DEVELOPMENT OF METHODS TO CHARACTERISE MASS TRANSFER

BEHAVIOUR OF PLANT TISSUES DURING OSMOTIC DEHYDRATION

A Thesis

Presented to

The Faculty of Graduate Studies

of

The University of Guelph

by

CYBELLE MARIE O. FERNANDEZ

In partial fulfilment of requirements

for the degree of

Master of Science

Mach, 2000

O Cybelle Marie O. Fernandez, ZOO0

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ABSTRACT

DEVELOPMENT OF METHODS TO CHARACTERISE MASS TRANSFER BEHAVIOUR OF PLANT TISSUES DURING OSMOTIC DEHYDRATION

Cybelle Marie O. Fernandez University of Guelph, 2000

Advisor: Professor Marc Le Müguer

The application of osmotic deh ydration as a pre-processing technique in food

processing has not been fully utilised becüuse of the lack of appropriate models that could

be used For designing processing parümeten and equipment. This study riimed to

contribute to these needs by developing methods that describe the mass trünsfer

behaviour of plant materials treated in osmotic solutions. Macroscopic and microscopiç

approaches were employed to describe the changes thüt ocçur in the tissues during the

process. An in-depth study of apple tissues led to a method of classification thüt describes

the rate of dehydration of vürious plant materials. This method took into account the

initial states of the material and the various mas trrinskr resistances from the solution

and the material itself. These behaviours were also verified with microscopy and image

analysis. The rneasurements of the volumes of the cells are facilitlited by the drve lqmrnt

of an image analysis program that automatically detects the ceIl's edges and measures the

volume in three-dimension.

ACKNOWEDGEMENTS

This work is a product of the collaboration of many professors. reseürchrrs.

scientists, and students who shared their time and expertise and contributed to achieve the

goals of this research. This thesis would also never have been completed wirhout the

support of so niüny people wlio hdpéd in one way or the oiiier.

The biggesi penon behind al1 these is none other than my advisor. Dr. Marc Le

Maguer. whose brilliünt mind never failed to challenge me. Althoiigh. his qualiries as LI

person and as a true advisor in so müny aspects. trünscends his intellect. His high

standards of excellence. which could be very hard to achieve. at times. became attüinnblr

because of his unwrvering support throughout a11 these years. What 1 am most gr;iirtul

for. I believe. is that he never gave up on me. It is this belief and patience that hrlprd me

encourage rnyself to keep going despite of whüt sometimes seerned to be insumountable

obstacles that 1 had to encounter these p u t three and a half yeürs.

1 am also very grateful to the members of my advisory cornmittee who have bern

very patient; their help and encouragement have been sources of inspiration. I thank Dr.

Dennis Murr for always taking the time out to help me and discuss with me the plant

physiology aspect of this work. To Dr. Robert Lencki. for his valuable and chiillenging

comments.

To Dr. Donald Mercer, who helped smooth a lot of wrinkles by patiently reading

the t'irst draft without complaining that he had to read Chapter 5 before Chapter 3. His

guidance during the last stages of this research has k e n very valuable. He dso made sure

that 1 did not give up on this by not giving up on me. Again. I feel blessed to have

someone like hirn around especially during those difficult times. My sincerest

appreciation also to Dr. Yukio Kakuda. for chairing my defence and for allowing the use

of the HPLC equipment, as well as for the after-hours advice.

To my fellow mernbers of the osmotic dehydration research group, my heartfelt

gratitude:

To Gianfranco Mazzanti, who was my CO-researcher not only on osmotic

dehydration but also on life; my gratitude for the countiess favours, professional and

personal, that he has extended. To Dr. Sarah Wilson. for the statistical and technical

advice but most of dl, for being a greüt listener as my lifeline during troubled moments.

To my ever-reliable assistant. Julie Jee. who patiently prepared the hundreds of apple

slices; nin the experiments and still remained my friend. To Dr. John Shi. for his

technical assistance: to Dr. Hoy Chu, for his assistance in image analysis as well as in the

other aspects of this work.

1 am dso grateful to the following scientists and researchers:

To Dr. Sandy Smith, for staning me up on microscopy and image analysis. She

hûs dways been there ready to help in any way that she could. To Andrew Moore of

Laboratory Services Division. for his assistance in the image acquisition and analysis.

To Dr. John Greenwood of the Department of Botany, who introduced me to the

many aspects of the science of Botany and Microscopy. To Dr. Lany Peterson, and his

liiboratory staff. for helping me with the LSCM work. Their lsboratory has always been a

joy to work in and 1 am so thankful to al1 of them for sharing it with me. Special thanks

go to Dr. Yukari Uetake, who did not give up when the first images we got were rlmost

nothing but a blank screen. Her expertise has led to the development of the protocol we

needed for the acquisition of the images. To Dr. Melissa Farquhar. the manager of the

LSCM facility. for her valuable comments and suggestions. To Lewis Melville. for the

countless hvours, technical help and most of all, for the always-lively conversations.

To our col laborators at the Department of Computing and Information Sciencrs-

to Dr. Stefan Kremer. who enthusiastically replied to my advertisement and for his

guidance in the development of the image ünalysis progrnm. To Damiaan Habets. who

developed the prognm; rny sincerest appreciation to both of them for al1 the assistance

and for such a fniitful collaboration.

Of course. to the rnüny people at the Department of Food Science for their

technical and mord support. To Dr. Müssimo Marcone. for ülways being ready to assist

in the laboratory work. Most of all. I am very grateful for his friendship and prüyers. To

Dr. Rickey Yada. who helped me out from a tight situation from the beginning of my stay

in Guelph. His support for my student's rights as well as to rny volunteer work has made

a whole of lot of difference to my stay in Guelph. To Francie Niekamp. for lier valuable

suggestions on the staining rnethods as weil as to her technical assistance in the Iüb. To

the staff at the main office especially to the following: to Margaret Wdmsley. for

facilitating al1 rny registration and financial needs. To Donna Motayne. for speeding up

our requisition forms and for the nice tnlks. To Linda Petemnac. for al1 the countless

assistance she has extended.

To Dr. Mansel Griffiths, for his time and advices on the other microscopy aspects

of the work. Also, for introducing me to Dr. Le Maguer and so, that was how ail these

stmed. My thanks as well to his research associate. Dr. Luba Brovko. for her assistance

in the microscopy side and for translating an article for me.

My sincerest thanks also go to the many wonderful people who have worked with

Dr. Le Maguer: to Brenda Bailey. who was his former secretary in Food Science: to the

former staff of the Office of Research in Omafra, most especially to Pat Spence. for the

assistance she has extended in so many ways and for being such a good friend. Also. to

Rhonda Algiers and Rosanna Miller who were always very helpful and encouraging;

many thanks to the wonderful conversations. as well. My gratitude also goes to Mrs. Ivy

Le Maguer. who in the short time that 1 have known her. has been very encouraging and

understanding.

To the great friends that I have made during my stay in Guelph:

To my best friend Hong Wang. who shüred so much of life with me thesr p u t

three and a half yeaa. To Oyie Umüli. who has been such ü d e x frirnd and advisor. no

matter where she is in the world. To Jola Majak-Siwik. for taking ciire of me and for

worrying about me always. To Mark Yoshimasu, for the fun times and for bring

concerned about me. To Andy Cao. for the Company during those very Iüte nights whcn 1

was writing this thesis. for many other things. To Nick Petropolous. for being there when

I needed him the most.

