMG curves (Figure 2). As can be clearly seen in Figure 2, there are two Ti and T2 components, and the less mobile component at the beginning of the CPMG curves (short T2) having a shorter TI, as it relaxes more quickly in the I-R curve.

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-2500 5000

0

0

Figure 1. A rypical2D-Relaxation surface for a traditional butter.

moo 6000

a r

4000

5

2000

P eE

o

e a

-2000 4000 4000

NMR Data Point

Figure 2. A typical series of I-R weighted CPMG curvesfor a traditional butter, Figures 3 and 4 present the 2D VGand VR surfaces of these signals calculated with the grouping “Light’T’Traditional”, reflecting the moisture contents of the samples. Figure 3 shows that the greatest differences are in the middle of the I-R axis and at the beginning of the CPMG axis. This indicates that the TI of the samples changes with moisture content, shifting the null-point of the I-R curve and that the T2 or the quantity of the fast relaxing component is modified. On the other hand, Figure 4 shows that there is a significant pmportion of structured variability in the signal intensities which is not explained by the “Light”/“Traditional” grouping, especially towards the end of the CPMG signals. A detailed analysis of the CPMG relaxation curves using CONTIN and

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MARQT Is showed that this difference is due to the longer relaxation times of the aqueous phase in the margarine samples.

0

0

Figure 3. 2 0 VGplotfiom ANOVA on I-R weighted CPMG curves, based on groups “Light’’/ “Traditional”,

0 0

Figure 4. 2 0 VRplot from ANOVA on I-R weighted CPMG curves based on groups “Light”/ “Traditional”,

While it is possible with the QP20+ to program complex pulse sequences in order to acquire 2D relaxation surfaces, many commonly used TD-NMRinstnUnents do not have this capability. In such cases it is possible to create artificial 2D relaxation surfaces by calculating the Outer Product of independently acquired I-R and CPMG curves, as shown in Figure 5.

Figure 5. Unfolded Outer Product matrix for a traditional butter. Prior to calculation, the CPMG curves were range scaledfrom 0 to 1

It is clear that these OP matrices do not contain all the information to be found in the true 2D signals. However they can be used to detect some of the simultaneous variations in the 2 signals. Figures 6 and 7 present the VC and VR surfaces of these OP matrices based on the grouping “Light”PITraditiona1”. The similarity between these s u r h e s &om the OP ANOVA and those obtained using the true 2D relaxation surfaces is evident, in particular at the beginning along the CPMG axis.

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0 0

Figure 6. 2 0 VGplot of matricesfrom Outer Product of I-R and CPMG curves.

Figure 7. 2 0 VRplot of matricesfrom Outer Product of I-R and CPMG curves.

Having demonstrated that these two types of signal (2D relaxation surfaces and OP surfaces) contain significant information, PLS regressions were performed on them both to create predictive models for the moisture content of the samples. The evolution, as a function of the number of Factors included in the models, of the Y-Variances and the Durbin-Watson values of the X-Loadings and B-Coefficients vectors (see below) was used to determine that 3 was the optimal number of Factors in both cases to limit over-fitting. Figures 8 and 9 show the B-coefficients surfaces for the two models The similarity between these surfaces is again evident. Figures 10 and 11 are the regression lines obtained using the two models. It is clear that there is no significant difference between them.

0 0

0

Figure 8. 2 0 B-Coeflcients plotfiom PLS on 2 0 I-R / CPMG surfaces.

.. *.

..

0

Figure 9. 2 0 B-Coeflcients plot from PLS on OP matrix of I-R and CPMG curves.

- .. I

e;

o

Figure 10. Predicted vs. observed moisture from PLS on 2 0 I-R / CPMG surfaces. RZ=O.996, MSEC=8.22%

10

20

ao

40

LO

00

70

Figure 11.Predicted vs. observed moisture from PLS on OP matrices of I-R and CPMG curves. R2=0.974,RMSEC=7.20%

1.2.2. Plant and Vegetable Oils. The Iodine Number (IN) of 20 plant and animal oils of pharmaceutical quality (SociktC Industrielle des Oleagineux, Coopkration

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Yharmaceutique Franqaise) were either taken directly ffom the accompanying Analytical Bulletins or calculated fiom the given fatty acid compositions. The IN values, which reflect the degree of unsaturation of the oils, ranged fiom 70 to 150. For each oil, 3 NMR tubes (10od) were filled to 1Omm. The samples were thermostated and measured at 20°C in the QP2W instrument. Several different types of pulse sequences were tested but the Analysis of Variance on the relaxation curves showed that only the TI curves contained information on the Iodine Number. This result confirms the correlation between IN and TI observed by Brosio et al. l6 and El Khaloui et al. " The following InversionRecovery sequence was used :

'-'

I-R = [180" -- 2 *(1.36) -- measure -- RD} N withN=30,i=l toN,RD=5s, Scans=4 Fourier Transform-Mared spectra were acquired fiom 345Ocm-I to 550cm-'for the same oils on an FTS60 spectrometer (BioRad) using a ZnSe single-reflectionHATR cell at room temperature, with 128 scans and a resolution of 16cm-'. The Analysis of Variance on the spectra gave a clearly structured VG plot with, as expected, particular vibrations strongly influenced by the Iodine Number (results not shown). For each sample, the Outer Product matrix was calculated between the I-R relaxation curve and FT-IR spectrum. PLS regressions with fiom 1 to 10 Factors were performed between the X-matrix of unfolded OP matrices and the Y -matrix of IN values. The evolution of the X- and Y-Variances, and the Durbin-Watson @) values of the XLoadings and B-Coefficientsare presented in Figure 13. 1.5

I

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Number of Factors in PLS regression model

Figure 13. X-Variances (*), Y-Variances (0)and the D values of the X-Loadings (+) and B-Coeficients M, as afunction of number of Factors used in the PLS regression between the I-R /FT-IR Outer Product matrices and the Iodine Numbers. The sudden increase in the D values between 3 and 4 Factors indicates a significant increase in the randomness in the X-Loadings and B-Coefficients vectors due to the inclusion of noise to improve the adjustment of the PLS regression model. This corresponds to the point where the X-Variance abruptly drops below 10%. If the signals only contained noise and it were equally distributed among the 10 Factors, their X-

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Variances would all be equal to 10%. Therefore any Factors with X-Variances greater than this value may be assumed to contain information. The D-values and Variances both indicate that a 3 Factor PLS model is optimal. Figure 14 shows the B-Coefficients surhce for the 3 Factor PLS regression between the I-R/FT-IR OP matrices and the Iodine Number while Figure 15 shows 2 profiles through the B-Coefficients surface, parallel to the FT-IRaxis.

Figure 14. B-Coeflcients sur$ace for the 3 Factor PLS regression between the I-WFT-IR Outer Product matrices and the Iodine Numbers.

0.6 -

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1600

1000

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Figure 15. Profiles through the B-Coeflcients surface in Figure 14, parallel to the FT-IR mcis at 109 ms (-- 4 and 14.9 s (A on the I-R axis.

Although in this case the use of the OP matrix did not signiftcantly improve the predictive ability of the PLS regression model compared to that of the FT-IR spectra

Signal Treatment and Analysis in Magnetic Resonance

21 1

alone, it does have the advantage of introducing another dimension along which the signals can be resolved. As both Brosio and El Khaloui have shown, TI increases with the Iodine Number. Therefore oils with low IN values (fewer unsaturations) contribute more to the earlier part of the I-R curve while those with high IN values (more unsaturations) contribute more to the later part of the I-R curve. The vibrations in the Inhued spectra corresponding to different degrees of unsaturations are then resolved along this axis in the B-Coefficients surfice. 1.1.3. Starch-Lignin Mixtures. Starch was pre-extruded in presence of water before incorporation of lignin at 5 concentrations : 0, 5, 10, 15 and 30%. The starch and lignin powder were mixed and then extruded at 12OOC in presence of water to produce canes. Films were also produced by compressing the canes at 140°C and 250 bar for 10 minutes. Although it has not yet been verified by microscopy, this compression most probably results in a modification in the crystallinity of the samples. In order to have information about the behaviour of water in the samples, they were equilibrated for 10 days at 2loC, at 2 different Relative Humidities (33%, above a saturated solution of MgClz and 75%, above a saturated solution of NaCl). An adjustment to the G.A.B. sorption curves gave average moisture contents of 3.5% (MgC12)and10% (NaCl) for the mixtures. TD-NMR measurements were done, in triplicate and at 2OoC, on the 20 starch lignin mixtures using the QP2O-t. It was not known which, if any, of the almost unlimited number of possible pulse sequences would produce an informative signal concerning the moisture content, lignin content and form (cane or film)of these samples. Therefore, several different sequences were applied and the resulting signals analysed statistically. Because of the very low moisture content, the CPMG sequence could not be used and so a simple FID was used to characterise the proportions of "solid" and "liquid" protons and their apparent transverse relaxations (Tz*).Normal and Inverse Goldman-Shen (GS) sequences were used to see if there was any cross-relaxation (CR) between these two phases and, if so, whether it varied with the composition of the samples. An Inversion-Recovery sequence was used to observe the longitudinal (TI) relaxation while a Multiple Pulse - Spin Locking sequence was used to observe the rotating h e longitudinal (TI& relaxation. The instrumental parameters of these sequences are given below :

-

Free Induction Decay (Tz *): 90°, [r - measure1 - RD solid + liquid components : 100 points; fiom 11ps to 3 1ps liquid component : 100 points; fiom 3 1ps to 23 1ps signal averaged to 2*20 points RD=2s Normal and Inverse Goldman-Shen (CR): {9OoX- t, - 90°,, - rvar- 90°,, - t , - measure1 - t, - measure2 - RD), N=30 war = 0.001*(1.74) ms; for i = 1 to N t l = 30ps; t2 = 12 ps (solid + liquid); t3 = 69 ps (liquid) each point acquired is the average of 5 with dwell of 0.2 ps RD = 2s

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Inversion-Recovery (TI): (180°, - rvar- 90°, - t2 - measure - RD}, N=20 war = 0.5*(1.55) i-' ms; for i = 1 to N t2 = 11 ps (solid + liquid) RD = 2s Multiple Pulse Spin Locking (TI&: 90°, - T - {(9OoY- 211, - 9OoY - 2.5 - 9ODy- T - measure - T) - RD N

N = 80; M = 2 T=

10 ps (solid + liquid)

RD = 2s averaged to 40 points The GS liquid signals were subtracted fiom the corresponding GS solid+liquid signals to clearly isolate the effects of any cross-relaxation and longitudinal relaxation on the two compartments. The 8 signals for each sample were then concatenated, range scaled fiom 0 to 1 and centred. The shape of the normal and inverse GS solid curves in the typical vector of concatenated TD-NMR signals shown in Figure 16 indicate that this sample does not present any cross-relaxation- the evolution of the curves would simply be the result of longitudinal relaxation. Figure 17 shows the complete matrix of concatenated signals for 58 samples (2 signals eliminated as outliers). Here it is clear to the eye that the signals in the data matrix contain information on the moisture level, the form of the samples and the lignin level (not shown).

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This somewhat subjective appreciation is confirmed by ANOVA. In Figure 18, the VG and VR plots for the 3 ANOVAS, based on moisture, lignin and form show clearly that :1) the moisture level influences the liquid part of the FID, the liquid parts of the inverse GS, the end of the I-R, and the start and end of the MP-SL; 2) the lignin level influences the middle of the I-R, and the middle the GS solid signals; 3) the form has a very strong effect on the ends of the GS solid signals and the inverse GS liquid signal. The complementarity of the VRand VGplots makes it clear that these three factors are the only significant sources of variability in the signals.

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PLS regressions with from 1 to 10 Factors were performed between the X-matrix of concatenated TD-NMR signals and the Y-matrices of moisture and lignin contents, and form as binary values. The X- and Y-Variances and the D values of the X-Loadings and B-Coefficients are presented in Figure 19. The evolution of these curves shows that the optimal number of Factors to include in the three models, in order to limit the overfitting, are respectively 3, 3 and 2. Figure 20 presents the B-Coefficients for the three models. These curves are very similar to the VG plots in Figure 17, but include the sign of the contribution of the signals to the moisture, lignin or form values.

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0

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Figure 19. X- Variances (4, Y-Variances (0)and the D values of the X-Loadings (0) and B-Coeflcients (4 as a function of number of Factors used in the PLS regression between the concatenated signals and moisture (lefl), lignin (centre) andform (right). For moisture, there is a positive contribution of the liquid part of the FID, a negative contributionof the normal and inverse solid GS and a positive contribution of the inverse liquid GS. The positive contribution of the middle of the I-R curve indicates a decrease in the TI of the samples with increase in moisture content. This decrease in TI has been confirmed by multiexponential decomposition of the I-R curves. For lignin, there is no contribution fiom any of the liquid signals. Adding lignin does not modify the state of the water in the samples. There is however an increase in the solid FID signal and a positive contributions in the middle of the normal and inverse solid GS and fiom the middle of the I-R curve. The addition of lignin to the samples only slightly increases the solid FID signal. The positive contribution of the middle of the solid GS and I-R curves indicates a decrease in the TIof the samples without a signifkant modification in the proportion of solid content. This decrease in TI has been also confirmed by multiexponential decomposition of the I-R curves. 0.04

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The situation for the form of the samples is more complex. The change fiom cane to film is associated with a negative contribution fiom both the normal and inverse solid GS and the normal and inverse liquid GS. Similarly, there is a slight increase in both the solid and liquid FID intensities. This evolution is not easy to explain but may be due to a modification in the relaxation properties of both the solid and liquid components of the sampIes. This could explain the modification in the shape of the solid FID signal from lorentzian to more gaussian. Multiexponential decomposition of the 1-R curves shows a slight decrease in TI but only for the highest lignin contents. The observed and predicted values for the 3 PLS regression models are presented in Figure 21, along with the associated statistics. ln 0

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Figure 21. Observed andpredicted values for moisture (lefi), lignin (centre) andform (righr). The statistics of the PLS regression models are moisture :RMSEC = 7.66%, R2 = 0.970 (3Factors); lignin :RMSEC = 13.93%, R2 = 0.954 (3Factors);form : RMSEC = 33.63%, R2 = 0.766 (2Factors). 1.3 Conclusions These results confim the interest of applying chemometric techniques directly to

TD-NMRsignals as a means of detecting information and to have an indication of the changes taking place. However, a complete understanding of the relaxation phenomena occurring in the samples still requires the calculation of relaxation parameters such as TI, ‘Tz and the cross relaxation rate. The statistical analysis of Outer Product matrices (Outer Product Analysis - OPA) can in many cases be useful to facilitate the comparison of variations occurring simultaneously in two sets of signals and to artificially increase the resolution of the signals. References 1. A. Davenel, P. Marchal, J.P. Guillement, “Rapid coolung control of cakes by low resolution NMR” in : Magnetic resonance infood science, P.S. Belton, I. Delgadillo, A.M. Gil, G.A Webb (eds), Royal Society of Chemistry, Cambridge, 1995, p.146.

2. A. Gerbanowski,D.N. Rutledge, M. Feinberg, C. Ducauze, Science des Aliments, 1997, 17,309. 3. C Airiau, F. Gaudard, A.S. Barros, D.N.Rutledge, Analusis, 1998,26,66.

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4. M.C. Vackier and D.N. Rutledge, J. of Mag. Res. Anal., 1996,2(4) 321

5. M.C. Vackier and D.N. Rutledge, J of Mag. Res. Anal., 1996,2(4) 31 1. 6. N.J. Clayden, R.J. Lehnert, S. Turnock, Analytica Chimica Acta, 1997,344,261. 7. J. Czerminski, A. Iwasiewicz, Z. Paszk and A. Sikorski, Statistical Methods in Applied Chemisw, Elsevier, Amsterdam 1990, p. 186.

8. A. Hoskuldson, J. Chemometrics, 1988,211. 9. J. Durbin and .G.S. Watson, Biometriku, 1950,37,409. 10. F.E. Barton, D.S. Himmerlsbach, J.H. Duckworth, M.J. Smith, Appl. Spectrosc., 1992, 46,420.

1 1 . I. Noda, Appl. Spectrosc., 1993,47,1329 12. M.F.Devaux, P.Robert, A.Qannari, M.Safar, E.Vigneau, Appl. Spectrosc., 1993,47 , 1024. 13. A S . Barros, M. Safar,M.F. Devaux, D. Bertrand, D.N. Rutledge, Appl. Spectrosc., 1997,51(9), 1384. 14. N. Gouti, M-F Devaux, B. Novales, D.N. Rutledge, M. Feinberg, Analusis, 1998, (in press) 15. D.N.Rutledge and A.S. Barros, The Analyst, 1998,123,55 1. 16. E. Brosio, F. Conti, A. Dinola, S. Sykora, J. Fd. Technol., 1981, 16,67. 17. M. El Khaloui D.N.Rutledge, C.J.Ducauze, J. Sci. FoodAgri., 1990,53,389.