To my 7 College family who were the best housemates in the world: my gratitude

most especially to Jackie who generously shüred her room when I still have to trek down

to Guelph from Toronto: to Julie who is such an angrl of a friend: to Noel. wh« waz ;I

much-needed sounding board; to Marieka, whose gentle ways I miss greütly: to Mary

who is always a wondefil cornpanion and a great source of optimism; to Kathleen. for

making my trips to Guelph and back to Toronto so much more comfortable.

To my childhood friends from the Philippines who always remind me of warm

sunshine and cool breezes; especially to Deb (my seatmate for life). to Topsie, Jimmy.

Patrick, Carina and to Giselle.

To my family, for the tremendous love and support al l these yearu: to rn y Mnther

for her constant prayers and faith in me. To my one and only sister Joy. and to the four

greatest brothers in the world: Ariel, Ruel. Philip and Diofel and to thsir br t t r r hti lvcx

lim John. Laura, Penny. Gemma and Ninia. for the many. many ways that they have

helped me. To my nephews and nieces: JJ. Clithy. Arirl James. IR. Mahprl. Johnna-lrnrt.

Lianne, Lara and Dori. for al1 the fun cimes.

My deepest gratitude ûlso goes to the Zarate and Onas hmilies for thr i r

unwavering support and confidence in me: especially to my Aunt Pering mi tu iny

cousins: to Dionex, whose enthusiasm for anything I achieve, no marter how littla. is

always inspiring; dso to Janice. Juliet, Cary. Fr. Ben. Gladys and May and to their

families and friends; many thanks also to the Meneses family.

To the many relatives and friends who have been very rncouraging üII thrse

yeürs, both in the Philippines and in Canada. my sincerest ihanks.

Finally. I dedicate this thesis to my two Fathers up there.. .

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ................................................................................................. i

TABLE OF CONTENTS ................................................................................................... vi

.............................................................................................................. LIST OF TABLES x

. . ........................................................................................................... LIST OF FIGURES x 11

. . LIST OF SYMBOLS ....................................................................................................... sv11

1 . [NTRODUCTION ........................................................................................................... I

2 . REVEW OF LITERATURE .......................................................................................... 4

2.1. Initial Raw Müteriüi Propenies ................................................................................ 4

........................................................................................ 2.2. Kinetics of Mms Transfer 6

2.3. Propenies of Plant Materials .................................................................................... 7

3.4. Mass Transfer and Water Relations in the Celi ........................................................ 9

2.4.1. The chernical potential of water as driving force for mass trûnsfer ........... 9

2.4.2. The osmotic pressure of water, concentration of solutes and water flow I O

.............................. 2.4.3. Turgor pressure, elasticity of ce11 walls and fluid flow I l

2.5. The Phenornenon of Plasmolysis ........................................................................... 15

............................................................................ 2.5.1. Importance of plasmolysis 17

2.5.2. Structural and chernical aspects .................................................................. 18

................................................................................... 2.5.3. Observing plasmoly sis 20

................................................................... 2.6. Laser Scanning Confocal Microscopy 20

............................................................................................. 2.6. 1 . T L microscope 2 1

............................................................................... 2.6.2. Fluorescent compounds 2 3

.............................................................................. 2.7. Image Analysis and Processing 25

3 . CLASSIFICATION OF PLANT MATERIALS ACCORDiNG TO MASS

TRANSFER BEHAVIOR .............................................................................................. 28

........................................................................................................ 3.1. Introduction ,... 28

3.2. Methods ....................,,,........................................................................................... 29

3.3. Results and Discussion ............. ,., ........................................................................... 35

3.3.1. Description of mass transfer behaviour ..................................................... 35

................................................................... 3.3.2. Classification of plant materials 45

4 . CHARACTERISATION OF MASS TRANSFER BEHAVIOUR OF APPLE

........................................................................................................................... TISSUES 50

............................................................................................................ 4.1 . Introduction 50

........................................................................................... 4.2. Materials and Methods 51

....................................................................................... 42.1. Srmple preparation 51

............................................................ 4.2.2. Vacuum infiltration measurements 5 3

.......................................................... 4.2.3. Kinetics of water loss and solids gain 54

........................................................................................... 4.3. Results and Discussion 62

...................................................................................... 4.3.1. Vacuum infiltration 6 2

4.3.2. Kinetics of water loss and solids gain ....................................................... 67

............................................................................. 4.3.3. Inverse polynomial fitting 73

4.3.4. Development of a thermodynamics approach for analysis of equilibrium

........................................................................................... conditions in the tissue 8 7

4.3.5. Effects of solution and material resistances on mass t r a d e r coefficients

................................................................................................................. and fluxes 97

vii

4.3.6. Classification of mass transfer behaviour of plant materials based on

ratio of bulk flow transport to diffusion transport ........................................... 1 15

4.4. Summary .............................................................................................................. 1 19

5 . DEVELOPMENT OF METHODS TO OBSERVE AND MEASURE PLANT

TISSUES DURING OSMOTIC DEHYDRATION ........................................................ 1 22

5.1 . Introduction .......................................................................................................... 132

5.2. Materials and Meihods .......... ............................. ............................................... 124

5.2.1. Dye selection and development of protocol for acquisition of images .... 124

5.2.2. Development of methods to measure volumes of structures ................... 116

5.3. Results and Discussion ....................................................................................... 129

5.3.1. Microscopy method ..................................................................................... 129

................................................................................. 5.3.2. Image analysis system 139

5.33. Application of developed methods on apple tissues ................................. 161

5.4 Summary ............................................................................................................ 177

...................................................... 6 . GENERAL SUMMARY AND CONCLUSIONS 179

............................................................................................................ 7 . REFERENCES 182

....................... APPENDK A Water Loss and Solids Gain Data of Different Materials 187

APPENDK B Protocol for the preparation of srmples and sucrose content measurements

..................................................................................................................... using HPLC 193

................................................. APPENDK C Procedure for Determination of Density 195

................................................................ APPENDrX D Detemination of Water Loss 197

..................................................................... APPENDIX E Results of Kinetics Study 199

........................................ APPENDM F Application of the Inverse Polynomial Mode1 200

viii

APPENDIX H Equations for Determining Mass Transfer Coefficients for Different

Concentrations of Osrnotic Solutions .......... ... . ...... .... . ..................................... . . . .. ....... 206

LIST OF TABLES

Table 3.1 Results of %WL analysis .................................................................................. 36

Table 3.2 Results of % SG analysis .................................................................................. 36

Table 3.3 Osmotic conditions of materials and description of their m a s transfer

................................................................................................................... beliaviour 38

Table 3.4 Description of wüter loss behüviour based on Si .............................................. 40

Table 3.5 Description of water loss behüviour biised on S2 .............................................. 40

Table 3.6 Results of water flux analysis ............................................................................ 44

Table 3.7 Results of solids flux analysis ........................................................................... 44

Table 3.8 Description of mass tnnsfer behaviour bued on fluxes . J,, .............................. 46

Table 3.9 Description of m a s trmsfer behaviour based on fluxes . J , .............................. 46

Table 3.10 Classification of water loss behaviour based on tluxes .................................. 47

Table 3.1 1 Classification of solids gain behaviour based on Ruxes ................................. 18

Table 4.1 Vacuum Infiltration Measurements on Granny Smith Apples .......................... 63

Table 4.2 Porosity Measurements on Apple Tissues ........................................................ 65

................................................................................. Table 4.3 Rrsults of % WL anülysis 76

Table 4.4 Results of % SG analysis .................................................................................. 76

Table 4.5 Results of water flux analysis ......................................................................... 78

Table 4.6 Results of solids flux analysis ........................................................................... 78

................... Table 4.7 Computed equili bnum values at different sucrose concentrations 96

Table 4.8 Effects of solution md material resistances on m a s transfer ........................... 98