Comparative Chemometric Analysis of Transverse Low-field 'H NMR Relaxation Data Iben Ellegaard Bechmann, Henrik Toft Pedersen, Lars Nflrgaard and Sflren Balling Engelsen* FOOD TECHNOLOGY, THE ROYAL VETERINARY AND AGRICULTURAL UNIVERSITY, ROLIGHEDSVEJ 30, DK-1958 FREDERIKSBERG C, DENMARK

Corresponding author 1 ABSTRACT

Transverse relaxation data from low-field (23.2 MHz) 'H N M R was investigated by different data analytical approaches. The quantitative information in the relaxation data of 200 intact salmon samples with respect to overall water and fat content was evaluated by four different data analytical methods: (a) bi-exponential curve fitting followed by linear regression, (b) forward selection of variables followed by multiple linear regression (FSMLR), (c) partial least squares regression (PLS) and (d) non-negative alternating least squares regression (NN-ALSR). The investigation demonstrates that the quantitative prediction performance is significantly enhanced (reduction of the prediction error from 14% to 34%) by the use of multivariate chemometric procedures such as PLS. While PLS is an extraordinarily robust and efficient algorithm its strictly orthogonal latent variables suffer from a difficult qualitative interpretation. NN-ALSR and FS-MLR which exhibit a quantitative performance comparable to PLS do not suffer from this problem, but are more unstable and ineffective data analytical techniques. 2 INTRODUCTION

Fat and water are very important quality parameters for fish flesh, not only because of the nutritional importance of fish fat as a source of unsaturated fatty acids, but also because they influence most of the functional properties of the product. Increasing demands for quality, assurance and product quality documentation in the seafood industry have led to the need for rapid, simple, inexpensive and objective analytical methods for assessing seafood quality. The use of near infrared spectroscopy' has been proposed as a rapid method for assessing seafood quality but low-field pulsed 'H NMR provides a costeffective alternative method which is rapid, direct, volume-based, non-invasive and may be non-destructive. It has been shown that transverse water proton relaxation from lowfield NMR can be used to detect changes in fish muscles during frozen storage or processing*. Recently, we have demonstrated the great potential of using low-field NMR in combination with chemometrics as a rapid analytical technique for the determination of water, fat and water-holding capacity in intact fish flesh3. The application of chemometric data analysis in LF-NMR is only sparsely treated in the literature. Data structures produced by N M R and analytical chemical systems in

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general can be divided into classes reflecting the complexity of the data, ranging from scalars, vectors, and matrices to higher order data structures. More than 90% of the papers published in analytical chemical journals are based on zero-order data structures4 (one datum per sample). The use of zero-order data requires that the measured signal is a known function of the property of interest and e.g. univariate linear regression is based on zero-order data. An improvement of the model can often be achieved by using first-order data structures4 which gives the possibility for detecting outlying samples. The relaxation data presented here are typical first-order data where each sample gives rise to a vector of intensities recorded at the same time-points and a set of calibration samples yields a matrix. LF-NMR papers published on first-order data using two-way data analytical methods, for instance PLS regression, have only recently begun to emerge5x6. The objective of this work was to investigate and compare the quantitative performance of four different data analytical strategies to low-field relaxation data. The reference point of the comparison will be the quantitative performance of onedimensional analysis assuming exponential decay functions. Of the other three methods two will use latent variables and one will use variables in original variable space. In two respects low-field NMR relaxation data is extreme: the co-linearity of the data is very high and the exponential type of decay functions are extremely non-linear. While the latter has impact on the convergence properties of curve fitting algorithms, the high colinearity can be considered as a challenge for the multivariate chemometric algorithms which are usually quite robust in handling highly co-linear data. Table 1 lists a comparison of the co-linearity of low-field NMR data ( 1 00 samples and 5 12 echoes) with typical NIR data spectra7 (98 pectin samples and 525 wavelengths) usually considered being highly co-linear. Table 1 Co-linearity of Low-field Relaxation Data Given in Percent Method of NTC t R<0.5 0.5
0.99
3 EXPERIMENTAL

A 5 kg salmon (after gutting) was purchased and cut into 1 1 slices of 4 cm each, yielding a total number of 200 cylindrical stamped out fish samples. One long side of the salmon (100 samples) was submitted to NMR analysis and used for water content determinations and the other half submitted to NMR analysis and used for fat content determinations'. Water content was determined by drying in an oven at 105°C overnight and fat content was determined by chloroform extraction*. NMR measurements were performed on a 23.2 MHz Maran benchtop pulsed 'H NMR analyzer (Resonance Instruments, UK) equipped with an 18 mm variable temperature probe. The temperature was maintained by a continuous flow of dried air and the samples were allowed to temperature equilibrate for 30 minutes prior to analysis. Optimal NMR measurement arameters including inter-pulse spacing (z) in the applied Carr-PurcellMeiboom-Gil&' (CPMG) pulse sequence and the temperature of analysis was determined in an initial experiment3. The CPMG pulse experiment was carried out using

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an inter-pulse spacing of 500 ps, a receiver delay of 6 seconds, 8 scan accumulations, a dwell of 0.5 ps and at a temperature of 25OC. 512 even numbered echoes were sampled.

4 THEORY 4.1 Exponential Fitting Transverse relaxation, Tz, was fitted by bi- and tri-exponential analysis according to the equation:

M(t) = Ci Mo,i exp [ -t/T2,i 3 + B where Mo,i is the amplitude of the i'th exponential and Tz,, is the characteristic transverse relaxation time constant for the i'th exponential (see Figure 1). To facilitate automatic generation of multiple curve fittings this was performed using in-house software written in Matlab (The Mathworks Inc., MA, USA). The curve fitting of the relaxation data was carried out using a Simplex algorithm" for the non-linear characteristic relaxation time constants, T2,,, combined with a least squares fit of the linear amplitude parameters, Mo,i, inside the function evaluation call. This simple approach proved to exhibit extremely robust convergence behavior and to be relatively fast.

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centered CPMG data set. The variance explained by the first two PC’s is approx. 100 % (99% + 1%). The mean relaxation, first and second loadings are common to all the samples, while the scores are different for each sample. The number in front of the loadings are the corresponding score-values for each sample.

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4.2 Chemometrics

The data obtained from the experiments were analysed using multivariate chemometric methods. Most of the latent variables methods used in chemometric data analysis are in one way or another based on principal component analysis (PCA)l2. The essence of the principal component methods lies in their construction of latent factors (or principal components) from underlying latent structures in the original data. Mathematically, principa1 components are linear combinations of variables that have special properties in terms of variance. In PCA the two-way data matrix is decomposed into systematic variation and noise, as illustrated in Figure 2. The systematic variation is described by the principal components (PC1, PC2 etc.) which each represent the outer product of scores and loadings. The scores are related to the samples whereas the loadings are related to the variables. PCA is a method for extracting the systematic variations in a single data set represented as a matrix (X). The standard multivariate calibration approach is to use principal component regression (PCR) or partial least squares (PLS) regression, where the independent variables are decomposed into a set of scores. The dependent variables are then regressed on these scores instead of the original variables. The model structures of PCR and PLS are the same bi-linear structure as in PCA. The general purpose of PLS is multivariate calibration, i.e. to find a mathematical relation between two data sets, X and Y. PLS performs a simultaneous decomposition of the X and Y matrices in such a way that the information in the Y matrix is directly used as a guide for the decomposition of X, and then performs a regression on Y. Alternating least squares regression provides a simple repetitive solution to two regression problems. Given a few profiles (S), the amounts (C) required to reconstruct the original data (X) are calculated by the least squares solution: C = XS S S Then an )-:. -1 improved estimate of the profiles (S) is obtained by calculating: S = X C(C C) from which an improved estimate of the amount can be calculated and so forth. By continuing this iterative approach until no further improvement is observed, a best fit in the least squares sense is obtained to the original data matrix (X). In its native form ALSR does not enforce any constraints to the latent variables for which reason the solution in a pure ALSR approach is often sensitive to the initial guess of hidden profiles. A significant stabilisation of the ALSR solutions can often in practice be dealt with by adding constraints. In this study we use the non-negativity constraint implemented in the least squares with the sacrifice of a significant decrease in convergence speed. Forward selection of variables is a pragmatic method in which subsequent variables are selected stepwise according to their capability to improve a multiple linear regression (MLR) model. In the first step the variables (time-points) are tested in univariate regression models against a reference variable. As these models are cross-validated, the variable with the lowest prediction error is chosen. Next, all two variable MLR-models are investigated based on the chosen variable in combination with all the remaining variables (one-by-one). All these models are also cross-validated and the variable that (in combination with the first chosen variable) gives the lowest prediction error is chosen. This procedure is continued until the prediction error increases by the introduction of a new variable or until a predetermined number of variables has been chosen. The validation is performed as full cross-validation that leaves out one sample at a time from the calibration set and uses the rest for establishment of the calibration model15. Throughout this work the parameter root mean square error of cross-validation (RMSECV) is used as an indicator for the overall prediction ability of the model. RMSECV is defined by:

GT

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where jiis the predicted value of the y variable for sample i, y I is the actual value of the y variable for sample i , and Z is the number of samples.

5 RESULTS AND DISCUSSION An overview of the chemical composition of the 200 salmon samples (100 from each

long side used for water and fat determinations, respectively) is shown in Table 2. Table 2 Overview of Chemical Composition of the Salmon Analysed (in % w/w) Standard Constituent Number of samples Minimum Maximum Mean Deviation Fat Water

100 100

1.98 39.14

41.52 77.04

17.78 60.90

9.94 8.20

A calculated mean of characteristic relaxation times, Tz, for the bi-exponential fit based on all 200 salmon samples resulted in the following two values3: T2,l = 49 ms and T2.2 = 252 ms. The corresponding amplitudes (which provided the best correlations to the reference measurements) were used for univariate modelling and the results of the regressions models are listed in Table 3. The prediction error of the regression models was calculated to 1.10% and 2.55% for water (see Figure 3) and fat, respectively. The best model to water was performed on the amplitude of the slow- relaxing component and evidently the CPMG pulse sequence investigated is most sensitive to water. In fish flesh, water and fat content usually adds up to 80%16which is also reflected quite well by the mean values calculated in Table 2. For this reason it is quite possible that the fat prediction is based on an indirect negative correlation to water and indeed the intercorrelation between fat and water is calculated to be R = -0.94. Nevertheless, this correlation is slightly improved in the regression model based on the amplitude of the fast-relaxing component. In addition to bi-exponential fitting, tri-exponential fitting was also examined and the result was basically a split-up of the slow-relaxing component and an even faster fast-relaxing component3. It appeared, however, that there was no significant improvement in the prediction ability.

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Figure 3. Water content prediction of 100 salmon samples from a regression model based on the CPMG (z = 500 p )pulse sequence at 25°C. (a) PLS predictions based on the collected 512 echo-points in the relaxation curves and (b) univariate linear regression predictions based on the M ~ ,amplitude J from a bi-exponential curvefitting.

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Table 3. Regression overview. Performances of quantitative regression models are compared through the prediction error, RMSECV. and the correlation coefficient, R. Method Xvalues vvalue Factor(s) RMSECV R 1 2.55 0.96 Fat M0,2 Water 1 1.59 0.98 Exponential fit Mo,i Water 1 1.10 0.99 Mo,it 2 1.99 0.98 Fat Echo-points Water 2 1.39 0.99 FS Echo-points Echo-pointst Water 2 0.79 1.oo CPMG Fat 2 2.20 0.97 PLS CPMG Water 2 1.37 0.99 CPMGt Water 2 0.73 1.oo CPMG Fat 2 2.80 0.96 0.99 Water 2 1.33 CPMG~ "4-ALSR 3 2.16 0.97 Fat CPMG CPMG+ Water 3 0.82 1.oo 1 outlier removed \

,

In a subsequent experiment we conducted an exhaustive forward variable selection on the two data sets. In an attempt not to overfit the data (forward selection can improve the fit almost indefinitely even under full-cross validatory control) we decided only to select two variables which for completely selective time-points should correspond to two latent variables. The two variables were then used as the only regression variables to predict water and fat content. In the selection of variables which include the main variation with respect to water information variables (echo points) 208 and 24 were selected and the prediction error for the regression equation was calculated to 1.39%, considerably better than the regression on exponential parameters. However, if we take out sample 89 that is detected as an outlying sample by the multivariate techniques (see below), the prediction error is reduced quite drastic to 0.79%. In case of FS of variables covarying with fat content, variables (echo points) 199 and 135 were selected and the prediction error calculated to 1.99%, again slightly but significantly better than the regression based on exponential decay functions. Secondly, we applied multivariate methods to the relaxation data. As indicated by Table 3 the performance of the PLS regression was found to be better than the univariate regression on the exponential fit parameters, providing a reduction in the prediction error for both water (reduction by 34%) and fat (reduction by 14%) content. Besides the ease of use and the enhanced prediction ability, the use of PLS also facilitates the detection of outliers. In the PLS regression plots sample 89 was clearly detected as an outlier and a more precise model could be calculated once this sample was removed. It is perhaps interesting to note that removal of this outlier in the univariate regression on the exponential fits also results in an improvement in performance. Finally, we performed NN-ALSR calculations on the data sets. The very slow convergence behaviour of this approach does not allow hll-cross validation during regression for which reason we have only calculated two and three component solutions for all samples and subsequently performed multiple linear regression with full cross-validation on the best component in the concentration matrix. However, since NN-ALSR, unlike PLS, is independent of the reference data in the calculation of the concentrations, this approach was considered to be acceptable from a validation point of view. The performance of univariate regressions to

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Echo

fat and water content using the best correlating component in the concentration matrix are listed in Table 3. The results on the fat and water prediction errors, using two-component models were in this case found to be significantly higher than those for the PLS models. However, the inclusion of an extra component results in a performance comparable to PLS. Curiously, this optimal performance was achieved when performing univariate regression on a “concentration” component that is negatively correlated to water content. Figure 4 shows the “resolved profiles” from the three-component NN-ALSR solution, all of which have exponential character and are quite similar to the solution of the triexponential fitting (Figure 1C). 6 CONCLUSIONS

The present study has demonstrated that the multivariate regression models can reduce the prediction error with up to 34% when compared to univariate regression models on “hard” exponential fitting procedures. The use of multivariate data analysis and low-field NMR has the advantage that the analysis can be performed easily and precisely at the same time without requiring specially trained operators. The multivariate analysis has the additional advantages of being faster and more robust than the exponential fitting procedures and being able to facilitate detection of outliers (relaxation and/or sample abnormalities). From a multivariate data-analytical point of view second-order data structures4, where each sample gives rise to a matrix and a set of calibration samples to a cube are very interesting, because these data make it possible not only to detect outlying samples but also to obtain parsimonious and unique solutions. LF-NMR has a great future potential for generating second order data, for instance by collecting all information from inversion recovery pulse sequences, from 7’1-rho experiments and from applying gradient with variable lengths. We foresee a grand avenue for the combination of low-field NMR and multivariate data analysis. As spectrometers become more and more advanced (increasing field strength and homogeneity, multi-channel instruments, field gradients, etc.. .), the new multivariate data analysis can compensate for the low-resolution (reproducibility is required) and with advantage work in higher dimensions. 7 ACKNOWLEDGEMENTS

This investigation was sponsored by the Danish Veterinary and Agricultural Research Council (SJVF), the Center for Predictive Multivariate Process Analysis and the Danish

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Center for Advanced Food Studies (LMC). We are indebted to Professor Lars Munck for inspiring support and Gilda Kischinovsky for helpful comments on the manuscript. 8 LITERATURE 1. 2. 3.

4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

T. Isaksson, G. Tngersen, A. Iversen and K. I. Hildrum, J. Sci. Food Agric., 1995, 69, 95. P. Lambelet, F. Renevey, C. Kaabi and A. Raemy, J. Agric. Food Chem., 1995,43, 1462. S . M. Jepsen, H. T. Pedersen and S . B. Engelsen, submitted to J. Agric. Food Chem (1998) K. S. Booksh and B. R. Kowalski, Anal. Chem., l994,66,782A. L. G. Thygesen, Holzforschung, 1996,50,434. A. Gerbanowski, D. N. Rutledge, M. H. Feinberg and C. J. Ducauze, Sciences des Aliments, 1997, 17,309. S . B. Engelsen and L. Nsrgaard, CarbohydratePolymers, 1996,30,9. E. G. Bligh and W. J. Dyer, Can. J. Biochem. Physiol., 1959,37,911. H.Y. Carr and E. M. Purcell, Phys. Rev., 1954,94,630. S . Meiboom and D. Gill, Rev. Sci. Instrum., 1958,29,688. J.A. Nelder and R. Mead, ComputerJournal, 1965,7,308. S . Wold, K. Esbensen, and P. Geladi, ChemometricsIntell. Lab. Syst., 1987,2,37. C. L. Lawson and R. J. Hanson, “Solving Least Squares Problems”, Prentice-Hall, 1974. R. Bro and S . de Jong, J. Chemometrics, 1997,11, 393. S . Wold, Technometrics, 1978,20,397. H. H. Huus, “Quality and quality changes in fresh fish”, FA0 Fisheries Technical Paper, 1995, Vol. 348, Chapter 4, p. 23.

Quality Evaluation of Atlantic Halibut (Hi poglossus P hippoglossus L) during Ice Storage Using H NMR Spectroscopy Beathe Sitter,' Jostein Krane? Ingrid S. Gribbestad,' Leif J ~ r g e n s e n ~ and Marit Aursand3

' SINTEF UNIMED, MR CENTRE, N-7034 TRONDHEIM, NORWAY

* NTNU, FACULTY OF CHEMISTRY AND BIOLOGY, MR CENTRE, N-7034 TRONDHEIM, NORWAY SINTEF APPLIED CHEMISTRY, GROUP OF AQUACULTURE, N-7034 TRONDHEIM, NORWAY

1 ABSTRACT 'H nuclear magnetic resonance (NMR) spectroscopy has been investigated as a possible tool for quality evaluation of Atlantic halibut (Hipoglossushipoglossus L). Perchloric acid extracts of muscle samples, taken fkom chill-stored fish over a period of three weeks, were analysed with NMR. TMAO was not found to change notably with storage time, whereas phosphocreatine was only detectable in samples taken at the day of slaughter. N A D P was detectable in samples up to two days after slaughter. ATP and its degradation products were monitored during the whole experiment, which enabled calculations of K-values. Kvalues derived fiom NMR data was compared to K-value measurements with Fresh Tester. The K-values found in the NMR analyses were low during the whole period. These experiments have proven that Nh4R is a possible method for studying ATP degradation of Atlantic halibut, with the potential of monitoring several metabolites simultaneously.

2 INTRODUCTION World sale of farmed fish increase and it is estimated that the annual demand will grow fiom about 70 to 90 million tons in the course of the next decade (1). The Norwegian aquaculture industry produces nearly 320 000 tons per year of Atlantic salmon (Sulmo sulur) (2) and further development in the aquaculture industry will depend on the ability to get other species in cultivation. Atlantic halibut (Hippoglossus hippoglossus L) is a relatively new fish species for cultivation in Norway and nearly 150 todyear are produced for consumption. However, halibut is one of the highest priced fish species, its meat is firm, white and tasty and has great potential for farming in cold marine water. Halibut may become as important as Atlantic salmon in the Norwegian aquaculture industry. Today the marked demand is larger than the production volume and there is a great interest to develop the cultivation

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I

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0

0

ATP

III

Ho-P-o-cHI OH

l It 0

OH

( r j

wl" k c

OH

GXj m c i i

AMP

0

Imine

Figure 1:Enzymatic degradation of adenosine triphosphute to hypoxanthine infish muscle. methods for halibut. Consequently, most of the published research regarding farmed halibut is emphasised on the feeding and cultivation process (3 - 6). The quality aspect, both the nutritional and the eating quality of farmed fish have been brought into focus. The demands for specificationof quality criteria and adequate methods for use in quality control are increasing.

In general, chemical and biochemical changes will occur during handling and storage of the fish that influence the sensory quality (7). Fish spoilage is complex, and consists of several interrelated processes, both enzymatic and bacterial. In our preliminary work with farmed halibut, we have studied the changes in pH, texture and water holding capacity in white muscle during ice storage (8). However, the post mortem degradation products of high-energy phosphates (HEP) which include adenosine triphosphate (ATP), adenosine

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diphosphate (ADP), adenosine monophosphate (AMP),inosine monophosphate (IMP), inosine ( H a ) and hypoxanthine (Hx),are important parameters for fish quality assessment (9). ATP in fish muscles is decomposed by a series of enzymatic processes to hypoxanthine by the route shown in Figure 1 (10). The degree of fieshness is often expressed in terms of a value K ( I I), described by ATP and its degradation products in fish meat (Eq. 1).

[Inosine]+[Hypoxanthine] K(%)=lOOx

(1)

[ATP] + [ADP]+[AMP]+[IMP]+[Inosine]+[Hypoxanthine] The ATP degradation products inosine, hypoxanthine and free ribose have all been suggested as fieshness indicators (12). The phosphocreatinehorganic phosphate ratio has also been found to be a sensitive index of early metabolism (13), while degradation products of trimethylamine oxide (TMAO) are considered indicators of fish meat spoilage (14). Other compounds, such as nicotinamide adenine dinucleotide (NADH+), have also been found to change with storage time (1 9,and may Serve as an index of quality. Freshness test papers are commercially available, supplied as kits for K-value determination. Many of them are, however, inaccurate in determination of K-values of fiesh fish in that sense that they tend to underestimate the freshness values the fust days of storage. The conventional method for ATP post mortem degradation product determination is High Performance Liquid Chromatography (HPLC) (7). Some of the products are very labile and intensive chemical treatment before analysing may result in loss of information. NMR is a non-invasive and non-destructive technique with the potential to detect multiple components, and to a great extend reflect the composition of the studied tissue. The NMR method may be a valuable tool in evaluating quality changes during handling and storage. 3'P-NMR has been used for in vivo and in vitro studies of HEP changes in fish muscle (13, 16 - 18). Howell et al. (14) have used proton NMR spectroscopy to study TMAO and its degradation products in cod and haddock.

To obtain a correct picture of the metabolic changes in fish muscle during handling and storage, studies on intact muscle would be required. Studies of intact tissue can be done with high-resolution magic angle spinning (HR-MAS) analyses that should be a time effective method for quality evaluation. In our initial studies information fiom extracts is useful for analyses of intact tissue, since spectra of tissue will be more complex, caused by broader lines and then overlapping peaks. Our aim with this study was to interpret the high-resolution 'H NMR spectrum of acid extracts of muscle sample from stored halibut and to examine the potential of NMR spectroscopy as a method for evaluation of fieshness and quality changes during handling and storage of halibut.

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3 EXPERIMENTAL 3.1 Fish, sampling and analysis Farmed Atlantic halibut (Hippoglossus hippoglossus L), 5 k 1.5 kg were used. The fish were harvested with a landing net and killed by a blow to the head (approx.10 sec from netting to death), gutted and stored on ice in polystyrene boxes. During the storage period white muscle samples for post mortem catabolism studies and pH was recorded immediately after the fish were killed and after 0, 1, 2, 6, 14 and 21 days in the storage experiment. White muscle pH was recorded between the lateral line and the dorsal fin using a combined electrode (Radiometer type GK 2713 electrode connected to a Radiometer type pH M 80 meter, Copenhagen, Denmark) directly inserted into the muscle. For high-energy phosphate degradation studies, samples (1-2 g) of white muscle were excised and fieeze-clamped within approximately 20 sec using aluminium tongs which were pre-cooled in liquid nitrogen (19). Two parallels were taken from each fishlsample time; one was used for the freshness test strips analyses and one for the NMR experiment. Samples for the NMR experiment were stored in liquid nitrogen before fieeze-drying and perchloric acid extraction according to Erikson (9) and Sellevold (20). 3.2 Freshness test strips The fieshness test kit (Transia Fresh Tester, Transia, France) was purchased from Food Diagnostics (Norway). The sample was added extraction reagent (10 ml) in a plastic bag before the fish meat was crushed and blended with the solution manually. The fieshness testing paper was soaked in the filtrate, covered with plastic film and left dark at room temperature for 10 minutes before immediate evaluation of the colour reaction and K-value determination. 3.3 'H NMR spectroscopy

The neutralised perchloric acid extract was lyophilised and redissolved in 0.6 ml phosphate buffer in D20 @H 7.5M.1, Cambridge Laboratories, England) with TSPA-dd (3-(trimethylsilyl) 3,3,2,2-tetradeuteropropionic acid sodium salt, Merck) used as chemical shift reference. Mode1 compounds such as ATP, ADP, AMP, IMP, inosine and hypoxanthine (all purchased from Fluka) were also analysed in the same phosphate buffer (0.6 ml). Two of the PCA extracts were spiked with small amounts of the reference model compounds for proper identification. A Day 21 sample was spiked with tiny amounts of hypoxanthine, inosine and IMP (ca. 0.5 pm) in succession, whereas a Day 0 sample was spiked with AMP,ADP and ATP (ca. 0.5 pm), also in succession. 'H NMR spectroscopy was performed on a BRUKER DRX600 instrument, operating at 600.130 MHz for protons. The spectra were recorded using presaturation of the water resonance, followed by a 90° excitation pulse. Free induction decays (128) using a spectral width of 12 kHz were collected into 64 K data points, giving an acquisition time of 2.7 seconds. The fiee induction decay was multiplied with a matched exponential filter before zero filling to 128 K real data points and Fourier transformed.

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The spectra were analysed both by integration and non-linear peak fitting program The integration routine is standard software to all BRUKER instruments. Peak fitting was done with a program based on a least square minimisation method (PeakFit from Jandel Scientific, Germany). The peak fitting function used was a combination of Lorentzian and Gaussian functions. 4 RESULTS AND DISCUSSION

4.1 Interpretation of the 1H NMR spectra. The 'H NMR spectra of PCA extracts from halibut show numerous metabolites. The spectra are dominated of signals from TMAO, creatine/phosphocreatine and lactate. There are also detectable signals fiom ATP, ADP, IMP, NADHf, alanine, formate, glucose, glycine and taurine (Figure 2). Assignment of the ATP degradation compounds was done on the basis of comparison with 'H Nh4R spectra of pure compounds (Figure 3). The peaks firom the different compounds appear in the same spectral region, which complicates the assignment of these 'H NMR spectra. The signals fiom the ribose unit can not be used for quantification, mainly because of overlap with other compounds in the isotropic solutions but also loss of intensity of signals due to the multiplicity caused by coupling.

TMAO

I"

, a-Ck

62

%o~pp,,

5.4

18

& 4.6

4.2

3.a

3.4

Cr

PCr

\ ____-

l . , r l I . , . I I . , . . ~ ~ , . . . l , . I ~ l

iao

9.0

ao

7.0

6.0

5.0 (pv)

40

3.0

2.0

I .o

0.0

Figure 2: ' H NMR spectra of PCA extract from a sample taken at Day 0. Abbreviations: ADP: Adenosine diphosphate, ATP: Adenosine triphosphate, Ala: alanine, Cr: Creatine, Glc: Glucose, Gly: Glycine, IMP: Inosine monophosphute, Lac: Lactate, NADH' : Nicotinumide adenine dinucleotide, PCr: Phosphocreatine, TUAO: trimethylamineoxide and Tau: Taurine.

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I“’

C’,H,

ring

C2 ring

h 8.4

8.0

7.6

7.2 (PPm)

6.8

6.4

6.0

4.8

L

4.6

4.4

f.2 PPm

4.0

3.8

Figure 3: ‘H NMR spectra of hypoxanthine (a), inosine (6). IMP (c), AMP (4, ADP (e), and ATP fi in phosphate buffer. The chemical shiji scale is assigned to TSPA-d4 at 0 pprn. Carbons in ribose are labelled ’. Near the suppressed water-signal there are some residual C 2 H signals, which are almost completely suppressed because of its close vicinity of water.

The chemical shifts of pure compounds in buffer differ fiom those in the perchloric acid extracts due to differences in the dielectricity constant of the solutions. Therefore two samples were spiked with the reference model compounds for proper identification. In Figure 4 hypoxanthine give rise to two peaks at 8.21 and 8.19 ppm (Figure 4b). Inosine appear as a singlet at 8.35 ppm and a peak at 8.25 ppm, which overlaps with one of the resonances fiom IMP. IMP also gives rise to the singlet at 8.60 ppm (Figure 4). The Figures 4 e-h demonstratesthat AMP,ADP and ATP contribute to the peak at 8.27 ppm, the peak fiom ATP at a just slightly lower shift than AMP and ADP. AMP gives rise to a well-resolved peak at 8.62 ppm, whereas ADP and ATP give overlapping signals at 8.56 and 8.53 ppm, respectively. The signals fiom hypoxanthine, inosine, IMP and AMP are all well separated, and the area of the peaks can be determined by integration. Both signals from ADP and ATP on the other hand are overlapping with each other, and can therefore not be integrated separately. When estimating the K-value, there is no need for separate quantification of these compounds since the phosphorylated compounds all contribute to the numerator in Eq. 1 and the total amounts of ADP and ATP can be determined as a sum.

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* A7p

a68

a60

89

a44 836 828 (Rn3

am

8U868

a60

89

a44 &828820a*

Figure 4: Spiking of two PCA extracts. ' H NMR spectra of a Day 21 sample (a). The same sample has subsequently been added small amounts of hypoxanthine (b), inosine (c) and IMP (4. ' H NMR spectra of Day 0 sample (e). The same sample has subsequently been added small amounts of AMP ($, ADP (@ and ATP (h). The resonance signals with increased intensity are labelled * and identifies the peaks of the specific compound.

4.2K-value calculation - a comparison between NMR derived data and K-value strips.

Figure 5 demonstrates how the different HEP post mortem compounds varies with storage time. The initial amounts of IMP are low, but increase rapidly and are the dominating in all samples fiom Day 1 on. It also demonstrates the small variations in chemical shifts that make spiking a valuable tool in assignment of the spectra.

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Formate

Day1

"p'

1

1

Irp

ATP + ADP

ATP + ADP

Day 0

I

f

Table 1 The development o the K-value (?A)of Atlantic halibut stored on ice for 21 days (n=6) calculated from the H NMR spectra of PCA extracts and measured by Fresh Tester FTP 11 (Transia).

Dav 0 1 2 6 14 21

I

'H NMR Peakfitted 0 0.5 (M.3) 2.2 (M.3) 13.1 (k3.0) 28.5 (f3.2) 44.7 (k7.5)

'H NMR Integrated 0 0.9 (M.5) 3.4 (f1.3) 14.8 (f4.0) 29.8 (f3.4) 48.0 (f6.6)

1

Fresh Tester FTP I1

14.7 (f1.8) 14.0 (f2.2) 12.8 (f2.0) 24.2 (f5.2) 24.3 (f6.6)

The K-values were calculated by using Eq. 1 for both methods of area measurements, the results are presented in Table 1. K-values calculated by integration of

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spectra are slightly higher than those calculated fiom peak fitting . Area calculation by peak fitting is assumed to give the most correct picture since the PeakFit program calculates the separate areas of overlapping peaks but also because integrals are more affected by baseline distortions. The differences in K-values found by these two methods of area calculations are small, and peak fitting is a time consuming method compared to integration. For routine purposes this has to be considered compared to the accuracy needed in the results. The K-values were also measured with Freshness Test Paper Kit and these results are presented in Table I as well. It is not a very good correlation between the NMR derived data and data obtained by the Fresh Tester. The K-vaIue determination with fieshness test strips is based on a simplified equation (Eq. 2) (21). [Inosine]+~ypoxanthine] K (“YO) = 100 x

[IMP]+[Inosine]+[Hypoxanthine] In this simplified version of the K-value equation, ATP, ADP and AMP are left out and result in higher K-values compared to the standard K-value equation (Eq. 1). The same tendency has been observed in comparative studies between HPLC derived data and the Fresh Tester for Atlantic salmon (9). Generally, it seems as if the Fresh Tester is not suitable for determine the fieshness of halibut during the first days after slaughtering. 4.3 Post mortem catabolic changes during ice storage.

For all fishes the NMR-derived K-values were, as expected, zero at Day 0 and for one fish the K-value was also zero at Day 1. This is due to no detection of inosine or hypoxanthine in the NMR spectra. The K-value increase slowly within the frst week of ice storage ffom 0.5 % in average at the first day of storage to 13.1 % after six days of ice storage. Inosine was detected in the spectra fiom Day 1 while hypoxanthine is not detected until Day 6. The enzymatic breakdown of HEP seems to go slowly and after 21 days on ice the K-value is only 44.7 %. In general, the enzymatic activity resulting in degradation of HEP in fish muscle to hypoxanthine varies among fish species (12). Correspondingly, for Atlantic salmon, the K-value (examined by HPLC) increase ffom zero at day 1 to 38 % after six days on ice (9) and for cod stored on ice the K-value rise fiom 5-10 % to 100 % during four days on ice (12). Ehira (12) observed for plaice nearly the same degradation pattern of HEP that we see for halibut. Furthermore, in muscle of halibut compared to fish species as salmon and rainbow trout, the IMP concentration is constantly higher than the level of inosine through out the whole experiment (Table 1 and Figure 5). For rainbow trout IMP amount to only one third of the inosine concentrationafter five days on ice (22). The degradation of ATP to IMP and hypoxanthine is linked to the sensory quality of the fish flesh where IMP is related to a “good” fish taste but hypoxanthine gives a bitter taste (23). The keeping quality of halibut is known to be good. Several days on ice are commercially recommended to obtain the optimum muscle quality of taste and texture. The maintenance of high IMP level in muscle compared to the other HEP degradation products during most of the storage period can be one factor explaining the good keeping quality of halibut. The slope in K-value calculated fiom the H N M R spectra of PCA extracts in these experiments increase to slowly for the K-value to be a good index of fieshness of Atlantic halibut (9), (12).

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During ice storage, the pH decreased fiom 7.0 to 6.2 as an average for six fish during the tirst two days and increased fiom 6.2 to 6.4 during the next 18 days on storage (data not shown). TMAO is the dominating peak in all spectra of the PCA extracts, and it does not decrease notably with time. None of the degradation products have been found in the NMR spectra, trimethylamine (TMA) was not detected in any of the samples. TMA is formed by bacterial degradation of TMAO and the NMR results indicate low bacterial activity in the muscle through out the storage period. Howell et al. (14) has reported that DMA has been detected using NMR spectroscopy on extracts of cod filets stored at -20°C for 12 months. Phosphocreatine is only detected in the baseline samples (Day 0) as expected, since phosphocreatine is known to decompose to creatine and phosphate within a few hours (13). N A D P is detectable up to Day 2, and no traces can be found after six days of storage. We were not able to identify the degradation products of NADH'. This is probably due to initial low concentration of NADV and thereby low concentration of the degradation constituents.

5 CONCLUSION

The high-resolution 'H NMR spectra of PCA extracts of muscle of Atlantic halibut has been interpreted and the NMR technique seems to be a valuable method to study HEP degradation in fish. Additionally, compared to traditional methods for HEP studies as for instance HPLC, the largest potential of 'H NMR is to monitor changes in a number of metabolites simultaneously. However, for accurate quantification, the sampling procedure needs to be further refined and standardised. The assignment of PCA extracts fiom muscle tissue will be used as a basis for fk-ther N M R analyses of intact tissue with HR-MAS. Studies of intact tissue has the potential of providing a time effective analyses of post mortem catabolism This method can be applied to any other fish species. The high-energy phosphate degradation process in muscle of Atlantic halibut seems to be slow compared to muscle of fish species as cod and salmon. The inosine monophosphate concentration remains at relatively high level fiom Day 1 and hypoxanthine is not detected in the first days of storage. K-value seems not to a valuable indicator of muscle freshness for Atlantic halibut.

6 ACKNOWLEDGMENTS This work was carried out as a part of a SINTEF project financially supported by The Norwegian Research Council (NFR-project 115717/120).

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7 REFERENCES 1 . K. 0ren and S.O. Stefansson, “A Metamorphoric Analyses of environmental Protection Strategy of the Norwegian Fish-farming Industry”, The Norwegian Institute of Technology, Trondheim, Norway, 1991. 2. Kontali Analyse AS, Kristiansund, Norway, 1998 http://www.fiskeoppdrett.nolEnglish/Statisticu‘97~stats~~u~ulture.ht~

3. R. Nordtvedt and S. Thuene, Chem. Intel. Lab. Syst., 1995,29,271 4. T. Nzss, T. Harb, A. Mangor Jensen, K.S. N k , and B. Norberg, Progressivefishculturist, 1996, 58,2 12

5. B. Bjrarnson, Aquaculture, 1995,0138,290 6. H. Hallaraker, A. Folkvord, and S.O. Stefansson, Aquaculture, 1995,0034, 139 7. U. Erikson, “Muscle quality of Atlantic salmon (Salmo salar) as affected by handling stress”, Dept. of Biotechnology, Norwegian University of science and Technology, Trondheim, Norway, 1997, 1. 8. R. Slizyte, “Quality changes in muscle of farmed halibut (Hippoglossus hippoglossus) during ice storage studied by different methods.”, Norwegian University of science and Technology, Faculty of Chemistry and Biology, Trondheim, Norway, 1997, 1 .