Table 4.9 Mass transfer coefficients and behaviour of apple tissues at different sucrose

concentrations ..................................................................................................... 107

Table 4.10 Mtiss trrinsfer coefficients and behaviour of different materials at 60% sucrose

concentration ........................................................................................................... 117

Table 5.1 Order of sucrose treatment ................................... ,,,, ...................................... 165

LIST OF FIGURES

Figure 2.1 Structure and contents of a parenchyma ce11 (Brook . 1964 from Mohsenin.

r gai0 ............................................................................................................................ 8

Figure 2.2 Two-dimensional representation of cellular conglomerate as thin.walled . fluid-

fillcd shells ................................................................................................................ 13

Figure 2.3 Diagrammatic representation of cells undergoing plasmolysis ....................... 16

77 Figure 2.4 Optical pathway of a fluorescence laser scünning confocal microscope ......... -,

Figure 2.5 An idealisation of the opticül sectioning propeny ................................ .. .... 2 4

Figure 3.1. Schematic diagram for clüssification of rnass transfer behaviour .................. 34

Figure 4.1 Flow diagram for sample preparation ............................................................ 52

Figure 4.2 Set-up for Osmotic Dehydration Treutment .................................................... 56

Figure 4.3 Sampling Diagram for Kinetics Study .......................................................... 5 8

Figure 4.4 Cells in fresh tissue und the distribution of main components in the tissue .... 66

................................................................ Figure 4.5 Cells in vacuum-infiltrated tissue 6 6

.......................................................................... Figure 4.6 Cells in water-fi lled tissue 6 6

....... Figure 4.7 Kinetics of water loss of appie slices in different sucrose concentrations 70

...... Figure 4.8 Kinetics of solids gain of apple slices in different sucrose concentrations 72

Figure 4.9 Inverse polynomial fitting of water loss vs . time at different sucrose

............................................................................................................ concentrations 74

Figure 4.10 Inverse polynomial fitting of solids gain vs . time at different sucrose

concentrations ....................................................................................................... 7 5

xii

Figure 4.1 1 Experimental and cdculated water loss values at different sucrose

concentrations ............................................................................................................ 80

Figure 4.1 7 Experimental and calculated solids gain values at different sucrose

concentrations ........................................................................................................... 81

Figure 4.13 Density of apple slices during osrnotic dehydration at different sucrose

concentrations ......................................................................................................... 8 2

Figure 4.14 Change of surface area of apple slices dunng osmotic dehydration at different

sucrose concentrations ............................................................................. .,, ............ 83

Figure 4.15 Thickness of apple slices during osmotic dehydmtion at different sucrose

concentrations ........................................................................................................... 84

Figure 4.16 Volume of an rpple slice from weü and thickness meüsurements üt differen t

sucrose concentrations ............................................................................................... 85

Figure 4.17 Volume of lin apple slice rneüsured with pycnometry at different sucrose

............................................................................................................ concentrations 86

................. . Figure 4.18 Volume of an apple slice from pycnometry vs wüter loss values 88

Figure 4.19 Volume of an apple slice from area and thickness measurements vs . water

loss values ............................. ,., ............................................................................... 89

Figure 4.20 Partition of water in the tissue at equilibrium between the extracellular space

and the cells ..............................................................................................................

Figure 4.2 1 Dcnsity as a function of sucrose concentration .............................................

Figure 4.22 Log of viscosity as a function of rnolality of sucrose solution ....................

Figure 4.23 Diffusivity as a function of sucrose concentration ...................................... 101

Figure 4.24 Mass transfer coefficient (k, ') of different sucrose concentrations .............. 102

Figure 4.25 Theta values of different sucrose concentrations ......................................... 104

Figure 4.26 Mass transfer coefficients. k., and k*.. of different sucrose concentrations ... 105

* ... Figure 4.27 Overall mas transfer coefficient K. at different sucrose concentrations 108

Figure 4.28 Mass transfer coefficient of the material . knr at different sucrose

concentrations .......................................................................................................... 109

Figure 4.19 Bulk Row Y S . sucrose concentration ........................................................ I l l

....................................................... . Figure 4.30 Bulk flow vs mole Fraction of solution 112

......... Figure 4.3 1 Müss transfer behaviour of üpple at different sucrose concentrations 114

Figure 4.32 Mass transfer behaviour of vanous plant materials at 60% sucrose solution

................................................................................................................................. 118

Figure 5.1 Fresh onion epidermis treûted with FDA ....................................................... 130

Figure 5.2 Onion epidemis stained with FDA after treatment in 20% sucrose solution 130

Figure 5.3 Onion epidermis stained with FDA after treatment in 60% sucrose solution 130

Figure 5.4 Fresh pear tissue stained with FDA ............................................................. 133

Figure 5.5 Pear tissue stained with FDA after treatment in 60% sucrose solution ......... 133

Figure 5.6 Cell walls of fresh potato tissue stained with Methylene Blue + AzureB ..... 135

Figure 5.7 Cell walls of potato tissue stüined with Methylene Blue + AzureB and treated

with 60% sucrose solution ....................................................................................... 135

Fi y r e 5.8 Cell walls of fresh potato tissue stained with Neutral Red ............................ 137

Figure 5.9 Cell walls of potato tissue stained with Neutral Red and treated with 60%

sucrose solution ............................................ 137

Figure 5.10 A stack of 7 optical slices of apple cells stained with FDA and treated in 60%

sucrose solution ....................................................................................................... 140

xiv

Figure 5.1 1 A stack of 4 optical slices of apple cells stained with FDA and treated in 60%

sucrose solution ....................................................................................................... 142

Figure 5.12 A montage of 16 optical slices of iipple cells stained with FDA and treated in

60% sucrose solution ............................................................................................... 144

Figure 5.13 A z-projected image of the 16 optical slices from Figure 5.12 .................... 146

Figure 5.14 Pre-processed image of üpple cell walls treated in distilled water .............. 150

Figure 5.15 Smoothed image of the siime image from Figure 5.14 ................................ 150

Figure 5.16 Smoothed image using thresholding by edge-base detection ...................... 152

Figure 5.17 Smoothed image with thinned boundaries ................................................... 152

Figure 5.18 Fitting the "spider" at the centre of a cell .................................................... 154

.................................... Figure 5.19 A cell with its edges detected by the spider (arrow ) 154

..................................... Figure 5.20 Multiple cells with spider-detected rdges (ürrows) 157

Figure 5.2 1 Three-dimensional reconstruction of spider-detected cells from Figure 10. in

.................................................................................................. siimple-points view 157

Figure 5.22 Wire-frame view of the three-dimensional reconstruction of the same cells in

Figure 5.21 ............................................................................................................... 159

Figure 5.23 Volume measurements results window on top of the three-dimensional

............................................................................................ reconstruction window 159

....................................................... Figure 5.24 A ce11 undergoing osmotic dehydration 161

.......... Figure 5.25 Procedure for sample preparation for sample treatment with LSCM 164

Figure 5.26 Schematic diagram for image acquisition of apple tissues during osmotic

.......................................................................................... deh ydration (single mn) 166

Figure 5.27 Images of üpple tissue (same section for al1 treatments) stained with MB after

........................................................................................... each of the 7 treatments 168

Figure 5.28 Images of apple tissue (same section for al1 treatments) stained with FDA

.................................................................................. after each of the 7 treatments 171

................................................. Figure 5.29 A z-projected image with the labelled cells 174

* ...................................... Figure 5.30 An optical slice with one ceIl Iabelled and traced 174

xvi

LIST OF SYMBOLS

Density of the osmotic solution. kg/rn3

Density of the fresh tissue. kg/m3

Chemical potentiai of water, Joulelkmoi. Superscript c refers to céll. superscripi soi to solution