9. U. Erikson, A.R. Beyer, and T. Sigholt, J. Food Sci., 1997,62,43 10. B.-0. Kassemsam, B. Sanz Perez, J. Murray, and N.R. Jones, J. Food Sci., 1962,28, 28 1 1 . T. Saito, K. Arai, and M. Matsuyoshu, Bull. Jap. SOC.Sci. Fish., 1959,24, 750

12. S. Ehira, Bull. Tokai Reg. Fish. Res. Lab., 1976,88, 1 13. A. Chiba, M. Hamahuchi, M. Kosaka, T. Asai, T. Tokuno, and S. Chichibu, J. Food Sci., 1991, 56, 660 14. N. Howell, Y . Shavila, M. Grootveld, and S. Williams, J. Sci. Food Agric., 1996, 72,

49 15. N. R. Jones and J. Murray, Bull. Jap. SOC.Sci. Fish., 1966,32, 197 16. L. Jrargensen and H. Grasdalen, Comp. Biochem. Biophys., 1986,84B, 447 17. G. van den Thillart, A. van Waarde, H.J. Muller, C. Erkelens, and J. Lugtenburg, Comp. Biochem. Biophys., 1990,95B, 795 18. G . van den Thillart, F. Korner, A. van Waarde, C. Erkelens, and J. Lugtenburg, . I Magn. Reson., 1989,84,573 19. H. Brarjeson and E. Fellenius, Acta Physiol. Scand., 1990,96,202

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20.0. F. M. Sellevold, P. Jynge, and K. Aarstad, J. Mol. Cell. Cardiol,, 1986, 18,517 21. I. Karube, H. Matsuoka, S. Suzuki, E. Watanabe, and K. Toyama, J. Sci. Food Chem., 1984,32,314 22.M. D. Huynh, J. Makcey and R. Gawley, Seafood Science and Technology. Proceedings of the international conference: Seafood 2000, 199023.

23. G. C. Fletcher, H. A. Bremner, J. Olley and J. A. Statham, Food Rev. Znt., 1990,6(4), 489

Applications of Magnetic Resonance to Food Processing and Engineering

Magnetic Resonance Temperature Mapping A. G . Webb and J. B. Litchfield DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING, DEPARTMENT OF AGRICULTURAL ENGINEERING, UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN, USA

1 INTRODUCTION

Many important technical problems in food processing could be addressed if a technique existed to measure temperatures non-invasively and non-destructively inside solid foods during processing. One such example is aseptic processing of foods which dates back to the early 1950s when the Dole system was first used for the aseptic filling of metal c a m 1 The objective was to produce a higher quality product than could be obtained with traditional sterilization techniques. This improvement resulted from less over-processing of the product, due to more efficient heat transfer and better control of product temperatures. This process works well for liquids and for particles small enough to be heated sufficiently quickly: there is a problem, however, in the high temperature short time (HTST) processing of foods containing particles over a few millimetres in diameter, in which heat transfer is effectively conduction controlled.2 Aseptic processing of food products is normally performed in a continuous system in which the product is pumped through a series of heat exchangers, which heat the food, and then through an insulated holding tube where particles, if present, may continue to be heated by the carrier fluid. The liquid is heated through the walls of the heat exchangers, but the particles exhibit thermal lags. The extent of the lag depends on a number of factors, including particle size, the thermal and mechanical properties of the particles and carrier fluid, residence time, and flow characteristics. To ensure that all parts of the product achieve the required temperature exposure to guarantee a microbiologically safe product, and at the same time preserve the nutritional and sensory value of the product, detailed knowledge of the heat transfer coefficient is required. An obstacle to the development of aseptic processes for foods containing particles is the absence of information for the temperature-time relationship at the cold spot of the particles. Non-invasive temperature mapping using magnetic resonance

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imaging (MRI) is a promising technique for enabling direct measurement of heat transfer. The alternative is to use conservative model-based estimations3 which result in higher than necessary temperatures being used, in turn destroying flavours and nutrients. 4

2 TEMPERATURE MAPPING USING MAGNETIC RESONANCE IMAGING A number of methods have been devised for measuring temperature non-invasively using either magnetic resonance spectroscopy or imaging. Temperature dependent magnetic resonance parameters include the molecular self-diffusion coefficient,5-9 the spin-lattice relaxation time,10-17 and the proton chemical'shift.18-26 Other parameters such as the net polarization and spin-spin relaxation time have been used to a lesser degree. Although most techniques were originally designed for medical imaging, for example to investigate ablation or heating of tumours, they are finding increasing use in food science. It is interesting to compare the major problems which arise in medical and food science applications. In medical imaging, particularly if heat needs to be applied for a long period of time, measurements can be compromised by changes in blood perfusion and blood flow. In food science, the sample being heated is often either flowing, or is being heated by a flowing liquid, which can cause image artifacts. The following sections discuss the three main methods of temperature mapping in terms of physical basis, relative advantages and disadvantages, and applications to food science.

2.1 Temperature Dependence of the Molecular Diffusion Coefficient (D). The origin for the temperature dependence of the diffusion coefficient of water arises from the Stokes-Einstein equation:

where k is the Boltzmann constant, T the temperature,

the viscosity and RD the

molecular hydrodynamic radius. The dependence can also be expressed as:

D = D, exp

E kT

-3

where D , is the diffusion coefficient at infinite temperature and Ea is the activation energy for molecular diffusion. It is commonly assumed that the activation energy is

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independent of temperature for the temperature range relevant to medical and food science applications. In order to encode diffusion into the signal intensity of magnetic resonance images, diffusion weighted images are acquired using a variation on the wellknown Stejskal-Tanner spectroscopic method.27 The sequence is shown in Figure 1.

180

90

read phase I

n

Figure 1. Basic dirusion weighted imaging sequence, in which the difision encoding gradients (shaded areas) are applied, in this case, along the slice select direction. The dependence of the signal intensity on diffusion in a particular voxel is given by: S = exp -[yZGz8’D(A-q)]

[31

The diffusion coefficient can be calculated on a voxel-by-voxel basis by acquiring successive images with a different value of g. If images are acquired at an initial temperature (Ti) and final temperature (Tf), and the corresponding diffusion coefficients are calculated as Di and Dfrespectively, then:

For Tf - Ti << Ti, this expression simplifies to:

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In order to calculate the value of the activation energy, calibration experiments must be carried out to relate the diffusion coefficient as measured using MRI with a known temperature distribution, usually measuredwith a fibre optic temperature sensor. An example from food science is shown in Figure 2.28 Diffusion weighted images were acquired for a potato which was 25 mm in diameter and 60 mm in length. This was presoaked for a number of hours to ensure uniform moisture distribution. The sample was placed in the centre of a 45 mm diameter sample holder, which was in turn set in an 8 cm diameter radiofrequency NMR coil, impedance matched to 200 MHz. The heating system consisted of a water bath, pump, tubing and thermocouples. The sample was heated by circulating water at 50OC. The initial temperature of the sample was 20OC. Diffusion mapping data were acquired at 1 minute intervals for a duration of 4 minutes, beginning 1 minute after heating. In this case, for rapid data acquisition, two images only were acquired, one with no diffusion gradients, and the other with a "b factor" of 350 sec/mm2. The echo time (TE) was 95 ms, repetition time (TR) 700 ms, and 16 phase encoding steps were acquired resulting in a total image acquisition time of 10 seconds. Non-magnetic thermocouples (copper-constantan) were used with 1.2 mm outside insulation diameter around a pair of 0.25 mm diameter wires. The junction length of the thermocouple was 1 mm long. The thermocouples were implanted into the sample at radial depths of 3, 6 , 9 and 12 mm, and data were acquired with a Campbell 21X micrologger at 2 second intervals, simultaneous with acquisition of the MRI data. Excellent agreement is seen between the thermocouple and MRI data in Figure 2.

a~MRI 2 min &

MRI 4 min

50 -

0

0

E c 3 .

; a E

k

40-

30-

-20

-10

0 10 Distance (mm)

20

Figure 2. MRI and thermocouple profiles at 2 and 4 minutes in a cylinder of potato during heating. The initial potato temperature was 2 0 W , and the potato was heated by circulating water at 500Cpast the sample.

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Diffusion weighted imaging has a high thermal sensitivity, with changes of more than 2% in the value of D per degree Celsius. The major disadvantage is related to the fact that several factors, other than temperature changes, can also cause changes in the diffusion coefficient. For example, variable moisture content can cause differences in the value of D within a sample, and this moisture content also change as a result of thermal changes, and heterogeneity within a single sample results in spatial heterogeneity in the activation energy for molecular diffusion, giving different values of the thermal sensitivity. Careful characterization of a particular system is, therefore, necessary to be able to monitor temperature changes in non-uniform systems.

2.2 Temperature Dependence of the Spin-lattice (Ti) Relaxation time For a simple system consisting of "free" water, the TI relaxation time is determined solely by intermolecular dipole-dipole interactions, and can be related to the temperature dependent correlation time:29

This leads to a simple expression for the temperature dependence of TI:

T,

Oc

exp(-%)

[71

where k is Boltzmann's constant, Ea is the activation energy and T the temperature. This exponential function is approximately linear over the small temperature ranges typically found in hyperthermia treatments of cancer, with a sensitivity of 0.8 - 2.0 % per OC depending upon the particular tissue being studied. However, at higher temperatures, it has been found that for food samples, the activation energy itself is a function of temperature.

A further complication arises since, in food samples, the water is often found in both "free" and "associated" states. This means that additional relaxation mechanisms become important: for example, chemical exchange between free and associated water,

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and cross-relaxation between water and macromolecules via dipole-dipole interactions. If it is assumed that the system is in the fast chemical-exchange regime, then: 1

-

TI

a TI,

+-

1-a

191

TI, + %

where TI, is the TI of the associatec. water, a is the fraction of water that is associated, Tlf if the T I of the free water, and Ta is the correlation time of the associated water. The net result is that the temperature dependence of each type of sample studied needs to be calibrated carefully in order to make accurate temperature measurements, and heterogeneity within a single sample may lead to different dependencies of T I upon temperature. Spatial information on the T I relaxation times can either be obtained using a spinecho or gradient echo imaging sequence, shown below in Figure 3.

180

90

reaw I

-H--

phase

I

I

read

phase

Figure 3. Gradient-echo ( l e f ) and spin-echo (right) sequences used to measure temperature via changes in the spin-lattice relaxation time.

The signal intensity from a spin-echo sequence is given by:

S =

,

bD

J

where k(T) is the temperature dependent polarization, Pproton is the proton density, TR is the repetition time between successive 90 degree pulses, TE is the echo time and bD

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represents signal loss due to diffusion. For the usual case of TR>>TE, TE<
For the gradient echo sequence, the signal intensity is given by: S..

k(T) sin a (1-el) 1- elcos a

where el = exp ( - T W l ) . Both sequences can therefore be used to measure changes in T1 via the image signal intensity. The main advantages of the spin-echo sequence is its robustness with respect to changes in the homogeneity of the main magnetic field (which cause signal changes in gradient echo sequences). However, data acquisition is much slower due to the longer repetition times necessary for a reasonable signal-tonoise ratio. Experiments were carried out on carrots, since these are a popular ingredient in aseptically processed soups and stew~.~0131 The top section of a large carrot, approximately 28 mm in diameter, was thoroughly cooked in order to make it as homogeneous as possible. It was then cooled and placed in a cylindrical plastic chamber and held in place with plastic screws. For calibration, water from a uniform temperature bath was pumped around the sample for approximately 30 minutes before acquiring an image: water temperatures between 18 and 85OC were used and confirmed using a fibre optic sensor. MRI was performed as described previously using a 4.7 Tesla (200 MHz) magnet and 8 cm diameter radiofrequency coil. Images were acquired using an inversion-recovery spin-echo sequence with a very short TE (6 ms). Thirty two phase encoding steps were used. A non-linear voxel-by-voxel T i fit was used to process the data, shown in Figure 4. The error bars were determined from the standard deviation from the average of four measurements at each temperature. Linear regression was used to quantify the relationship as: T,(sec) = 0.48

+

0.0318T('C)

For dynamic imaging of the spatial distribution of temperatures during heating, the carrot sample was prepared in the same manner as described above. The initial temperature of the carrot was 20OC and water at 83OC was pumped around the sample to create dynamic temperature gradients. Water from the constant temperature bath originally bypassed the sample until a steady state temperature in the bath was obtained.

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30

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Figure 4. TI vs temperature for cooked carrot. TI was measured with an inversion recovery spin-echo pulse sequence. Three way valves were then used to divert the flow through the chamber in which the sample was placed, and the experiment was started. Image acquisition time was 24 seconds, and the nominal sampling time was recorded when data acquisition reached the half-way point. The sampling resolution was 0.18 mm in the frequency encoding direction and 1.25 mm in the phase encoding dimension. Horizontal temperature profiles are shown in Figure 5. As the temperature increased there was more scatter in the measured temperatures, and the outer ring of the carrot exhibited a lower T i than the remainder of the carrot. After heating for 370 seconds, this caused an apparent dip in temperature approximately 5 mm from either side of the carrot centre. In order to eliminate this error, homogeneous carrot samples without the core ring would have to be used, or the temperature dependence of the ring region T i would have to be determined.

I

"15

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Figure 5. Temperature profiles in cooked carrot heated with 83OC water The initial temperature of the carrot was 200C.

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2.3 Temperature Dependence of the Proton Resonance Frequency. The local magnetic field at the nucleus depends not only upon the macroscopic external magnetic field (Bo), but also upon the electronic shielding of the nucleus (a) and the magnetic susceptibility of the sample ( x ) . The latter two quantities are temperature dependent as shown in equation [13].

The effect of increasing temperature is to alter the nature of the hydrogen bonding in ~ a t e r . These 3 ~ effects lead to a change in the shielding constant. For a large number of tissue types, including cooked and uncooked pig brain, pig muscle, pig liver and pig kidney,33 1% NaCl solution, 4% agar, boiled egg white, and chicken muscle,34 the temperature sensitivity is approximately -0.01 ppm / O C . This constant thermal sensitivity across a wide range of samples is a great advantage when compared to the previously described techniques, where the temperature dependence on the T i relaxation time and molecular self-diffusion coefficient is highly sample specific. The magnetic susceptibility is also temperature dependent, the effect arising mainly from the change in water density with temperature, and is equal to +0.003 ppm / OC.35 Changes in the proton reference frequency are most easily transformed into temperature maps by measuring phase changes from phase-sensitive magnetic resonance images produced by a gradient echo sequence. In a sequence with an echo time of TE, and temperature increment AT, then the phase change in an image voxel is given by: A@(T) = -0.01 y.TE.B,.AT One problem with the method, as outlined above, is that any drifts in the main magnetic field will result in apparent temperature changes, which are unrelated to any real effects. In order to minimize the effects of any drift, external phantoms (usually vials of water or acetone) are placed around the sample being heated. In most cases, these are placed far enough away so that no heating occurs in the phantom, and linear interpolation of the phase changes occuring in the phantoms can produce a "magnetic field drift map" which can be subtracted from the phase map of the sample. In cases where conductive heat transfer from the sample cause the phantoms themselves to change temperature, then readings from fibre optic probes placed in the phantoms can

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be used to correct for these effects. The overall procedure is summarized by the flow diagram shown in Figure 6.

lo

01

1 "c0l

correction of the temperature drift in the reference phantoms

corrected phase difference image

temperature map

Figure 6. Flow diagram of the image processing procedure for phase mapping. The temperature dependence of the water chemical shift was evaluated for raw and cooked potatoes (Potato Specialists, Blue Island, IL) and an agar gel.36 Cylinders 1.5 cm in diameter and 6 cm long were made from a 1.5%agar gel, and cut from a raw red potato and a red potato boiled for 5 minutes. The cylinders were placed in a water bath at 70OC until they reached uniform temperature. They were then removed and placed in a holder inside an 8 cm radiofrequency coil along with a test tube of water as a reference signal. Fiber optic sensors (model T104-06PT-03, Photonetics, Wakefield MA) were inserted into the sample and the test tube and the temperatures recorded every 10 seconds. A spoiled gradient recalled acquisition in steady state (GRASS) gradient echo sequence was used with TR 50 ms, TE 6 ms, a 6 x 4 cm field of view, 64

25 1

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x 64 data matrix, two transients and a slice thickness of 3 mm. Figure 7 shows that there is a good straight line dependence of the chemical shift on temperature for all three samples studied. A$ (radians)

3.5

3 2.5 2

0.

I I

cooked potato o

raw potato

=Do* 1

UPa. *.

1.5 1

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Figure 7 . Temperature dependence of the waterproton chemical shzft. The slope of the lines are the temperature coefficients: -0.00983,-0.0104,and 0.0107ppm /OCfor the gel, raw and cooked potato respectively.

As mentioned in the introduction to this paper, aseptic processing is a natural application for temperature mapping using MRI. A number of experiments have been performed. The setup for measuring temperature during aseptic processing is shown below in a simplified diagram. The usual steel holding tube has been replaced by one made of kynar, a perfluorocarbon polymer, for magnet compatibility. For the results shown in Figure 9, a "potato soup" (potatoes in starch solution) was heated. The particles used were obtained from a commercial supplier, and thus not all the particles were shaped like cubes. Some of the particles cut from the surface of the potatoes had curved surfaces: this effect occurred more often for the larger particles. Figure 9 shows the effect of particle size on the measured temperature distributions within the particle: as expected, larger particles showed a larger change in temperature distribution across their diameter than smaller particles.

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qIgJz-l Ingredients

Kynar tube

Steam

\

Blend tank

Condensate outlet

PD pump water ngil:+:

water

Outlet

PD pump back pressure

Cooling water outlet

Figure 8. Aseptic processingsetup for temperature measurement using MRI.

I10 108 06 4 04 El02 $100 % 98 96 t- 94 92

I

I

I

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__

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23 +8s

33 43 pixel number -48s -3328

Figure 9. Effect of particle size on the measured AT. The larger particle on the left shows a larger AT than the smaller particle on the right. The AT can be seen diminishing over time. After 5 minutes the temperature is almost constant.

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3 SUMMARY OF TEMPERATURE MEASURING METHODS

Each of the three methods described previously has its individual strengths and weaknesses. While none of the weaknesses prevents the particular method from providing good data, considerable care must be taken in careful calibration and interpretation. Temperature mapping based on the T1 relaxation time has the advantage that it is highly robust with respect to inter-image motion. The exact position of the sample, and the homogeneity of the main magnetic field at that position, are not factors which influence the T 1. The thermal sensitivity is reasonable, particularly when temperature elevations are large. The major disadvantages relate to the complexity of the temperature dependence for heterogenous systems, and the temperature dependence is markedly different across meat, vegetable and fruit samples. Calibrations must be performed, minimally, on each species being studied. Also, at very high temperatures, the T 1 becomes very long, meaning that data acquisition times to obtain a given signal to noise ratio are lengthened. The thermal sensitivity of the diffusion coefficient is usually higher than that of the T i . It is also insensitive to inter-image motion, but can produce large artifacts if the sample moves during acquisition of a single data set with different values of the diffusion encoding gradient. The major disadvantages are identical to those mentioned for the T i , namely sample dependence and the need for calibration. It should also be noted that diffusional signal losses at high temperatures also necessitate signal averaging to obtain high signal-to-noise images, and therefore temporal resolution is lost. The final method based on the proton reference frequency appears to be the most promising, if accurate thermal maps are needed. The lack of variance of thermal sensitivity with different samples is its major advantage. The main drawback is the motion sensitivity over the entire time of the experiment, since any movement results in the sample experiencing a different magnetic field, and image processing via phase subtraction from a reference image is no longer possible

4 CONCLUSIONS AND FUTURE DIRECTIONS

The area of temperature mapping in food science is relatively young and still developing. Using high field magnetic fields, two-dimensional heating profiles in processes such as aseptic processing and ohmic heating can be obtained with high thermal, spatial and temporal resolution. A particularly important question is whether

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these techniques can be used at much lower fields, where the magnet technology becomes much cheaper. The need for great thermal accuracy might also be relaxed if the aim is merely to determine the extent of heating within the sample rather than the exact temperature distribution. Acknowledgements. Funding for this project has been provided by The Center for Aseptic Processing and Packaging Studies and the FDA National Center for Food Safety and Technology. All imaging experiments were obtained using facilities provided by the Biomedical Magnetic Resonance Laboratory, Shared Instrumentation Grant 1s 10RR06243, and the Biomedical Research Technology Grant PHS5 P41 RR05964. References.

1. D.R.Heldman, Food Technol., 1989,43, 122. 2. P.J.Fryer et al. J.Food.Eng., 1993, 18, 101. 3. S.K.Sastry, B.F.Heskitt and J.L.Blaisdel1, Food Technol., 1989, 43, 132. 4. D.L.Parrot, Food Tech., 1992, 46,68. 5 . D.LeBihan, J.Delannoy and R.L.Levin, Radiology, 1989,171,853. 6. J.Delannoy et al. Magn.Reson.Med., 1991, 19, 333. 7. Y.Zhang et al. Int.J.Hyperthermia, 1992,8,263. 8 . T.V.Samulski et al. Znt.J.Hyperthermia, 1992,8,819. 9. J.McFal1 et al. Int J.Hyperthennia, 1995,11,73. 10. D.L.Parker et al. Med.Phys., 1983, 10, 321. 1 1. D.L.Parker, IEEE Trans.Biomed.Eng., 1984, BME-31, 16 1. 12. R.L.Dickinson et al. J.Comput.Assist.Tomogr., 1986, 10,468. 13. H.E.Cline et al. Magn.Reson.Med., 1993, 30,98. 14:H.E.Cline et al. Magn.Reson.Med., 1994, 31,628. 15. 1.R.Young et al. Magn.Reson.Med., 1994, 31,342. 16.I.R.Young et al. Magn.Reson.Med., 1994,32,358. 17. R.Matsumoto et al. J.Magn.Reson.lmag., 1994,4,65. 18. K.Kuroda et al. Biomed.Thermology, 1993,13,43. 19. J.De Poorter et al. J.Magn.Reson.Ser.B, 1994, 103,234. 20. J.De Poorter et al. Magn.Reson.Med., 1995.33,74. 21. J.De Poorter, Magn.Reson.Med., 1995, 34, 359. 22. Y.Ishiharaet al. Magn.Reson.Med., 1995,34,814. 23. K.Kuroda et al. Magn.Reson.Med., 1996,35,20.

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24. A.H.Chung et al. Magn.Reson.Med.,1996,36,745. 25. J.R.MacFal1 et al. Med.Phys., 1996,23,1775. 26. K.Kurodaet al. Magn.Reson.Med., 1997,38,845. 27. E.O.Stejskal and J.E.Tanner, J.Chem.Phys., 1965,42,288. 28. X.Sun, J.B.Litchfield and S.J.Schmidt,J.Food.Sci., 1993,68, 168. 29. N.Bloembergen, E.M.Purcel1and R.V.Pound,Phys Rev., 1948,73,679. 30. G.Hulbert, J.B.Litchfield and S.J.Schmidt,J.Food.Sci., 1995,70,780. 3 1. J.B .Litchfield, J.Magn.Reson.Anal., 1996,2, 172. 32. J.C.Hindman, J.Chem.Phys., 1966,44,4582. 33. Y.Ishihara et al. Magn.Reson.Med., 1995,34,814. 34. R.D.Peters, R.S.Hinks and R.M.Henkelman, Magn.Reson.Med., 1998,40,454. 35. J.S.Philo and W.M.Fairbank, J.Chem.Phys., 1980,72,4429. 36. C.A.Kantt, A.G.Webb and J.B.Litchfield, J.Food.Sci., 1997, 62, 1.

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Study and Modelisation of Starch Gelatinisation in Potatoes with Magnetic Resonance Imaging Caroline A. Toussaint,' FranGois Langevin; Jean-Pierre Pain' and Adeline Goullieux3

' DEPARTEMENT DE GENIE CHIMIQUE, UNIVERSITE DE TECHNOLOGIE DE C O M P I ~ N E , FRANCE

* CENTRE D'IMAGEFUE MEDICALE AVANCEE, COMP~GNE,FRANCE DEPARTEMENT DE GENIE BIOLOGIQUE, IUT D'AMIENS, FRANCE

1. INTRODUCTION Within the food industry, potatoes are often processed from their raw to a cooked form e.g. chips, boiled and fried potatoes... The study of modifications of the potato matter is thus of great interest to the food industry. During the cooking of potatoes, starch gelatinisation is the most significant modification. Studies on the gelatinisation of potatoes have already been carried out by Verlinden et aZ' : they demonstrate that the gelatinisation process contributes only to a limited extent to the texture. Privisani et aZ2 studied the kinetics of starch gelatinisation in potatoes and showed that the equation of the ungelatinised fraction nG was a first order reaction:

The dependence of the constant of speed of gelatinisation kg at temperature T can be expressed by the law of Arrhenius3 :

a

The objective of this study was to design model capable of predicting the gelatinised fraction of a cylindrical potato sample varying as a function of time, temperature and penetration of the front of gelatinisation. The technique of magnetic resonance imaging was used for all experimental data acquisition. All cellular and extracellular media rich in water produce strong resonance of the water proton, permitting good MRI. In medicine4, this is used to identify different soft fabrics ; similarly in food science; the MRI provides a tool of studying temperature and textural changes of food product's6,

2. THEORY 2.1 Quantic Aspect Magnetic resonance imaging4 is based on the same principles as spectroscopy NMR. The nuclei with non null values of spin, like those of proton (1H) and carbon 13 (13C), have one angular momentum or spin. As these nuclei have an electric charge, their rotation produces a microscopic magnetic moment @ . In absence of an external magnetic field, the

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magnetic dipoles are randomly directed. In the presence of a field B,, however, the dipoles are directed in the parallel or antiparallel direction of the field B,. For the proton H and carbon 13C, two states of energy are allocated : m = +1/2 or -1/2. The difference in energy between the two states is : E2 - El = yfiBo= hY (3) where A is the reduced Planck's constant, h the Planck's constant, Y the gyromagnetic coefficient and Y the frequency of transition. As low energy states are favoured, there is a resulting magnetisation M in the direction of the states with low energy.

2.2 Excitation By using the laws of traditional physics, the magnetic dipoles are not stationary but turn around the axis of B, at a frequency called Larmor frequency : YO = ~00/2n= yB&X (4) where W, is the angular frequency. When a radio frequency of Blcos(ot) is applied with a frequency 0 equal to COO, the moment M is magnetised around axis z towards plan x-y. The intensity and the period of application of B1 determine the degree of rotation of M.

2.3 Relaxation After the application of the radio frequency, the magnetic moments return to their original position, the axis z. This relieving is caused by the return of the longitudinal and transverse components of magnetising to their original positions. Longitudinal or spin lattice relaxation time T,, is related to the return to the low energy level of the excited protons, and is characterised by the return of the Mz component of magnetisation. Transverse or spin - spin relaxation time T,, is due to the dephasing of the spins in plan xy, and characterises the reduction in the Mxy component of magnetising. It should be noted that T2 5 TI.

2.4 T, weighted images by MRI In the quantum model, the radio frequency transfers some of the nuclei from the low to the high energy level. Consequently, T,determines time necessary for these nuclei change their quantum state. The physical, chemical and thermal environment of the sample determines the facility of the transfers of the nuclei between these two energy levels. Imbalance is caused by the force exerted on the nucleus by the radio frequency. The existence of these fluctuations at the Larmor frequency produces a force on the nucleus close causing a return to the low energy level at a different speed. Furthermore, by modifying the environment of the nucleus, this relaxation time can be modified. If this time is higher than T, (Figure 1). the second impulse will be sent before complete relaxation : the signal is thus different for two starch samples in different environments, in T, weighted images. It is this phenomenon which enables images of gelatinisation to be obtained. Thus, in food science, in particular for potatoes with small variations in structures, a very sensitive signal is obtained ; this makes MRI an excellent tool to characterise and quantify modifications in foods. From the point of view the capacity of work, the evolution of gelatinisation is quantified and modelled directly starting from the signal obtained.

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3. MATERIAL AND METHODS

TR

TE

ms

ms

75

25

'

a

a 35"

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M atrice

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256x256

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Gradient of selection of cut Gradient of reading -

Gradient of phase

. .

Echos

FID Echo of gradient Figure I :Example of an echo ofgradient The images were taken in the Centre d'hagerie Miidicale Avanck in Compi5gne. The apparatus Vectra GE produces a magnetic static field Bo of 0.5 Tesla. The reception antenna used is an antenna called " head " 22 cm in diameter and has an area of radio frequency of 21 MHz. 3.3 Modelling of the gelatinisation

Modelling was carried out using computer softwares Matlab@ and Excel@. The raw T, weighted images obtained by MRI were initially reprocessed to provide matrices of

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identical size. Then by a program realised under Matlab@, an interpolation of the values of the same x-coordinate signal of the cylinder made it possible to screen off some of the heterogeneities and thus to obtain a profile of gelatinisation (Figure 2). ,

S

i

g

G

. X

X

Y View 3D

Cut 2D

Profile of gelatinisation

Figure 2 :Method to obtain a profile of gelatinisation

The ungelatinised fraction is the result of the ratio of the signal S and the maximum signal .,S ,,S is however a factor which varies according to the parameters of the equipment of MRI,the protonic density @ of the sample and the relaxation times T, and T2. For each experiment,,S was thus determined, a calibration on the raw potato (S = So) and cooked potato (S= S,) was performed (Figure 3). Lastly, the data obtained in each profile of gelatinisation are integrated in a data table to be analysed and to allow the development of a mathematical model under Excel 0.

Figure 3 :TIweighted images of> raw potato (le4) and of a cooked potato at 100°C during 5 minutes (right); value of S,, obtained by S (cooked) - S (raw) 4. RESULTS AND DISCUSSION

The parameters used to study the ungelatinised fraction nG are the time t, the temperature T, at the surface of the cylinder (e.g. temperature of the water because the heat transfer by convection at the surface of the cylinder is zero) and the factor related to the radius of the cylinder r'. This factor is obtained by the equation :

(5)

r ' = R, - r with r the radius and R, the cylinder radius. This factor is used to study the gelatinisation from the surface to the centre. Based on equations 1 and 2, a model is obtained on the form : (6)

with pfg parameter of advancement of the front of gelatinisation in potato, QRf constant speed of gelatinisation at the temperature T"', T"' temperature of reference,,,,t minimum

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time necessary for the start of gelatinisation, E, energy of activation of the gelatinisation reaction, R universal gas constant. The parameters are then determined and show several significant points. Firstly, the constant speed of gelatinisation is a function of r' : (7) kglref= 0.00831 s-l for r' < 5.5 mm kgZref = 0.00313846 X r' - 0,00894985 s-l for 5.5 mm 8.5 mm

kgTf

Two zones are distinctly observed with kFf a constant zone (k rrf et k,,"? and a B' This variation of the constant speed of gelatinisation is caused by transitory zone (kgTf). the heterogeneous structure of potato. This can be seen in the TI weighted images of the raw and cooked potato (Figure 4).The figure clearly shows that the centre structure and composition is different from the outer region. If then a Matlab@ program is used, an intensity profile of non gelatinised starch in raw and cooked potatoes can be obtained (Figure 5) ; as shown the centre of the potato gelatinises more slowly than the outer part of the sample. This is reflected in the higher rate constant used for the centre of the sample. There are several possible reasons for this : the structure of potato is different, the quantity of reserves of starch or its availability, related to its water content, vary according to radial position.

Figure 4 : TI weighted images of potatoes raw Clef) cooked (right) : heterogeneity caused by a vein in the structure ofpotato Signal intensity (96)

,w

h

~

~

~

-

70

gelatinisation less significant

Bo-

gelatinisation more significant

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Radial position in center cross sectionhrn)

8

-

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.

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-

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0

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,

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Radial position in center cross section(mm)

Figure 5 :profiles of intensity :raw potato (left) and cookedpotato (right)

.

.

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Applications of Magnetic Resonance to Food Processing and Engineering

Another component of the equation is fin, minimum time necessary to the start of gelatinisation (Figure 6) : (8)

t min(r',T, fflfer'

138.71 38.38xexp _ _ Twrer

1-

-1 f 1+ \- 8.53 + 0.385 x T,,e, )x exp (-53.98+T,,,)

II

/

x u - r')

JJ

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oLv+-0

30

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-f

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+

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Figure 6 :Representation of the signal of the ungelatinisedfi-action according to time : characterisation of tmin (bottom) It should be noticed that the equation confirms the optical observation. The geater the On the other hand, an increase in temperature reduces.",,t penetration depth the larger kin. The last factor of the equation to be considered is the function pfg(r',Twam) which takes account of the advance of the front of gelatinisation in potato : (9)

lI

>t water, the function pfi, in a homogeneous solid model, would be simply relatedto the penetration of the front of gelatinisation and would be on the form p,,'=axexp(-br'). In fact, a much more complex function is obtained, related to the fact that the potato has a heterogeneous structure. Thus a function pfgreplaces the function pfi in r' and Twater, related to the heterogeneity of our food. This was already partly taken into account by the constant speed of gelatinisation. The heterogeneous distribution of the starch in the potato thus plays a important role in this model. The results obtained after image processing (Figure 7 and Figure 8) allowed following mathematical modelling :

(10)

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with:

E,=6850 J.mol-' (experimental value) R=8.3 14 J.mol".K-' Txf=lOO "C

000 1=4min30

1=4min

t=5min

Figure 7 :Map of gelatinisation by MRI: Cooking of 11potato cylinders in water at 80°C (left));times of cooking are represented on the right. ~-

-

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-experimental profile of gelatinisation - -model _ _

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of profile of gelatinisation - -. .__

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Figure 8 :Profiles of gelatinisation of a potato cylinder cooked in water at 100°C on a depth of 4.5 mm

5. CONCLUSIONS A model of starch gelatinisation in potatoes during cooking has been produced. A first order equation was deduced. It elucidates several significant points : the heterogeneous structure of potato (three different regions with different availability of starch). It is noted, however, that in spite of this heterogeneity, a model can be designed and is presented by the equation 9. This equation provides a value of the non gelatinised fraction at any point according to the time and temperature.

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The use of the MRI to study and model the gelatinisation of potatoes is of obvious interest. In addition to the excellent precision obtained during the acquisition of the images (approximately lmm), the principal attraction of this technique is the non invasive aspect of data acquisition. As a result, errors are avoided during the acquisition of the data (i.e. : by microscopy: sample cutting causes a certain deformation of the cylinder). Furthermore, the modelling of the gelatinisation of potatoes is of great economic interest. Such a model will permit process optimisation, i.e. time and temperature of cooking for each sample size. This work is a part more general programme of study of the thermal transfer in potatoes at temperatures involving gelatinisation. Indeed, during cooking at temperatures ranging between 70 and 100°C, the phenomenon of gelatinisation is linked to that of heat transfer objective. This study thus allowed us to model the gelatinisation of potatoes. Our current work is to study the heat transfer in a dynamic state (realisation of images by MRI during the cooking of the potato cylinders) and to develop a model of the heat transfer in potatoes without interference of gelatinisation. 6. ACKNOWLEDGMENT This work was supported by the Conseil Regional de Picardie, project numbers 96-A1. The potatoes used were kindly supplied by P. Collot, Villemoyenne, France.