Density of water. kg/m3

Bulk flux1 diffusive transport ratio

Corrected ratio of dehydration

Viscosity of the solution. mPas

Correction factor for mass trinsfer coefficient

Interface area of the material, m2

Linear intercept for t/water loss

Linear siope for Vwater loss

Water activity. Superscript c refers to cell. superscript sol to solution

Linear intercept for Vsolids gain

Linear dope for Vwater loss

Molar concentration of solute in extrûcellular space. kmol/m3

Molu concentration of solute at interface, kmol/m3

xvii

Total molar concentration of osmotic solution. kmol/m3

Binary diffusivity. m'ls

Flux of solids at interface. kg/m2s. kmoll m2

Flux of water at interface, kg/m2s. kmoll m2

Mass transfer coefficient for the solution. m/s

Moliir müss tmnsfer coefficient for the xtivr zone of material. kmol/rn2.s

Molw mmass triansfer coefficient for the solution. kmol/m'.s

Corrected molar mus transfer coefficient for the solution. kmol/m2.s

Overall molar mass trmsfer coefficient. kmol/m2.s

Superficial length of the piecr of materinl. m

Initial mass of piece of tissue. kg

Mass of solids (sugar). kg

Müss of piece of tissue at any time. t. kg

Müss of water, kg

Final mass (mass at equilibrium). kg

Moiecular weight of osmotic solute. kg/kmol

Apparent molecular weight of cellular solutes. kgfkmol

Molecular weight of water, kg/kmol

Number of moles of soluble solids in the cell, km01

Number of moles of water in the ceIl, km01

Turgor pressure, Pa

Initial value of pressure in cells, Pa

Universal gas constant, 83 14 ~a.m'/K.kmol

xviii

Re Reynolds number

R, Flux ratio for correction factor

SI Kinetic constant for water loss, l/s

Sz Kinetic constant for solids gain, Ils

Sc Schmidt number

SC Fraction of solids gained at time t. kgkg

SG, Fraction of solids gained at equilibrium. kg/kg

SCi Shewood number

T Trmperiture. K

I time, s

14 Velocity of liquid flowing out in the rxtracelluliir space. ds

V,. Volume of a cell, m-'

Volume of the tissue. m.'

Fraction of water lost at time t, m3

Fraction of water lost at equilibrium, kg/kg

Fraction of water that can move out but remriins inside the material st time.

Weight fraction of soluble solids in fresh tissue

Weight fraction of soluble solids in fresh tissue

Weight fraction of solids at time r

Weight fraction of water in fresh tissue

Weight fraction of water in fresh tissue

xix

Molar fraction of solute at the interface

Molar fraction of solute in osmotic solution

Mole fraction of the solution. Superscript c refers to cell, superscript sol to solution

Mole fraction of the soluble solids inside the cell. Superscript c refers to d l . superscript sol to solution

Volume of slice

Volume of air in the slice

Volume of insoluble solids in the slice

Volume of soluble solids in the slice

Volume of writer in the slice

1. INTRODUCTION

Osmotic dehydration is a process of removing water from food materials through

immersion in a hypertonic solution of mainly sugars or salt. It is ü pre-processing step

that üchieves and facilitates both the loss of water through diffusion across the semi-

permeable membranes of the food. and the pre-concentration of the rnüterial through

minimal solids gain. This process is an improvement of the direct dehydration procedure

because it is less damaging to the food material and less energy consuming without phase

change.

Research on osmotic dehydration hüs corne a long way since Ponting pionerreci i t

in 1966. Studies have been done on a variety of food materials and this application hüs

found greater utilisation on fruits and vrgetables as an effective preservation mrthod. It i s

interesting to note that the cornpiexity of the structure and morphology of thrsr plont

müterials rnake the undentanding of the m a s transfer process difficult. Hencc. studies

done on macroscopic levels (from r whole piece of materiül) were no[ ~iifficieiit and

intensive studies on a microscopic level were developed. These studies at the cellular

level al1 aimed towards the undentanding of the mass trwsfer phenornenon by proposing

models of simplified tissue structures.

S tudies from plant physiology have been very useful tools in constructing models

of the plant tissue structure to explnin the kinrtics of mus transfer. Toupin ri <il. i 1988)

presented a "realistic model of a cell and its environment". In this modrl. it wüs assumed

that al1 the cells in a piece of tissue have sirnilar structures and cell wall membrane

properties. The model wûs able to simulate al1 the important phenomena occumng in a

real tissue giving a qualitative description of the resistances of the structural bürrier to

permeation by its ability to monitor the changes O C C U ~ ~ ~ in plant tissue during osniotic

dehydration.

Marcotte and Le Maguer ( 199 1 ) investigated the behüviour of potüto tissue in an

osmotic solution at equilibrium by describing the structural changes of the cells during

osmotic dehydration. The extrnded thermodynamics approüch applied in the construction

of the model rnübled the equilibrium state of the plant incitrri;il io bt: quünlitïtxl ;iiid

predicted.

The mas transport phenornenon occurring in plant tissue during osmosis i n v d W.;

cornplex mechanisms; most of them controlled by the plant cells (Le Mügurr. 1988).

Studies on osmotic dehydration done on structures and propenies of plants at the crllulor

level. however. are mostly for monitoring growth. Those studirs tliüt ;ire wIcI! hi.

osmotic dehydration as a food processing method deal more with the rfkcts of the

process on the structure and composition of the food material. The vüriety of plant

materials avaiiable for the application of this process resulted in different approüches to

rxplaining the behaviour of each of the particular product studicd and hrnce. a vürirty of

models are available. each one suited to one specific matrrinl. At present. thercr dors not

rxist a general model for tissue behaviour as a function of precisely identified and

measurable tissue propenies. The main purpose of this study was to develop methods thüt

will describe these structure and propeny parameters. These methods would dlow the

characterisation of the tissue's behaviour in relation to meaningful and melisurable

quantities in the tissue and the basic driving forces for mass trünsfer. The characterisation

of these key physical panmeters provides the first step in the prediction and classification

of the mass transfer behaviour of plant tissues. This will üIso event~iülly Iead to the

deveioprnent of a general mode1 for osmotic dehydration of plant Foods. By devrloping

methods to facilitate the charactrrisation of plant tissue behaviour. the understiindiry of

both the plant materials and the process of osmotic deh ydrüt ion have hcr 11 1'11 ~xlwrcd.

Overall, this research will try to üddress the need for a rnicroscopic description of the

mas transfer phenornena in plant tissues.

The specific objectives of this research were first. to dcvelop a method of

classifying plant tissues according to their mus tnuisfer behaviour: second. ro be abIr to

characterise mass transfer behaviour of plant tissues at ii macroscopic level: and third. to

develop methods to observe and quantify plant tissue structures during osrnotic

dehydntion to bring the characterisation down to the rnicroscopic level.

This thesis will present a review of literature relevant to this study in Chapter 2. In

Chapter 3. methods of classifying müss trans fer behaviour for different plant tissue

materials are discussed with data taken from published l i ternture. A typical osmotic

dehydration mn on apples was also performed to get mass transfer data For

characterisation of the tissue's behaviour. These are discussed in Chapter 4.

Methods to observe and quantify plant tissue structures during osrnotic

dehydration are discussed in Chrpter 5 . These involved the use of a Laser Scanning

Confocal Microscopy (LSCM) system to observe plant tissue structures. Mrthods using

cornputer image analysis were developed to measure these structures. As preliminüry

tests. these developed methods were applied to üpple tissues. The results of this

application on apple tissues are discussed at the end of the chapter. A summary of the

study with the recornrnendations and conclusions follow in Chapter 6.

and freezing could alter the permeability of the material and would favour solute gain

niher than water loss (Ponting. 1973; Karel, 1975; Islam and Flink. 1982).

In osmotic dehydration of fresh plant tissues. it has been demonstrated that of al1

the variabies investigüted (e.g.. concentration. temperature. solute molecular wrigh t.

processing time). solute molecular weight was the dominant factor influencing solute

uptake. The effects on water transport and solute contained within the fruit were

üssessed by Saurel et al. ( 1994) as follows:

I. At low temperature (TdO O C ) and short processing time (te30

minutes). osmosis had ii direct effect on dehydration which inçreased

as the solute molecular weight decreüsed.

2. Loss of fruit solutes was not directly related t« the exteni uf

dehydration in the product. Therefore. i t would be possible to parrially

control this loss. Indeed. formation of a concentrüted superficial

barrier layer of solute from the soriking solution induced by high soliiir.

concentration andor high molecular weights would rrduce thc

apparent diffusivity and loss of fruit solutes. Nütural crll niembmne

bümers. which are better conserved at higli molecular weights and low

tempentures, could also reduce solute Ietage.

Hence, dehydntion soaking processes could be extended to orher foodstuffs. rven

those Iûcking ce11 membranes where a superficial layer can be formed (Süurel et el..

1 994).

2.2. Kinetics of Mass Transfer

There are two major mass transfer phenomena involved in osmotic dehydrÿtion:

the movement of solute into the material and the flow of water out of the tissue. There

have been numerous studies done to describe the kinetics of these two counter-current

flows. Variables such as temperature. time of treatment. nature, concentration and

composition of solutes. however. influence the m:iss trünst'er kinetics: thus. t h r variahi liiç

in reported resulrs.

The kinetics of m a s transfer is usually described through the terrns: wnter loss.

solids or solutes gain, and weight reduction. Water loss and soiid gain could be measured

through rate of water and solute flow, respectively. over time or through the amount of

water lost or solute gained after a certain period of time per ümount of initial mritrriril.

Solids gain clin also be studied through solute penetrütion techniques thlit determirie the

amount of solute uptake by the material with time.

Water loss and solids gain have been modelled using different approüchcs. If the

concentration of the osmotic solution remains constant and if the resistrince rit the surfrice

is negligible. Fick's second law can be applied (Magee et. al. 1983). Togrthrr with

Crank's equation. other studies (Conway, et al., 1983) were also able to model these

phenomena. to a certain extent. This approach, however, is limited to processes where the

rate of m u s transfer is simply due to diffusion where the tlow is unidirectional and there

are negligible interactions between the components in the tissue and the solute during

diffusion (Le Maguer, 1986).

Studies on model agar cubes have also been done to describe the "dewatering"

and "impregnation" processes (Raoult-Wack et. al. 1991). Some models were able to

describe not only water loss and solids gain but also reduction of wüter üctivity and

shrinkage of the samples (Hough. et al.. 1993).

2.3. Properties of Plant Materials

The tissues primürily eaten in fruits and vegetables are made up of pürrnchymü

cells t Figure 2. I I. These cells have different sizes but are renrrnlly polyhrdrons with 14

sides and relütively rigid cell walls. The part that is active in rnetabolism 1s the protoplut-

it is a thin film between the vacuole and the cell wall and represents about five percent of

the cell's total volume. The protoplasm is bounded on the cell wall by the plasmülemmü

and the membrane beside the vacuole is the tonoplut. The vacuole plays ci major role in

dehydr~tion because approximately 90% of the water in the plants cire presrnt h m . The

cell wall is composed of the primary wall. the middle lamellii. the pliismodesrnaiii and

other surface materials. During drying, these ceIl wall componrnts are drh ydrütrd w hich

partially accounts for the loss of weight and water that occurs. Brtwren the cells lies the

middle lameila. a structure cementing the cells together (McWilliams and Paine. 1977).

The nature of the protoplasm is generally studied to determine how different

substances can penetrate into the living cells. From these studies. the fact thüt the

protoplast of a plant ccll is very permeable to some substances but only slightly

permeable to others was established. Sugars, such as glucose or sucrose in solution could

slowly pass through the protoplasmic membranes. These hypenonic solutions effect

plasmolysis because of the greater total concentration it has than that in the cell sap when

the cell is in zero turgor (Noggle and Fritz. 1983).

Fi y re 2.1 Structure and contents of a parenchyma ce1 l (Brook, 1964 from Mohsenin, 1968)

According to Nobel (1974). the cell wall and the plasmalemma are permeable to

water and to some solutes. Water will be contained in the cell, under pressure as long as

there is ü high internai concentration of solutes. This capacity of the cell is measured hy

its cell potential. The difference between the magnitudes of ce11 turgor pressure (osmotic

pressure in general) and osmotic potential is the water potential. Net osmosis occurs if the

water potential inside the cell is not equül to that in the extrücellular tluid outside: the

process occurs Rom a region of high water potential to that of a lowrr wtitrr potenti J I at a

rite proportional to their difference.

The lipid bilayer of the plasmalemma (rather than the actuül cell wall) provides

the greatest resistance to osmosis. so its permeability determines the rats of osmosis into

or out of the ceIl as a response to a change in water potrntiüi. Osmosis occurs wirhout

transfer of solutes as long as the plasmalemma remains intact (Pitt. lLN2 i . Fcrrici- iiiid

Dainty (1977) reported that the main resistünce to water tlow into tlir cells is the crll

membrane rather then the apoplast: but in some cases, the resistünce of the üpoplüst iind

its water capacity can significantly contribute to the water potential rquilibriurn tiinr

constant of the tissue. The apopliist is that area in the ceIl wall and intercellultir spüccs

where water and solutes could move around.

2.4. Mass Transfer and Water Relations in the Ce11

2.4.1. The chemical potential of water as driving force for mass transfer

The chemical potential of any substance iiccording to Niklas ( 1992) is a measure

of its capacity to do work, which depends on a variety of factors, the mont important

king the concentration of the substance. As the concentration decreases. the capacit y of

a substance to do work. and therefore its chemical potential. also decreases. Thus

gradients in the chemical potential of water can be established by concentrated gradients

of solutes within the plant body, as well as by the simple fact thüt water re-mers and

leaves a plant at different points dong the plant cixis.

Nobel (1974) also stated that the chernical potential is app lied in dererm ining

whether a certain substance is in equilibrium across some barrier and ihus. would not tre

rxpected to move spontaneously from one side to the other. It is also used in predicting

the direction of net movement and the driving force acting on some sprcies which hüs

different chemical potential on the two sides of a membrane or othtir surfiicr sep;irütinp

the compartments under consideration.

2.4.2. The osmotic pressure of water, concentration of solutes and wüter tlow

[raki et al. (1989) observed that the rnovement of writer molecules ricross

membranes is readily apparent when cells lacking ceIl walls rire submerged in pure wiiter.

They noted that the cells swell and in some circumstnnces burst when the trnsile strength

of their plasma membranes is exceeded by the hydrostatic pressures that develop within

them. The [ensile strength of plant ce11 walls (on the order of 10' to 10" mPü) is

significantly greater than that of the plasma membrane; hence greater intemal pressures

are required to burst cells with even very thin plant cell walls. Osmotic pressure or

osmotic potential. therefore, is the pressure that must be applied to prevent the movement

of water molecules across either a biological or an artificial membrane; and this is

because stresses can be expressed in terms of pressure (Niklas. 1992).