1. 7. REFERENCES 1 Veriinden B.E., Nicolai' B.M. and De Baerdemaeker J. (1995). The starch gelatinization in potatoes during cooking in relation to the modelling of texture kinetics. J. Food Eng., 24, 165-179. 2 Pravisani, C.I., Califano, A.N. and Calvelo, A. (1985). Kinetics of starch gelatinization in potatoes. J. Food Sci., 50,657-660. 3 Kozempel M.F. (1988) Modelling the kinetics of cooking and precooking potatoes. J. Food Sci., 53,3, 753-755. 4 Castler, B., Vetter, D., Gangi, A., Principes de I'IRM, Editions Masson, Paris, 1994. 5 Schmidt S. J., Sun X., and Litchfield J.B. (1996). Applications of magnetic resonance imaging in food science. Critical Reviews in Food Science and Nutrition, 36(4), 357-

385. 6 Sun X., Schmidt S.J., and Litchfield J.B. (1994). Temperature mapping in a potato using half Fourier transform MRI of diffusion. J. Food Proc. Eng., 17,423-437. 7 Harada T., Tirtohusodo H. and Paulus K. (1985) Influence of the composition of potatoes on their cooking kinetics. J. Food Sci., 50,463-468. 8 Lamberg I. and Olsson H. (1989) Starch gelatinization temperatures within potato during blanching. Int. J. Food Sci. Tech. 24,487-494.

Online Magnetic Resonance Imaging for Detection of Spoilage in Finished Packages Timothy W. Schenz,' Bany Dauber: Colin NichollsY3Craig Gardner? ,~ P. Roberts3 and Michael J. Hennesy4 Valerie A. S ~ o t tSteven

' ABBOTT LABORATORIES, ROSS PRODUCTS DIVISION, COLUMBUS, OH, USA

* ABBOTT LABORATORIES, ROSS PRODUCTS DIVISION, now with ITW, INC., ITASKA, IL, USA SMIS LTD, GUILDFORD, SURREY, UK INTERMAGNETICS GENERAL CORP., TECHNOLOGY DEVELOPMENT GROUP, TYNGSBORO, MA AND LATHAM, NY, USA

1 INTRODUCTION

With aseptic packaging becoming more prevalent and preferred by consumers, food processors increasingly are more concerned with the sterility of the finished packages.' While batch sterility may be confirmed within hours with state-of-the-art rapid microbiological methods, total individual package integrity may not be known immediately because of problems during sealing or secondary packaging finishing. Spoilage has traditionally been detected, after an appropriate period of incubation, by one of several means: statistical sampling of the batch, manual inspection of each container, or ultrasonic inspection. Statistical sampling requires destruction of some of the batch and affords much less than 100% inspection. Manual inspection is time-consuming and costly. Ultrasonic inspection requires that a transducer contact each container. This introduces considerable packaginghepackaging costs into a commercial process. In this paper we report the successful commercialisation of a spoilage detection technology using magnetic resonance (MR) imaging.* The technology has several advantages over existing ones: 1. The method is entirely non-contact, with the result that entire secondary packaging (e.g., cases) can be analysed without additional handling. 2. The process is very sensitive to the many types of food contamination. 3. The process is rapid, allowing for analysis of finished product at production rates. 4. Bulk inspection (vs. surface inspection) is possible. 5. Spatially-selective interrogation of the sample is possible via modification of techniques developed for clinical MR imaging, The principles of operation will be presented, including several novel advances that have made this online technology possible. These advances include use of a permanent whole body imaging magnet, a modified slice-selective CPMG (Carr-Purcell-MeiboomGill) sequence, and statistical algorithms for spoilage detection. These elements make the technology adaptable to almost any package configuration.

2

BACKGROUND

Products such as fruit juices, puddings, and milk are typically being put into aseptic packages. Because of the nature of aseptic packaging technology, failure rates due to spoilage

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are on the order of about 1 in 10,000 units? compared to retort terminal sterilisation failure rates of less than 1 in 1 m i l l i ~ nFor . ~ this reason, most foods (at least in the United States) sold in aseptic packages are either low pH or are marketed with refrigerated distribution to maintain the sterility of the commercial product. Spoilage rates of distributed products can be decreased by 1) improving the aseptic technology or 2) inspecting the containers for spoilage before distribution. Since there have been no major improvements in the former, we have focussed our attention on the latter approach to improving the quality of aseptically packaged products. This is especially important for value-added products that are designed for consumers whose state of health may be immunologically compromised. Any challenge by an ingested microorganism could lead to serious complications. The goals of this project were to develop an inspection system with the following criteria: 1. Detect most bacterial organisms that lead to food contamination. 2. Minimise package handling by inspecting cases of product instead of individual packages. 3. Provide speed of inspection capable of maintaining overall line speed of the primary packaging process. 3

SPOILAGE AND MR

Typically, as food products spoil, proteoly7 30 sis of proteins and hydrolysis of carbohy25 drates proceed with a resulting decrease in 6.5 the pH of the food. This decrease in pH has 20 * always served as a prime indicator of spoil8 age in food products. The detection of bac- % 6 15 terial spoilage by MR was first noted by 5 10 ; Schenz, et al.’ They discovered that MR 5.5 relaxation processes were affected by the 5 degree of bacterial spoilage in liquid nutri5 0 tional products. For example, Figure 1 0 2 4 6 8 10 shows the changes in the free induction deLog 10 Microbial Counts cay (FID) signal of a sample inoculated with Clostridium sporogenes as a function Figure 1 Changes in pH and FID during of the microbial counts and DH of the samspoilage ples as spoilage progresses. Clearly, the onset of spoilage, as indicated by the growth of the organism beyond lo4 counts and a decrease in the pH correlates with the change in the FID signal intensity from the sample. The same sensitivity to spoilage can be seen in the relaxation time (T2) value of a spoiled sample. Table 1 shows the effect on T2 value of a sample inoculated with BaciNus circulans. Thus, MR and its extension to MR imaging can be used as a sensitive non-destructive tool to evaluate the sterility of commercial products. Furthermore, many types of bacteria can be detected by this technology. Table 2 lists some of the bacteria that have been detected in inoculated product using this technique.

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Table 1 Effect of Spoilage on T2 Values of Liquid Nutritional Product Inoculated with B. circulans

% Change in T2 relative to sterile contro1

Day 1

Day 2

Day 3

-2.95

6.23

14.71

Table 2 Organisms Detected in Inoculated Product Using MWMR Imaging Aspergillis niger Bacillus cereus Bacillus circulans Bacillus megaterium Bacillus subtilis Bacillus stearotherrnophilus

4

Candida albicans Clostridium sporogenes Eschericia coli Lactobacillus casei Pseudomonas aeruginosa Staphylococcus aureus

SPOILAGE AND MR IMAGING

These MR results were the basis for detecting spoilage by MR imaging. The images shown below are spin echo images acquired at a field strength of 0.15T. The T2 of the normal product in the central bottles was approximately 150ms and the images were acquired using a TE of 200ms and a TR of 750ms resulting in T2 contrast. The images were acquired with a 25kHz bandwidth, have 128x128 pixels and 2 averages were performed resulting in an acquisition time of 3 minutes 12 seconds. The field of view of the images was 300mm and the slice thickness was 30mm. Figure 2a and b show the effects of B. cereus and L. casei, respectively, on a liquid nutritional product. In each case the sterile container is in the middle. Notice how different organisms display different spoilage patterns in the same matrix.

a.

b.

Figure 2 MR images of spoilage in containers by a) B. cereus and b) L. casei. Center container is sterile.

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5 SYSTEM DESCRIPTION The complete system, developed jointly by Intermagnetic General Corporation (IGC), SMIS Ltd. and Abbott Laboratories, was customized from existing hardware and software to meet the specifications of the production line.

5.1 Magnet and Coil The system is a customised Low Field (0.15T) Clinical Imaging System. It utilises IGC's unique permanent magnet featuring a large elliptical clear bore that is 69 cm wide by 56 cm high, and minimal fringe field. The magnet itself is completely non-conductive to eliminate the deleterious effects of eddy currents and improve image quality. This magnet is well suited for a commercial production line as it uses neither cryogens nor cooling water, has low power consumption (= 150 W) and is essentially maintenance free. The RF coil was optimised by IGC to maximise signal to noise and provide high homogeneity. The system is sited in an RF screened room through which a conveyor system passes. The conveyor was made from selected materials that did not interfere with the static and RF magnetic fields and which gave no background signals. The conveyor infeeds and outfeeds are via tunnels which act as waveguides below cut-off, hence screening the system from radiated interference at the frequency of interest (6.1 MHz). The screened room is maintained at a constant temperature which stabilises the magnetic field. Further provision for field stabilisation is via a field compensation coil and power supply. Other special modifications to the system include special attention to the electrical power supply filtering, a series of customised hardware and software interfaces to link with and control the conveyor movement system, provision for emergency conveyor stops and system shut down and remote access and control for error debugging, training and software upgrades. 5.2 Pulse Sequences A special MR inspection sequence based around a multi-slice multi-echo sequence was developed to gather spatial data from a case of product. The following sequence description assumes a case composed of cups ofproduct in a 4 wide by 3 deep by 2 high arrangement, shown schematically in Figure 3, where the terms slab and slice are defined.

' iw \

vvv

Figure 3 Schematic of case positioning

e Slice 2 $z:zs:b

+Bottom slab

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The sequence diagram is shown in Figure 4 and consists of the following steps.

1. The case is positioned so that the centre of slice 1 is in the centre of the magnet. 2. The bottom slab of the case is selected and excited by a 90" RF pulse (a) as shown in Figure 4. 3. A time delay (A) is included for staggering echo times. 4. The top slab of the case is selected and excited by a 90" RF pulse (b). 5 . After half of the desired echo time (Te/2), slice 1 of the case is selected and excited by a 180" RF pulse (cl). 6 . The resultant echo from the top row of slice 1 (Bl) is acquired under a read gradient. 7. The resultant echo from the bottom row of slice 1 (Al) is acquired under a read gradient. 8. A time delay is included for relaxation before the next spin echo. 9. Slice 1 of the case is then selected and excited by a second 180" RF pulse (c2). 10. The resultant echo from the bottom row of slice 1 (A2) is acquired under a read gradient. 11. The resultant echo from the top row of slice 1 (B2) is acquired under a read gradient. 12. This is repeated until five echoes have been acquired from each row. 13. The next slice of the case to be inspected (Slice 2) is then moved into position and the sequence begins again.

Te -

I

t

ai b

c2

CI

c4

c3

c5

SLI

81 A1

AA

A1

AL

A A

A2 82

83 A3

A4 8 4

85 A5

Figure 4 Schematic ofpulse sequence

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Although this particular example shows a method that requires several measurements to inspect a single case, other systems and sequences have been implemented which inspect the entire case with only one case stop.

0 statistical method called control chartingp ....................................................................... in which the running average of items analysed is kept and compared to the new0 a * a 0 est data point. The standard deviation of % * 0 = a * the initial measurements can be used to .% v) O O 0 0 quantify how far from the mean the new" *. est data point lies. One can then establish ....................................................................... limits, in terms of numbers of standard deviations, past which action is required.

...

+50

Time

-50

5.4 System Software A customised user interface has been developed which acts as a shell on top of SMIS' flexible research interface. The concept behind the shell is that there are three hierarchical layers which permit: 1) a plant operator to run the system without any knowledge of MR technology; 2) a plant supervisor to adjust some parameters in the inspection to optimise particular conditions or 3) an experienced MR system user to access the full flexibility of the system to design and develop new inspections. As part of the inspection process the system is able at user-defined intervals to perform self diagnostic tests. The operator is

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also able to randomly insert test objects into the process stream to verify proper system operation. In operation, the system monitors the quality of individual units within a shipping case and will cause any case containing a faulty unit to be rejected and be labelled indicating which unit fails to meet the quality standard. After the operator has logged on, defined the batch number and product type to be inspected and acquired the control set data, the system is fully automatic and the only window visible when the system is running is the window shown in Figure 6.

Current DatelTrme Status text Not Running Current Case Number:

0

Figure 6 Main operation display

The box on the right shows the current batch number, product family & type together with the case type (units long by units wide by units high by number of scans per case). The system status window indicates the “health” of 7 areas of the system - Bo, shim, RF power, X, Y and Z gradient strengths and conveyor belt. Normally each icon is just displayed as is. But if a diagnostic test is failed, the relevant Icon is shown against a yellow background. The system then attempts to automatically correct the fault by bringing back on resonance, reshimming etc. If it fails again it re-tries a pre-defined number of times and a failure at this stage stops the line and the pertinent icon is shown with a red line through it. The lower half of the screen shows date, time, the case number currently being inspected and what the system is currently doing (scanning, diagnostics etc.). Below that are buttons to permit the end of the batch, stop at the end of the current case, resume the inspection and stop immediately on the current slice. A supervisor is able to define the rejection parameters and inspection protocols for any given product family and product type. The supervisor may also adjust the instrument auto diagnostic parameters to ensure data integrity and correct diagnostic performance.

6 SUMMARY We have successfully commercialized a spoilage detection system that can inspect cases of product. The inspection is non-contact, rapid, sensitive and adaptable to many package

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sizes and materials. Use of this technology adds another safety level to existing aseptic processing.

References

1. Zink, D. L., Emerg. Infect. Dis., 1997,3,467. 2. US Patent Pending. 3. Cousin, M. A. and Rodriguez, J. H., in ‘Principles of Aseptic Processing and Packaging’, Nelson, P. E., Chambers, J. V. and Rodriguez, J. H., eds., The Food Processors Institute, Washington, DC, 1987, Chapter 4, p. 37. 4. Hui, Y. H., ed., ‘Encyclopedia of Food Science and Technology’, John Wiley and Sons, Inc., New York, 1992, p. 2555. 5. Schenz, T. W., Courtney, K. L., Israel, B. R., and Reaves, L. A., US Patent No. 5,270,650, 1993. 6. BBN Software Products, RS/QCA I1 Overview, Chap. 3, Bolt Beranik and Newman, Inc., 1990

Magnetic Resonance Mapping of Solid Fat Content of Adipose Tissues in Meat A. Davenel,' P. Marchal,' A. Riaublanc2 and G. Gandemer2 CEMAGREF, DEPARTMENT OF AGRICULTURAL AND FOOD ENGNEERING, 17, AVENUE DE CUCILLE, 35044 RENNES CEDEX, FRANCE * INRA, LABORATOIRE DES INTERACTIONS DES MOLECULES ALIMENTAIRES, BP 71627, 44316 NANTES CEDEX 03, FRANCE

1 INTRODUCTION Low consistency of adipose tissues from more and more pig carcasses causes technological problems in dried meat products manufacturing such as insufficient drying, rapid rancidity and lack of cohesion between meat and lard in cutting. Lipids makes the major contribution to adipose tissue consistency while the others components including collagen and water do not appear to have significant effects132 (Enser, Whittington). Consistency of adipose tissues is related to the physical state of the lipids which depends on their chemical composition3. Numerous studies have been devoted to fatty acid composition of adipose tissues495. The measurements of the physical characteristics of lipids such as melting point or slip point of adipose tissues gave too tedious parameters to be used for selecting adipose tissues697. The determination of the crystallinity of fats such as margarine, milk or cocoa butter8 by the measurement of solid fat content (SFC) with NMR relaxometry is a well-known method9. Precht et a1.10 proposed formulae to predict SFC of milk butter at different temperature from the triacylglycerol distribution. Davenel et a1.11 showed that the solid fat content measured at 20°C (SFC20) is strongly variable in lipids extracted from pig fat tissues. They showed that the SFC2O variability was closely related to that of the proportions of triacylglycerols with two saturated fatty acids (R2= 0.95) and more specifically to the proportion of palmitoyl-stearoyl-oleoyl-glycerol (R2=0.92). The measurement of solid fat content by NMR is fast to perform, so it could be an interesting method for selecting adipose tissues in slaughterhouses. The chemical composition of fat tissues in the same pig is strongly variable, hence further studies are required for localizing some typical sites of sampling in backfat tissues of pig carcasses. The solid-liquid ratio is negatively correlated to the liquid proton content in partial crystallized fats. MR imaging is potentially able to map the liquid proton density of subcutaneous or intermuscular fat tissues provided that non-uniformity of MR images from large coil is accurately corrected. The correction scheme based on the simple dividing of tissue images by a uniform oil phantom images acquired with a low field MRI system gives accurate quantitative density weighted image&.

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In this work we first verify that the NMR measurement of solid-liquid ratio on direct biopsy instead of the measurement of solid fat content of lipids extracted from adipose tissues remains highly related to the triacylglycerol distribution. Then, we show that the corrected liquid proton density-weighed MRI images can give a accurate mapping of the solid-liquid ratio in pig backfat adipose tissues and in intermuscular fat tissues from pieces such as shoulder, loin and belly . Consequently, the technique should be an useful method to study the variability of the quality of fat tissues in animal carcasses and to localize typical sites representative of the quality of each carcass. 2 MATERIALS AND METHODS

2.1. Pig samples Twenty two backfats were cut off from pig carcasses slaughtered at about 170 days and 90 kg mean live weight. Backfats and carcass pieces, loin, shoulder and belly, previously frozen for preservation, were stocked at 20°C for stabilizing in temperature and physical state during four hours before imaging.

2.2 MRI measurements NMR imaging was performed at 20°C on a Magnetom Open (Siemens, Erlangen) 0.2T whole body imager equipped with a body receive coil. Davenell2 showed that the correction scheme based on the simple dividing by a uniform phantom image acquired with a low field MRI system appears well adapted to obtain quantitative density-weighed images highly corrected from inhomogeneous profile so long as T1-weighting of tissues or reference images is limited. Indeed, if the method is efficient to correct received RF coil non-uniformity, it cannot retrieve transmitter RF field non-uniformity which leads to variation of flip angle resulting to variations of T1-weighting from one region to another. Seven transverse images were acquired in a same acquisition for samples and reference phantom with vegetable oil (T1=144 ms, TZ=112 ms) by using a conventional multislice SE (spin echo) sequence with an echo time of 15 ms and a repetition time (TR) of 500 ms. Dimensional characteristics of MR images are given in Table 1. Four backfats piled on wooden plates, skin below, were simultaneously measured (Figure 1) and the patient table was successively shifted from 210 mm to obtain 28 images of each backfat. Every image of backfat was divided by the corresponding images of the oil phantom.

Table 1 Dimensional characteristics of MR images for the different samples Samples

thickness (mm) distance (mm) FOV (mm)

matrix size

No.slices

backfats

10

30

225x300

192x256

4x7

loin

10

15

128 x256

128x256

14

belly

10

15

150 x 240

160 x 256

14

shou1der

10

15

188 x 300

188 x 300

14

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Each corrected image was multiplied by the mean intensity value of the central region of reference phantom to eliminate effects of weighting normalized images by relaxation parameters of phantom solution or oil.

2.2 NMR measurements After MRI measuring, 284 samples of adipose tissues (about OSg) were cut off from inner and outer layers of backfats at different anatomic locations. Samples, previously frozen for preservation, were stocked at 20°C for stabilizing in temperature and physical state during 30 minutes before solid-liquid ratio measuring with Bruker Minispec 20 MHz according to the IUPAC standard method13 based on the measurement on a free induction decay. The quantification of the very fast relaxation component attributed to protons in solid phase and the slower relaxation component related to protons in the liquid phase leads to the classical solid-liquid ratio (SLR) determination.

2.2 Triacylglycerol chemical analysis After extracting lipids from backfat samples first used for NMR measuring, an aliquot of melted lipid was dissolved in a mixture of chloroform/ methanol ( U l ; v/v) to obtain a 5 mdml solution. Molecular species of triacylglycerols were separated in 45 minutes by reverse phase HPLC using a linear gradient of chloroform in acetonitrile. Molecular species were detected with a light scattering detector. It was assumed that the molecular species of triacylglycerol have a similar response and results were expressed as a percent of total molecular species presentl2. 3 RESULTS AND DISCUSSION

3.1 Relationship between solid-liquid ratio and triacylglycerol composition in adipose tissues Twenty triacylglycerol molecular species were identified in lipids extracted from adipose tissues. Seven species accounted for 90% of triacylglycerols and three of them, POO, PSO and POL, for at least 75%. Trisaturated triacylglycerols account for a very small proportion of the triacylglycerols in pig adipose tissue. Pig adipose tissues contained about 10% water. Despite variable water and collagen proportions in backfat, we showed that 93% of the solid-liquid ratio measured with Minispec apparatus on direct biopsy could be explained by those of the proportions of three triacylglycerol species (PSO, PPO, PSL) containing two saturated fatty acids. We confirmed the previous results obtained on extracted lipiddl : the only one triacylglycerol PSO explained 91% of the variability of the solid-liquid ratio (figure 1).Le Mestre14 found that triacylglycerols in the solid phase at 20°C are mainly triacylglycerols with at least two saturated fatty acids and that these triacylglycerols crystallised in the p’ form with a high melting point. Because trisaturated triacylglycerols account for only a very small proportion of the triacylglycerols in pig adipose tissues, we deduce that the solid phase of these lipids contains a high proportion of triacylglycerols with two saturated fatty acids.

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40

-5 2d

35 30 25 20

15 10

20

30 PSO (%)

40

50

Figure 1 Good agreement between solid-liquid ratio measured at 20°C (SLR20) with Minispec apparatus on direct biopsy and the proportion of of palmitoyl-stearoyl-oleoylglycerol (PSO)in extracted lipids. 3.2 Relationship between the solid-liquid ratio and the intensity of the region of interest in the corrected images The correction by dividing the raw images of the animal samples by those of an oil phantom with the same TR provided a dramatic visual improvement in image uniformity. Furthermore, the image correction by an oil phantom should be a very efficient technique to quantify liquid proton density. The linear correlation coefficient between the solidliquid ratio of the 284 backfat biopsies and the intensities of the corresponding region of interest from corrected images was evaluated to 92% (Figure 2).

40 -

.E

35

--

30

--

25

_.

SLR20 = -0.043 SE15 + 66

0

-3

.-0-

2 20

--

0

cn

15 -~ 10 J 650

850

1050

1250

SE15 (arbitrary units)

Figure 2 Good agreement between solid-liquid ratio measured at 20°C (SLR2O) with Minispec apparatus on direct biopsy and the intensity SE1.5 of the corresponding ROI from the corrected images

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This linear relationship was eventually applied to give resulting images expressed in SLR20 units. The method appears well adapted to obtain quantitative density-weighed images highly corrected from inhomogeneous profile so long as T1-weighting of tissues or reference images is limited. We must pay attention that if the relaxation time TR of 500 ms should be sufficient to map the SLR20 of adipose tissues in carcasses, it would be increased to quantify correctly the liquid proton density in muscles. SLR values in MR images were clearly related to the composition of lipids in adipose tissues, so subsequently we consider that SLR20 mapping reflects the physical state of lipids in adipose tissues.

3.3 Variability of the physical state and chemical composition of lipids in pig backfat NMR images converted in SLR20 units display a strong variability of the physical state of lipids in the same backfat and show particularly the presence of two distinct layers of adipose tissues (Figure 3). These two layers anatomically separated by a thin layer of conjonctive tissues become visible by MR imaging because of the high SLR contrast. The average SLR20 of the internal layer was 5.7 % higher than this one of the outer layer : this increase corresponded with a PSO proportion in the inner layer 9 % above the outer one. The mean SLR20 in the collection of the twenty backfats ranged from 19 to 25 % and tended to increase with the backfat weight (R2 = 0.4). The increase of the backfat weight took place by way of a thickening of eack fat layer but more specially of the inner one. Almost missing in the thinnest backfats, the inner layer was developping rapidly with the increase of fat deposite particularly on the back and at the level of the shoulders.

C

;LR

ham

IB

ID

5

10

15 20

25

32 30 28 26 24 22 20 18 16 14 12 30 mm

Figure 3 Display of two distinct adipose layers by SLR20 mapping of 23 transverse MR images and two reconstituted profiles of a same backfat.

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35 30 25 20 15 10

-5

0

5

10

15

20

Thickness of backfat (mm)

Figure 4 The SLR20 variation in pig bacvat for three animals used in the experiment. The MR images show a systematic SLR20 increase in the outer layer from the skin to the connective tissue between the two layers. The SLR2O distribution in the inner layer of the adipose tissue did not change appreciably (Figure 4). At the junction of the two layers on the back and at the level of the shoulders, there was a large discontinuity in the SLR20 distribution. The observed discontinuity was in accordance with the distinct discontinuity on both sides in the fatty acid composition measured by ChristielS. This author analysed the lipids extracted of thin sections 0.3 or 0.6 mm thick cut parallel to the skin surface from backfat of three carcasses and established that the concentration of 18:O and 16:O saturated fatty acids on the interior side of the connective tissue were distinctly higher than the corresponding values observed on the outer side of the connective tissue. At the level of the ham and the belly, this strong variation was not observed. At these locations, the SLR20 increase appeared progressive from the skin to the muscle and no layer of conjonctive tissues is generally observable suggesting that these adipose tissues originated only in a thickening of the outer adipose layer.

3.4 Variability of the physical state of lipids in pig adipose tissues in shoulder, belly and loin These observations can be confirmed and completed by analysing the MR images from particular pieces of meat from pig carcasses. The presence of the two distinct adipose layers in backfat was clearly observable in transverse MR images from loin and shoulder particularly near the cut (Figure 5 ) where the discontinuity is largest. At the opposite site of the cut in shoulder, this discontinuity gave way to a gradual SLR20 increase from skin to muscle and adipose tissues were less saturated at this location.

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belly 4

12 14 16 18 20 22 24 26 28 30 32 SLR% Figure 5 SLR20 mapping of adipose tissues in pork loin, belly and shoulder. At the opposite site of the cut in loin, the internal adipose layer became localized under a thin muscle and could be assimilated to intermuscular adipose tissues. So the internal layer was no longer observable in subcutaneous adipose tissues in belly and these tissues were distinctly less saturated. Despite voxels at the interface with muscles or bones were affected by partial volume effects, SLR mapping of animal pieces could be a useful technique to investigate the physical state of intermuscular adipose tissues. In figure 5 these tissues appear clearly with SRL levels higher than those of subcutaneous external adipose tissues and comparable with those of subcutaneous internal adipose tissues.

4 CONCLUSION The NMR measurement of solid-liquid ratio on direct biopsy instead of the measurement of solid fat content of lipids extracted from adipose tissues remains highly related to the triacylglycerol distribution, particularly to the proportion of palmitoylstearoyl-oleoyl-glycerol. Then, we show that the corrected liquid proton density-weighed MRI images can give a accurate mapping of the solid-liquid ratio in animal subcutaneous adipose tissues and in intermuscular fat tissues from different pieces. The large variation of solid-liquid ratio reflected significant differences of chemical lipid composition between animals and between the two layers of subcutaneous adipose tissue. These layers perform the prominent role of serving as an energy store and as insulation for the animal. Fat synthesis and deposition are influenced by changes in environmental temperatures, dietary fat and anatomical location. At research level, the MRI technique can be relevant

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to study the effects of animal production system on fat repartition, fat synthesis and deposition.

Acknowledgment This work was funded in part by OFIVAL (office national interprofessionnel des viandes de 1'Clevage et de l'aviculture).

References 1. 2. 3. 4.

5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

M. Enser, E. Dransfield, P.D. Jolley, R.C.D. Jones and M. Leedman, J.Sci. Food andAgric., 1983,35,1230. F.M. Whittington, N.J. Prescott, J.D. Wiid and M. Enser, J.Sci. Food and Agric., 1986,37,753. J.L. Perrin and A. PrCvot, Rev.Franc.Corps Gras, 1986,33,437. J.P. Girard, B. Desmoulin, M. Bonneau and G. Gandemer, Rev.Franc.Corps Gras 1983,30,73. C.A. Morgan, R.C Noble, M. Cocchi and R. McCartney, J.Sci. Food and Agric, 1992,58,357. C.H. Lea, P.A.T. Swoboda and D.P. Gatherum, J.Agric.Sci. Cambridge., 1970, 14, 1-11279. J.D. Wood, M. Enser, H.J.H. Macfie, W.C. Smith, J.P.Chadwick, M. Ellis and R. Laird, Meat Sci., 1978,2, 289. P. Lambelet, C. Desarzens and A Raemy, Lebensm.-Wiss. u.-Technol., 1986,19,77. K. Van Putte and J . Van Den Henden, JAmer Oil Chem SOC,1974,51,316 D. Precht and E. Frede, Fett Wissenschafrtechnologie, 1996,324-330. A. Davenel, A. Riaublanc, P. Marchal and G. Gandemer, Meat Sci, accepted. A. Davenel, P. Marchal, A. Riaublanc and G. Gandemer, J Magn Res Analysis, accepted. IUPAC. 'Standard methods for the analysis of oils fats and derivatives' (6th edition). First supplement, part 6. Pergamon Press, Oxford. UK,1982 M. Le Meste, G. Cornily and D. Simatos, Rev.Franc.Corps Gras, 1984, 107. W.W. Christie, D. M. Jenkinson and J.H.Moore, J.Sci. Food and Agric, 1972, 23, 1125

Time Domain 'H NMR: Its Relevance to the Processing and Storage of Starch Systems I. A. Farhat,* J. M. V. Blanshard and J. R. Mitchell DIVISION OF FOOD SCIENCES, SCHOOL OF BIOLOGlCAL SCIENCES, UNIVERSITY OF NOlTINGHAM, LOUGHBOROUGH, LEKS LE12 5RD, UK

1 INTRODUCTION Starch forms the main proportion of the world's food energy intake. The understanding of the processing of starch is a very active research area and constitutes one of the major challenges for the food industry particularly in the areas such as breakfast cereals, starch based snacks, baked products, etc. In the native form, the starch polysaccharides, namely the highly branched amylopectin and the more linear amylose are packed in a semi-crystalline granular structure. The shape, size (between 2-100pm) and composition of the granule depend on the botanical source of the starch'. 1.1 Starch Processing

The processing of starch involves the presence of water and a source of heat andor mechanical energy such as shear in extrusion or high pressure processing. The gelatinisation of starch refers to the heating, in excess water, of the native starch granules, which are insoluble in cold water. The gelatinisation is reflected by a sudden increase in viscosity resulting from the swelling of the granules and the leaching of soluble polysaccharides (Figure 1). Further pasting leads to the breakdown of the swollen granules and therefore to a drop in viscosity. Additional processing may lead to molecular degradation and debranching yielding a decrease of the average molecular weight. In low water - high shear conditions, typical of extrusion processing, starch granules may be damaged by shear forces and consequently a state of filly swollen granules might never be reached. It is clear that starch conversion is a continuum. It is now widely accepted that the degree of starch conversion is the most important physico-chemical pro erty in defining many of the attributes of starch based food products. Indeed, Gu (1995) associated the degree of starch conversion with the sensory attributes of breakfast cereals, while Mitchell and co-workers3described the direct relationship between starch conversion and the extent of expansion during extrusion puffing of maize. Therefore, there is a growing effort in developing rapid methodologies to assess the degree of starch conversion.

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Degree of starch conversion

Schematic representation of the mechanism of starch conversion: Figure 1 I : native g r m l e s . 2: gelatinisation: maximally swollen granules and leached soluble polysaccharides. 3: granules breakdown: polymeric structure with possible debranching. The behaviour of the water absorption index, e.g. the amount of water in the granules (WAI) which measures the size of the granules, the water solubility index which indicates the amount of soluble po&saccbarides and the viscosity in alkali which reflects the average molecular weight are shown. 1.2 Ageing of Processed Starch

The mechanism by which processed starch systems change on storage depends greatly on the ageing conditions in relation to the glass-rubber transition. This hrther complicated by water and temperature gaidloss, which could lead to the system undergoing the glass-rubber (or rubber-glass) transition (Figure 2). In the glassy state, the molecular mobility of the starch biopolymers is greatly reduced and consequently the ageing of is believed to result from localised motion. This sub-Tg ageing is often described in terms of enthalpy relaxation associated with a reduction of free volume4. In the rubbery state however, the degree of molecular mobility is enhanced by the higher temperature and the presence of plasticizers such as water, sugars, etc. The ageing of converted starch takes place through the phenomenon often referred to as retrogradation or staling. X-ray studies by Katz’, as early as 1928, revealed that retrogradation is best described in terms of the recrystallisation of the amorphous gelatinised starch. Although a range of techniques have been employed to study the kinetics of starch recrystallisation, wide angle x-ray diffraction (XRD) constituted the most extensive source of information. However, XRD and many other techniques widely used in retrogradation studies, particularly rheological methods, are often time consuming, do not readily offer means of controllin the samples temperature and water content, and yield in many cases irreproducible data .

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1.3 Objective

This paper reviews 3 examples where NMR relaxometry performed on a basic benchtop spectrometer operating at a single temperature provided usefil information regarding 3 aspects of starch technology: (i) starch conversion during extrusion processing, (ii) starch ageing through retrogradation and finally (iii) the hydration of a processed starch-sugar mixture.

"1

:

Rubbery state =>

water content % (wlw d.s.b)

Figure 2 Storage conditions of processed starch systems in relation to the glassrubber transition line. 2 NMR MEASUREMENTS

All 'H NMR experiments were performed using a Bruker bench top PC120 (20 MHz) Minispec operating at 4 0 . 1 " C with recycle delays typically of 1 to 2s. The FID was fitted to 2 gaussians: a solid-like component with a T; of a few tens of ps and a liquid-like component with a Tz of a few hundreds of ps. The spin-echo decay acquired with a 9Oo-18O0 pulse spacing of 262 ps was fitted to a single exponential. The spin-latticerelaxation times (TI) were measured using the inversion-recovery pulse sequence with twenty 180"-90" pulse spacing values (2 to 4096 ms). The amplitudes of the FID recorded at 11 and 70 ps were used to describe the TI of the rigid (yll - y70) and the mobile (y70) components respectively. The inversion recovery signal was best described as a single exponential. In the majority of cases reported in this paper, 0 the y70 components suggesting a comparable TI values were recorded for the ~ 1 1 - ~ 7and cross-relaxation process.

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3 STARCH CONVERSION

3.1 Sample Preparation

Waxy maize starch, w m s (National Starchesand Chemicals Co.,Manchester, UK) was extruded using a Clextral BC-21 co-rotating, intermeshing twin screw extruder at 120°C. The water level was adjusted to obtain non-expanded extrudates containing 43% water (w/w dry solid basis). The samples were sealed and stored at room temperaturefor 4 weeks allowing “fiJll” retrogradation as measured by XRD6. This was performed in order to deconvolute the effect of crystallinity fiom that of the processing history. The sample were then dried (vacuum oven 7OoC,15 hours), ground and rehydrated over saturated salt solutions. The water vapour sorption isotherms (Figure 4) showed, for RH values greater than 45%, a higher water uptake for the processedretrograded samples compared with native non-extruded waxy maize starch. 40-

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3.2 Results and Discussion

The plot of TI versus water content showed a pattern very similar to the familiar TI versus temperature plot with a clear minimum at -15% water (w/w d.s.b). At this water content, Tgvalues of 60°C and 70°C were reported for partially crystalline starch by DSC and spin-spin NMR relaxometry respectively’. Therefore, the TI minimum is occurring at approximately 30°C below the Tg measured by DSC. This phenomenon has been observed for other biopolymer systems* and is attributed to the fact that water retains a relatively high of mobility in biopolymer-waterglasses. The effect of processing on starch was clearly detectable in both spin-lattice and spin-relaxation results (Figure 4). The extruded then retrograded system showed TI values consistently smaller than the native control. Another interesting feature of the spin lattice relaxation results is the broader minimum of the plot of TI versus water

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content for the processed sample which indicate a broader distribution of correlation times around the value T ~ -5. lo-' s calculated using the BPP equation. The CPMG spin-spin relaxation times of the extruded sample were slightly higher than the control values particularly for water contents >15%. The decrease in the TZ at high water content could be related to the onset of fast exchange.