Metreveli and Bakradze ( 1985) stated that the condition. n., < fl,, iosmiitic

pressure of the extracellular and intracellular solutions. respectively) is usually truc. On

this bais. the difference in the osmotic pressure M = n, - fl.,,, is compensated by the

pressure due to deformation of the ce11 wülls. P and with maintenance of the quasi-

equilibrium state of the systrm. the equality An= -P (the ceIl is swollen). If this equality

dors not hold tnie, water will start to movc through the semi-permcablc mcrnbranc of thc

cell. the direction of which is dictated by the sign in the change of the osmotic gradient. If

the cell is in ü hypenonic solution. such that ne., > a,, then. water will movr out of the

cell and it will undergo negütive deformation after which plasmolysis occurs.

Simon's study ( 1977) on leakage of fruit cells in water showed thüt if the osmotic

stress in a panicular cell is too high. it will cause the cell walls. the plasmalrmma and the

tonoplast to plüsmolyse and burst. In tum, vxuolar solutrs will be relelised in the writer

where the tissue is immersed. Cells burst because of the steep osmotic gradient between

the external and intemal solutions so that the celk with the highest vacuolar soluce

concentration and the weakest walls will burst first. The progressive bursting of ce1 ls

causes a gradua1 loss of the ce11 contents. Tissues having large cells and high solute

contents like fruits have a higher turgor pressure that the cell walls can withstand.

2.4.3. Tutgor pressure, elasticity of cell walls and fluid flow

Turgidity according to Niklas (1992) rneans thüt protoplasts within cells are

completely hydrated. It was fitrther stated that in the case of turgid protoplüsts. the value

of the positive pressure potential equals the same of the negütive solute and matric

potentials, and the cell water potential equals zero. As the cell water potential becomes

I I

more negative, the turgidity of the ceil decreases: the ce11 becomes ilaccid and ultimately

plasmolyses. Turgor pressure is therefore, biomechanically important because it greatly

affects the tensile stresses generated within cell walls and the mechanical stiffness of thin

walled cells and thin wailed tissues. such as parenchyma. When the protoplüsts within

their thin-walled cells are fully turgid and opposed to the ceIl walls. they rxrn a

hydrostatic pressure that places the ce11 walls in tension.

The inflated protoplasts of fully turgid. thin-walled cells reduce the freedom of

cell walls to move (and buckle) under a compressive stress. When a uniform.

compressive stress is applied, iluid will begin to tlow out of the ce11 becausr of the

increase in turgor pressure and waier potential. The rate of this fluid tlow is highly

dependent on the permeability of the plasmalemma (Nilsson et al.. 1958).

In a cellular mode1 developed by Pitt (1982), i t was assumed that a crll is ii thin-

walled. tluid-filied vesse1 having a positive internai turgor pressure (Figure 2 . 2 ) . Whrn

the cell wall is compressed. the foliowing increases: the c r l l surhiçr ;irea. the ccll wüll

tension. and the turgor pressure. The rise in turgor pressure is the reason why c r l l fluid

flows out of' the cell through the cell wall. the rate of which drpends again. on the

permeability of the plasmaiemma. By considering the ceIl walls as being permeable. the

enclosed volume of the cell is assumed to decrease at a rate proportional to the difference

between turgor pressure, p and initial turgor pressure. p., (Pitt and Chen. 1083). This

assumption is in agreement with osmosis laws that the volumetric rate at which tluids

pass through a selectively permeable membrane is proportional to the difference in water

potentid on both sides of the membrane (Nobel, 1974).

Figure 2.2 Two-dimensional representation of cellular conglomerate as thin-walled. tluid-

filled shells. a= stress from compression (Pitt. 1982).

One other assumption in Pitt and Chen's study ( 1983) is that the rate of change of

the enclosed cell volume is proportional to the cell surface iireit. Testins of rhe modd

showed that increasing the pemeability. C increases the rate of rnigriition of watrr out of

the ce11 as turgor pressure increases. Increasing C will also effect a lower resistancr of the

ce11 in compression. By increasing the strain rate. r, the cell tluids move out of the ceIl ac

a slower rate. Increasing r. therefore. should increüse apparent cell stiffness and

counteract the effects of high C values. It was also found that the response of the çrll to

compression depends on the ratio r/C (relative strain rate). When ceIl wall permeability is

decreased or the stnin rate is increased. tissue stiffness increases. The greatest tissue

stiffness occurs when r/C = W. At this point. the cell wiill is in the rlüstic case iiiid

impermeable to ceIl fluids (C = O). or that r is rxtremely large (like in impact situations).

The model was also able to predict that tissue stiffness iit low strains indicares turgidity.

which is independent of cell wall permeability and strain rate.

High cell rigidity, according to Pitt and Chen (1983). significantly weakens

vegetative tissue because the cell walls are in a highly pre-stressed stiite before being

subjected to extemal stress. Cellular tissue may seem damaged by the expulsion of fluid

under compression and the ce1 1 walls and intercellular bonds are mechanicrilly diimaged

as well. They implied that with their model. it might be possible to devise a compression

test wherein turgor pressure and cell wall permeability can be estimated from the gross

mechanical propenies of the tissue. The moclel also predicted that a relaxation cffect

occurs such that the cells yield because of the fluid outflow causing cell wall rupture.

Munns et al. (1982) related turgor pressure. volumetric elastic modulus (E ) .

osmotic volume and ultrastructure of the walled, unicellular rnicroülga. Chlorelki

emersonii grown in extemal sodium chlonde concentrations. They concluded thüt a low E

means that a ceIl can maintain turgor despite sudden changes in n(osrnotic pressure) and

it would unlikely be to plüsrnolyse.

2.5. The Phenornenon of Plasmolysis

The most commonly observed phenomenon during osmotic dehydcition is the

separation of the protoplüst from the cell wall (Figure 2.3). This is temed as pliismolysis.

Botanists dl over the world have been trying to elucidate the actual events during

plasmolysis that occur inside the cells for over a hundred years. With the advent of

advanced microscopie techniques that allow the naked eye to see sharper images nt

higher magnifications, a number of new observations and theories have been presented.

Much of the basis of what we now know about this phenomenon hüs been taken froni

studies done as long ago as 1877 by DeVries who coined the term "plüsmolysis"

(Stadelmann. 1966). In 1883, however, Bower disagreed with DeVries' description of the

process. According to DeVries. the protopiüsmic body is completely isolatrd from the

cell wall when the plasmolysing solution is strong enough. Studies done after that showed

that delicate threads of protoplasm might connect ceils. thus keeping u protopliisrnic

continuity through their cell walls (Bower. 1883). In 1 887, Pffefer rstüblishrd the

concept of the osmotic mechanism for cell turgor. This was after finding experimentally

that the turgor pressure present in the plant ce11 is caused by osmosis and differential

permeability of the protoplasmic layer (Stadelrnmn, 1966). S tadelmann. Lee-S tadelmmn

and their CO-researchers (in the 1980's) and Oparka and his col leagurs ( in the I LILIO'\ 1 did

plasmol ysed

protoplast

l hypcnonic

1 solution

j J' J W protoplasi in a conve - protoplast.

balling away from the ce11 wall

nking

shrinking awüy x way

Figure 2.3 Diagrammatic representation of cells undergoing plasmulysis. Plasrnolysed protoplasts are illustrated in the different ways that they çould shrink

away from the cell wall.

more extensive developments on plasmolysis in the last 20 years. At present. this

complex phenomenon still remains an enigma to botanists and plant physiologists.

Osmotic dehydrütion as a food processing technique has to be understood at the

deepest level of the tissue being studied. The roles the microstructures play duriny the

process have to be detïned and understood in order to optimise this technique. Processing

parameters. as well as the design and development of equipment to be usrd for industrial

purposcs depend on the processes occurring <it the cellular Iwrl.