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Figure 4 Eflect of the processing on (a) the TIand (b) the Tzfor a range of water contents. The empty triangle in the TI plot represents the value measured on the wet sample before drying, The lines in both charts were added to ease viewing. 4 STARCH RETROGRADATION

4.1 Sample preparation

Non-expanded wms-water systems were extruded as described earlier. The samples were stored in sealed 8 mm NMR tubes at 4OoC +O. 1 in the spectrometer probehead. 4.2 Results and discussion

Both the spin-spin relaxation parameters recorded from both FID and CPMG decays and the spin-lattice relaxation times showed a strong dependence on the duration of storage. The implication is that the NMR properties were affected by the retrogradation process and by the extent of the reordering of the gelatinised starch'. The recrystallisation of amylopectin was accompanied by a decrease of the TZ of the solidlike component of the FID (Figure 5a) and an increase of the contribution of this same component to the total signal (Figure 6b) indicating that the increased crystallinity leads to a reduced molecular mobility. The increase of the Tz of the liquid-like component of the FID (Figure 5a) with the progress of retrogradation could be explained by the fact that an increasing population of starch protons that had a liquid-like behaviour in the freshly gelatinised starch becomes more rigid in the recrystallised system and contributes therefore to the rapidly decaying component6. The total NMR signal showed no dependence on the storage time indicating that, within the experimental error limits, there was no noticeable moisture change (Figure 5b). The spin-echo TZmirrored that of the solid component of FID. This behaviour is in agreement with the theoretical

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calculation relating the spin-spin relaxation rates of the water to that of the rigid polymer matrix lo. 425

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FID Tin-pin relaxationparameters as afinction of storage time (400C) Figure 5 for a 100:60 wmshvater extruhtes. The T1 results recorded on a comparable system (wmdwater 100:65) increased with retrogradation (Figure 6b). This behaviour is somewhat surprising, particularly that these systems, in the experimental conditions of this study are on the right of the TI minimum (TIversus temperature or water content), e.g. were T1 shows a positive dependence on molecular mobility. No significant difference was found between the relaxation times of the component recorded at llps and that at 70ps suggesting that water provides a relaxation path for the starch protons'.

Figure 6 Changes in the relaxation times (a) T2for a 100:60, and (b) TI for a I00:65 wmshvater extruhtes during retrograahtion.

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The changes in all NMR relaxation parameters due retrogradation reached plateau values after approximately 20 h. This kinetic is in agreement with wide angle xray results obtained on the same samples (Figure 7).

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XRD spectra recorded during the ageing-retrogradtion (25+2 T)of a Figure 7 100.60 wmshvater extrudale.

5 HYDRATION OF MIXED SYSTEMS

5.1 Sample preparation

Maize grits (Maizecor Foods Ltd.) were used for this study. The specifications from the supplier indicated 8-9.5% protein and a maximum 1% lipids. The starch was composed of approximately 25% amylose and 75% amylopectin. Maize and maize-sucrose 100:10 were extruded at 22 YOwater (wlw d.s.b) and a temperature of 170°C. After the expanded extrudates had been cooled, they were ground to fine powders, dried and rehydrated as described earlier lo. 5.2 Results and discussion

For water contents 47% for the maize and <10% for the maize-sucrose systems, there was a linear relationship between the ratio of liquidsolid amplitudes of the FID and the water content (Figure 8a). While the slope of this dependency (1.794) is in agreement with the value calculated based on the 'H densities of water and the maize components (1.786), an unexpected negative intercept leading to a value of approximately 2% water for a liquidsolid ratio of 0 was observed". This suggested that the first 2-3% of added water are tightly bound to the solid matrix and could contribute to the signal arising from the rigid-lattice. Another reason for this observation could be that the moisture contents were inaccurately defined since they could have been based on uncomplete drying. As the water content increased, the liquidsolid ratio measured on the maize-sucrose sample became progressively larger than that obtained for the sample with no sucrose (Figure 8a) indicating the progressive solvation process of sucrose and its subsequent participation to the liquid-like signal. This suggestion is supported by the shorter TZvalues recorded for the liquid-like component at water contents above 10% (Figure 8b). The TZ values were thought to be shortened by the increase of the viscosity of the aqueous phase through the presence of sucrose.

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287

These results are in agreement with the water vapour sorption isotherms of amorphous sucrose and extruded maize (Figure 9) where for RH values higher than 45% the affinity of water for sucrose is greater than that for maize. The value of RH=45% corresponds to water content of approximately 10%.

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Figure 8 (a) Liquid/Solid ratio and (8) TZof the liquid-like component of the FID as a finction of water content for maize and maize-sucrose 1OO:IO. The solid line in (a) represents the expected behaviour taking into account the 'H densites of the various components, and assuming water onb constitutes the liquid component of the FID. Adpedfrom {lo].

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Figure 9 Water vqour sorption isotherms (25 T) of amorphous sucrose and extruded maize lo.

CONCLUSION 'H NMR relaxometry can probe the changes in molecular dynamics during the processing, storage and hydration of starch based systems. It provides a molecular understanding of starch transformation, such an insight is crucial for starch technologists. The technique offers many advantages such as: reproducibility, little sample preparation, non-destructive analysis, easy control of measurement conditions (temperature, water content), rapid analysis, potential for automation and on-line

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analysis, etc. and last but not least, benchtop spectrometers are widely present in the food industry and only minor software modification would be required to perform analysis similar to those reviewed in this paper. It is however envisaged that the analysis might become less specific as the complexity of the system investigated increases. References 1 . J. Jane, T. Kasemsuwan, S. Leas, H. Zobel, and J.F. Robyt, StarcWSttiirke, 1994, 46(4), 121. 2. J. Fan, J.R. Mitchell and J.M.V. Blanshard, 1996,Znt. J. Food Technology, 31,55 3 . R. Guy, 1995, Proceedings of the ‘Extruded Cereal Products: Their Creation and Evaluation’workshop, March 1995, Sutton Bonington, University of Nottingham

4. R.L. Shogren, 1992, Carbohydrate Polymers, 19,83.

5. J.R. Katz, ‘A Comprehensive Survey of Starch Chemistry’, Ed. R.P. Walton, The Chemical Catalogue Company Inc., New York, 1928, Vol. 1, 100. 6. 1.A Farhat, PhD Thesis, University of Nottingham, 1996

7. M.T. Kalichevsky, E.M. Jaroszkiewicz, S. Ablett, J.M.V. Blanshard and P.J. Lillford, 1992, Carbohydrate Polymers, 18,77 8. S. Ablett, A.H. Darke, M.J. Izzard and P.J. Lillford, ‘The Glassy State in Foods‘, Eds. J.M.V. Blanshard and P.J. Lillford, Nottingham University Press, 1993, p. 189

9. 1.A Farhat, J.M.V Blanshard, J.L Melvin and J.R. Mitchell, ‘Starch Structure and Functionality‘, Eds. P.J. Frazier, A.M. Donald and P. Richmond, The Royal Society of Chemistry, Cambridge, 1997, p.86 10. S.F. Tanner, B.P. Hills and R. Parker, 1991, J. Chem. Soc. Faraday Trans., 87(16), 2613. 1 1 . 1.A Farhat, J.R. Mitchell, W. Derbyshire and J.M.V Blanshard, 1996, Carbohydrate Polymers, 30,2 19.

Subject Index

Adenosine diphosphate, 228, 230 Adenosine monophosphate, 228 Adenosine triphosphate, 230 enzymatic degradation, 227 Adipose tissue, solid fat content, 272-279 Agarose, gel porosity, 84 gelation mechanism, 21 Alanine, 230 Alternating least squares regression, 22 1, 223 Amylopectin, glass transition, 80 recrystallisation, 284 Analysis of variance, 203-205,209,213 ANOVA, see Analysis of variance Antioxidants, 126 Apple, 61, 84, 149 Apple pie, imaging, 11 Arabinans, 155 Arabinose, 166 Aroma, 85 Aspergillus niger, 86,266

Bacillus cereus, 266 Bacillus circulans, 265, 266 Bacillus megatherium,266 Bacillus stearothermophilus,266 Bacillus subtilis, 266 Banana, 83 Beans, 158 Beef broth, 85 Biopolymer fluid gels, 21 Biopolymer systems, water in, 45-62 Bitterness, 85 Black tea extract, 5 Bread, 281 quality, 126 Breakfast cereals, 280,281 Butter, 205-207 Cabbage, 149, 155 Candiahalbicans, 266 Carbon-13 NMR, 9, 80, 85, 126-134, 138,

144-157,164,167, 185-192 Carrots, 61 canned, 149, 150 cooked, 247 dried, 153-155 Carr-Purcell-Meiboom-Gill pulse sequence, 19,21, 50, 51,69, 73, 80, 85, 96,205,218,223,284 Cassava, 85 Cellulose, 166 PSRE method, 146,148, 149 Cellulose-polysaccharide interactions, 151 Cheese, 84 soft, internal structure by MRI, 24-34 Chemometrics, 217-225 Chicken muscle, 249 soup, 85 Chinese water chestnut, 177-179 Citrus cell walls, 159 Clostridium sporogenes, 265,266 Cod, 234 frozen, 85 Continuous wave methods, 4-6,195 Convective heat transfer coefficient, 84 Corn starch, 58-60 Correlation length, 117 Couchman-Karasz equation, 64, 86 CP, see Cross-polarisation CPMG, see Carr-Purcell-Meiboom-Gill pulse sequence Cream, viscosity, 84 Crispness, 105 Cross-polarisation in hydrated systems Cross-polarisation, 15, 80, 161-163, 167, 185-1 92 Crunchiness, 105 Cysteine, 136 Cytochrome C, 136

DETA, see Dielectric-thermal analysis Deuteron NMR, 84. 87

290

Dielectric permittivity, 106 Dielectric-thermal analysis, 105-1 10 Differential scanning calorimetry, 80, 87, 101, 185,186 Diffusionmeasurements, 8,3542,242, 253 Discriminant analysis, 26, 29, 32 Disulphide bonds, 128-1 30 DMA, see Dynamic mechanical analysis DMTA, see Dynamic mechanical-thermal analysis Durbin-Watson statistic, 204, 208 Dynamic mechanical analysis, 185, 188 Dynamic mechanical-thermal analysis, 64, 105-1 10 Dynamic nuclear polarisation, 197 Early instrumentation, 4 Egg custard, imaging, 11 Egg white, 249 Viscosity, 84 Electron magnetic resonance imaging, 193-200 Electron spin resonance spectroscopy, 135-1 43 longitudinallydetected, 197 Escherichia coli, 266 Exponential fitting, 2 19 Fat crystallisation, 20 solid, in adipose tissue, 272-279 solidlliquid ratio, 5,20 FID, see Free induction decay Fish changes during ice storage, 234 fadwater content, 222 lipids, 137-143 quality assessment, 228 Fluorescence spectroscopy, 135, 137, 142 Food appearance, 85 biopolymers, NMR, 115-125 compositional complexity, 81 dynamical complexity, 81 emulsions, stability, 8 high temperature processing, 241 macroscopic mobility, 80 molecular mobility, 80

Advances in Magnetic Resonance in Food Science

packaging, aseptic, 264 physical properties, 79-94 sensory properties, 79-94 spoilage, 264-271 stability, 7 1 structural complexity, 8 1 Formate, 230 Fourier transform techniques, 9,25, 97, 99, 209-2 11 Free induction decay, 51-53,57,69-71, 96,98, 100, 174, 190,211,213-215, 265,284,286 Free radicals, 135, 136 properties, 193 Freshness test strips, 229 Freshness Test Paper Kit, 234 Fresh Tester, 234 Fructose, 83 Fruit juices, 83 Fruits, 85 Galactomannan, 151, 152 Galactose, 166 Galacturonans, 155 Galacturonic acid, 166-177 Gelatine, 52, 73-78 Glassy state, 105, 116, 281 Glass-rubber transition, 111, 116, 123, 188,281 temperature, 64,64,67, 80, 84, 100 Glucose, 83, 155,230 Glutamine, 127-133 Gluten, 126 Glycerol, 63-72 Glycine, 127, 230 Goldman-Shen sequence, 122,211-21 5 Gordon-Taylor equation, 86 Haemoglobin, 136 Halibut, Atlantic, quality evaluation, 226-235 Hartmann-Hahn cross-polarisation, 159 Hemicellulose, 166 Histidine, 136 HPLC, 138,228 Human panellists, 82, 90 Hydrated carbohydrate systems, 95-1 04 Hydroperoxides, 136 Hypoxanthine, 227,228

Subject Index

Ice cream, 83 Imaging ESR, 193-200 magnetic resonance, 10-14,24-34,81, 193-200,242-255,256-263,264-271

Inosine monophosphate, 228,230 Instant corn starch, 90 Iodine value, 208-21 Ionic mobility, 56 Ion solvation, 56 K-value, 232 Kuhn segment, 117-1 19 Lactobacillus casei, 266 Lactose, 80 Lipid oxidation mechanisms, 135 Lipid solid fat content, 83 Lipids, oxidised, analysis, 138 Lipid-protein interactions, 136 Locust beangum, 151,152 Luz-Meiboom relation, 75 Lysine, ESR spectra, 139 Lysozyme aggregation. 3 5 4 2 ESR spectra, 140

McCall-Douglass equation, 172 Mackerel myosin, ESR spectra, 141 Macrostnrcture, 84 Magic angle spinning, 10,83,126-133, 158-165, 167, 185-192,228 Magnetic resonance mapping, solid fat content, 272-279 Maize starch, 80, 191 puffing, 280 Maltodextrin, 80 Maltose, 80, 100-102 Margarine, 205-207 MAS, see Magic angle spinning Methionine, 136 Methylgalacturonic acid methyl ester, 167-177 Methyl linoleate, 137-139 Microheterogeneity length, 76 Microstructure, 84 Mobility-resolved spectroscopy, 144 Modulus of mechanical loss, 110 Molecular rigidity, 151, 155

29 1

Multiple linear regression, 221 Myosin, ESR spectra, 141, 142 NADH', 230,234 Nitroxyl radicals, 193 Non-Brownian behaviour, 77 Nuclear magnetic resonance, multidimensional, 121 Oilseeds, 83 Onion, 149,161-164 Outer product analysis, 205,215 Ovalbumin, ESR spectra, 140 Oxygen- 17 NMR, 84 Pake pattern, 99 Palm kernel oil, hardened, 20,21 Partial least squares regression, 204, 208-210,213,223 Pasta, dried, imaging, 12 Peas, 158 Pectin, 166 PFG, see Pulsed field gradient Phase mapping, 250 Phosphate, inorganic, 228 Phosphocreatine, 228,234 P ~ o s P ~ o ~NMR, u s - ~9~ Physical properties of food, 79-94 Pig tissue, 249 adipose, 272-279 Pineapple, 149 Plaice, 234 Plant cell walls, 166-184 Plant oils, 208-21 1 PLS, see Partial least squares regression Polyacrylamide, viscosity, 84 Polymer behaviour, molecular basis, 116 Polymer chain rigidity, 111 Polymers reptation model, 117 tube model, 117 Polymer-water system, phase diagram, 123 Potassium-39 NMR, 90-92 Potassium salts, sensory attributes, 90 Potatoes, 158 cell walls, 177-1 82 crisps, 6 cubes, 84 temperature mapping, 244,250

292

Potato starch, 48, 50, 56, 59, 84 gelatinisation, 256-263 Principal components analysis, 26,28-32, 220,221 Proline, 127 Proteins aggregation, 35-42 damage by free radicals, 136 shape, 35-37 structural determination, 9 Proton mobility, 158-165 Proton NMR,5,38,84,96, 106-1 10, 166184,217-225,226235,280-288 Proton reference fiequency, temperature dependence, 249-2 53 Proton spin relaxation spectroscopy, 144-1 46 Pseudomonas aeroginosa, 266 Pseudo phase diagram, 115 PSRE, see Proton spin relaxation spectroscopy Pullunan, 100-102 Pulsed field gradient experiments, 35-42 Pulse sequence, for h4R imaging, 267 q-space formalism, 58-61 Raisin, 102 Relaxation phenomena, thermodynamics, 105-1 12 Relaxation ratedtimes, 6, 7, 21, 45, 49, 55, 63,65,69,73-78,88,95-104, 106,120, 129, 131, 132, 144, 158, 166184, 217-225,245-248,253,257,282-285 Rhamnose, 166 Rheology, 12, 13, 84 Ribose, 228 Root mean square error of cross-validation, 22 1,223 Ross equation, 55 Rubbery state, 105, 1 16,28 1 Salmon, 2 19 Atlantic, 226, 234 fadwater content, 222 Saltiness, 85, 90 Seafood, aroma, 85 Sensory properties of food, 79-94

Advances in Magnetic Resonance in Food Science

Sensory vs. instrumental measures, 85 Shear cell, description, 1 6 1 8 Signal decay shapes, 97 Single pulse excitation, 126, 129 magic angle spinning, 167 Slichter-Ailion region, 1 72 Sodium-23 NMR,84, 85, 90 Sodium ion binding, 90 Sodium ion mobility, 84 Sourness, 85 SPE, see Single pulse excitation Spinach, PSRE method, 146, 149 Staphyllococcus aureus, 266 Starch, 185-192 crystallinity, 66 gelatinisation, 256-263, 280 mobility, 84 processed, ageing, 28 I processing, 280,283 retrogradation, 284 Starch-lignin mixtures, 21 1-21 5 Starch systems, hydration, 286 Stokes-Einstein equation 242 Storage modulus, 107 Strawberry, 149, 155 Sucrose, 83 Sunflower oil, 20 Superoxide, 136 Sweetness, 85 Taste, 85, 89 Taurine, 230 Temperature mapping, 24 1-255 Temperature, non-invasive measurement, 24 1 Tomato, 153 Tomato sauce, viscosity, 84 Trehalose, 85 Triacylglycerols, 10,272,274 Trimethylamine, 234 Trimethylamine oxide, 228,230,234 Tripalmitin, 10 Trout, Rainbow, 234 Tyrosine, 127 Vegetable oils, 208-21 1 Vegetables, 85 Viscosity, 84

Subject Index

Water activity, 50, 65, 66 binding, 6 bulk state, 47 difkivity, 53 droplets, size distribution, 8 mobility, 63-72, 84 multilayer state, 47 multistate formalism, 45, 57 relaxation rate, 49,55 self-diffision coefficient, 87, 89 Waxy make, 63-72 Wheat, 126-134

293

flour, 90 grains, imaging, 12, 13 starch, 105-1 12, 186-191 Wines, 83 X-ray diffraction, 67, 68, 286 Xanthan gum, 90 Xerogel, 107 Xyloglycans, 15 1 Young’s modulus, 68 Zimmermann-Britten model, 6