2.5.1. Importance of plasmolysis

Plasmolysis is a unique technique in experimentd plant cell physiology to study

the physicochemical properties of individual plant cells and their aiter~tions in the living

state. Protoplasmic differences between ceIl types as well as changes c;wsed h>

developmental stages and environment within the same cell type have been wcll

demonstrated by plasmolysis (Lee-Stadelrnann and Stiidelrnann. 1989).

According to Lee-Stadelmann and Stadelmann ( 1989). the cell parameters most

commonly measured by plasmolytic technique are: 1) passive permrability uf the

protoplasm layer. 2) protoplasmic vixosity. 3) cell wall aitachment of plasmülemmo. 4

solute potential of a single cell with large central vacuole. and 5) the relative sizr of the

non-solvent space in the vacuole. Also. plasmolysis is the most frequently used test for

cell viability. The plasmolyticum also prevents the bursting of the freed protoplasts

during protoplast isolation (Lee-Stüdelmann and Stadelmann. 1989).

Since the most commonly observed phenomenon during osmotic dehydration is

plasmolysis. it is only right that this change could be well observed and understood. Most

studies on osmotic dehydration for optimising the process has been done primiirily hy

following the kinetics of water loss and solids gain. In later yeilrs. researchers hiive gone

deeper into the material itself as its complex structure seems to control or affect this mass

trmsfer behaviour. Plasmolysis gives the first description of the events thiit occiir inside

the cell when the tissue is placed in osmotic solution. The shrinking of the proioplüst is

related to the amoiint of water lost during trentment. Understanding hnw and why this

happens, as well as how much it has happened are valuable information. Utilising tools

employed by biologists to study this phenomenon may lessen the complexity of the pliint

tissue's behaviour.

2.5.2. Structural and chernical aspects

A structural mode1 proposed by Opürka. et ü1. ( 1994) presrntrd il plüsmolysçd ce11

in which the surface area of the plasma membrane of the crll is conserved by the

formation of "Hechtian threads" or "Hechtian strands" and endoc ytic vesicles,

respectively.

Hecht first observed these strands in 1912 hence. the name. These are fine

protoplasmic strands that connect the protoplast to the cell wall. Generally. it has been

assumed that these strands are forrned from the plasma plasmolysis. However. there is ü

contention as to their site of origin at the cell wall. Some researchers considered that

these strands were formed from individual plasmodesmata or piasmodesmatal pit fields

(Drake et al., 1978 and Strasburger et al., 1983 as cited by Oparka. 1994). However. it is

possible that these strands can be drawn out from sites of plasmodesmata and could

maintain the continuity of the plasma membrane between cells, although it is more

definite that the strands may onginate from wall sites other than plasmodesrnata (Oprtrkii.

1994).

As plasmolysis continues, the protoplast eventually balls up inside the crll.

maintaining contact with the wall over only a smüll part of its surface (Oparka. 1994).

This is preceded by a rapid decrease in turgor pressure that drops down to zero.

Significant changes in the volume and osmotic potential of protoplüst occur onlv when it

hlis staned to shrink away from the wüll (Adümec. 1984). Studies on plasmolysis of

epiderrnal cells of onion by Oparka et al. (1994) showed that as the plasma membcinr

contracted. i t remained smooth and uncreased. Also. i t w u clearly observed that the

connections between the cell wall and the plasma membrane are made up of thin strands

and not sheets. These observations mean that as the membrane collapses. an energy-

favour;ible circumstance occurs csusing the formation of strands. Their studies also show

chat in the plasmolysed cell. the ünchored cortical endoplasmic reticulum nrtwork is

encased by the plasma membrane as it shrinks üway from the cell wüll. Thrse Hrchtiiin

strands. as they have observed. conserve membrane surface ürea. When the cclls were

deplüsmolysed. the strands are reincorporated bück inio the main body of the expanding

protoplast and the plasma membrane retums to its original position dong the çr l l wall.

The study of Attree and Sheffield (1985) also showed smooth surfaces of plasmolysed

protoplasts. The formation of irregularly shaped protoplasts. protoplasrnic threads.

subprotoplasts and protoplasmic networks covering intemal wall surfaces gave strong

evidence that there is strong wall adhesion of protoplasm.

2.5.3. Observing plasmolysis

Various microscopie techniques allow the observation of plasmolysis depending

on the tissoe heing studied. Thin sections of tissues could be easily obsrrved under a light

microscope. Plasmolysis of isolated protoplasts could be stained with tl

or dyes and viewed under a fluorescence microscope or prepared

transmission electron microscopy (SEM or TEM). Atree and Sheffield (

uorescent probes

for scanning or

1985 1 developrd

a method to observe plasmolysed non-isolated protoplast of the fern Pteriditini using

SEM. They observed gametophytes with light microscope using differentiiil interference

contrat (DIC) and ultra-violet fluorescence rnicroscopy with tluorescein diacetate

(FDA). Oparkü and his colleagues (1994) studied inner epidemis of onion bulb sçales

using DiOCo. FDA and aniline blue with fluorescence microscopy. They dso usrd

electron microscopy iifter staining the cells with phosphotungstic acid (PTA).

Bower ( 1883) was able to enurnerite different plant materials that were studird

during his time. These were probably the fint ever experiments conducted and reported

on plasmolysis. Materials studied include ferns. flower strlks and leüfy stems. beets. flesh

of ripe apple, a variety of water plants, their petioles and leaves.

Presently, laser scanning confocal microscopy has rnabled the observiition O!'

thicker section of tissues. The right fluorescent stain. however. has to be discovered for

each type of tissue and matenal.

2.6. Laser Scanning Con focal Microscupy

Laser scrnning confocal microscopy (LSCM) is a form of light inicrosçopy in

which white or a narrow range of wavelengths of laser light excites a specific fluorescent

material (Vodovitz et al.. 1996). It is usually used in the tluorescrnce mode for imaging

biological objects of various types, but can also be used in the brightfield retlrction mode

for imaging of objects of different forms (Sheppard. 1994). The microscope can slice

very clean. thin optical sections out of thick fluorescent specimens (houe. 1990). The

stack of samples taken at different depths is known as a z-series or a z-projection and c m

be reconsinicted to produce n 3-D image or object of the original structure that can br

rotated or sliced in any direction (Vodovitz. et al.. 1996). It also has the capiibility to

obtain 3-D images of biological and other microscopic structures (Sheppard. 1991;

Samarabandu et al.. 1991) and offen the tremendous advantage over the tluorescence

microscope by discriminating out-of-focus background fluorescence (Wells et al.. 1 W O ) .

2.6.1. The microscope

A schematic diagram of the optical pathway for a fluorescence confocal microscope is

shown in Figure 2.4. The microscope in this image is an inverted iypr. In the confoçd

microscope. an image of a pinhole is projected on a specimen. The tïuonsceni probes

within the specimen are excited by the light, which is focused at a point. The light is

scanned across the specimen by a mirror. The emitted light plisses through a Jichroiç

mirror and is focused onto a pinhole behind which is pliiced an imiïging devicr (cg . .

photomultiplier tube). If the imaging pinhole is placed at the same dist;incr from the

specimen as the first pinhole as the optical path. the optical arrangement is sriid to br

confocal. In this configuration, light that is emitted above or below the plane of focus

within the specimen will fall before or behind the imaging pinhole. The net result is that

Figure 2.4 Optical pathway of a fluorescence laser scanning confocal microscope. The light emitted from the light source is focused on the imüging pinhole and retlected by the dichroic mirror. The light is then focused on a single point within the specimen. Mirrors rnounted on scanning devices move the focused beam in the X and Y direction within the specimen. The plane of focus in the Z direction can be altered by moving the stage. The light emitted from the excited point within the specimen travels through the dichroic mirror and is focused onto the detector pinhole. The arrüngement of the lrnses is such that they have the same focal length and are the same distance from the irnaging and detector pinholes. Such an mangement is confocal. Light that is emitted above or below the plane of focus is not focused on the detector pinhole and does not significantly contribute to the resuliant image. A. B and C . Lens elements (Bacallao and Garfinkel.

out-of-focus information is discriminated against, resulting in ü th in

(Bacallao and GIufinkel, 1 994).

The con focal principle was first described by Minsky in 1% 1 and

opticü

then th

section

tsindem

scanning system by Petran and Baer (Wilson, 1990). This was brought about by the need

to acquire an image from a section of a thick translucent specimen without the presence

of out-of-fociis information from surrounding planes. Not onl y c m the con focal principle

attain this: it also has the capability to enhance lateral resolution. The small aperture

through which light is focused ensures that an image is taken only from one pürticuliir

b e l or plane of the specimen. This ability to do optical srctioning or drpth

discrimination (Figure 2.5) is what makes confocal microscope vcry useful for ii numbrr

of imaging needs. The confocal microscope allows the acquisition of high-remlution

images with a depth of focus sufficiently small chat ail the detail thüt is imageci üppcürs in

focus. This depth of focus cm also be extended by adding togrther (integriiting) the

images taken at different focal settings without üffectinç the 1ütrr;il rcsoliir ion i H' i l w i .

t 990).

The capücity of the microscope to take optical sections for thrce-dimensional

reconstruction is a very important tool in biological microscopy. This allows the

investigation of structure inside a cell. for example (Sheppûrd and Cogswell. 1990).

2.63. Fluorescent compounds

Fluorescent compounds have been used for more than seventy yeürs to study

transport pathways within plants. Fluorescent probes are molecules that are intrinsically

fluorescent when viewed under radiation of diffenng wavelengths and do not bind to

23

F ocus position Extended focus

Figure 2.5 An idealisation of the optical sectioning property showing the ability to obtain a through-focus series of images. which may then be used to reconstnict the original volume object at high resolution (Wilson. 1990).

cellular constituents. Fluorescent stains meanwhile. are molecules which tluoresce only

after binding to a chemical constituent of the cell, or whose fluorescence is rnhünced

follow ing a chemical reaction (Oparka, 199 1 ). Fluorescent probes have remarküble

sensitivity and specificity for the detection and imaging of mücromolecullir structures and

physiologicül ions in cells or in cell-free extracts. This is the reason why fl~iorescence is

predorninantly used for biological confocal microscopy. With confoc:il tliinre.;cencc

microscopy. it is possible to study biochemical and physical processes. iii intact crlh ;i~id

tissues. as well as to define and identify morphological feütures (Wells anci Johnson.

1 994).

Fluorescent probes are characterised by their absorption and rmission

waveiengths. A fluorophore is excited by fluorescence excitation encrgy and then émits

this energy in a range of wavelengths where the rmission inrcnsiry ciin hc ipuniificil. For

fluorescence excitation. a narrow bandwidth (< 20 nm) is normülly used. wherclis for

fluorescence detection. the detection bandwidth can Vary. depending on the desired

sensitivity (full spectrum for maximum sensitivity) and resolution (narrow baiidwidth for

maximum resolution). A probe that works well in one application müy not necrssarily

work well in another because environmentai factors affect its tluorescence characteristics.

These include pH of the aqueous medium, solvent polarity (local), the proxirnity. and the

concentrations of the quenching species (Vodovitz et al ., 1996).

2.7. Image Analysis and Processing

The data sets obtained from a confocd microscope are composed of a stack of X-

Y arrays containing intensity values assigned to each point or pixel in the image. Most

images are stored in a 5 12 X 5 12 array with intensity values ranging from O to 255. The

25

intensity reprcsents the degree of fluorescence (and the number of emitting tluorescent

molecules) nt a point. A series of X-Y images separated dong the 2-axis compares the

three-dimensional data set. With image processing techniques. these dütü sets cnn be

enhanced or smoothed (Bricallao and Garfinkel, 1994).

Image processing as defined by Russ ( 1990) States thüt "it includes those inethods

thai rtart with an image (an array of pixels. each with n brightness or greywole viilue or

perhaps with a colour information and end with an image."

A considerable number of image processing techniques are currently availüble.

With the advent of more advance software. the possibility of triinsforniing inmges is

alrnost limitless. These techniques improve the quality of the images açquired by

enhancing brightness and contnst. It can sharpen edges or change the colours of specific

items of interest in the image. These enhancements facilitate the measurements and the

three-dimensional reconstruction of these images. As Russ (1990) hüs expliiined. the

difference between image processing and image analysis is in the extraction of

information from the image. Image processing. he stated, like word processing (or food

processing) is the science of rearrüngement. For example, pixel values could be iiltered

according to neighbouring pixel brightness but the numbers of pixels in the image remain

unchanged. Also, in word processing, cutting and pasting text could be performrd

without reducing text volume. Food processing is also another form of rearranging

ingredients to produce another kind of mixture. Image analysis on the other hand. Russ

stated, attempts to find those descriptive parameters. usually numeric, that could give us

more meaning to what the image is really trying to tell us. h a g e analysis therefore is a

more quantitative method that involves rneasurements of di fferent objects or the w holr

image itself.

3. CLASSIFICATION OF PLANT MATERIALS ACCORDING TO MASS TRANSFER BEHAVIOR

3.1. Introduction

Biological structures and behaviour are essentiall y difticult to describe in

quantitative ternis. Thornley (1976) stated that "üt a general Irvel, it might be ürgued thÿt

the quantitative description of structure. and how the rdte of change of thrit struçturc i s

determined by the environment and by the structure itself. is the crntnil problzm of

biology". In a process like osmotic dehydration of plant tissues. these complex structures

pose a chüllenging factor to food scientists and enginrers in the optimisation procrss and

design of equipment.

As hüs been discussed in the literature review. the structure and properties oî' the

tissues affect the mass transfer phenornenon occumng in plant tissues during osmotic

dehydration. This treatment gained a lot of knowledge from studies in plant physiology

which deals for example. with osmotic cell behnviour. solute diffusibility. .iiiJ plant

water econorny (Philip, 1958). These studies have enabled food scientists to understand

the complex relationships between the plant tissue's properties and its mass transfer

behaviour.

The specific roles, however, of each of the histological components have not been

fully determined. It is still unclear what structures or properties of the tissue cause the

pürticular mass transfer behaviour it exhibits. Under the same conditions of osmotic

dehydration for example, it was found that different kinds of fruits have different mas

transfer data (Giangiacomo et al., 1987). There are also possible variations in behaviour

between varieties and cultivars of the sarne material. which rnake the ~inderstrinding ot'

the mass transfer phenornenon al1 the more cornplex.

The purpose of this phase of the research wüs to classify the m u s tri1nsfr.r

behaviour of different plant materials. In order to relate the structure and composition of

a plant material to its mass transfer behaviour. severül sets of data of water loss and solids

gain were taken from the litenture and analysed. The mrthod of classificarion wah bascd

on the description of the material's behaviour according to easily measurablr parimeters.

These allowed the classification of the material's behaviour in relation to the biisic driving

forces of mass transfer.

3.2. Methods

In order to classify different plant materilils. the mass transfer behüviour of plant

tissues was first described. Botb water loss (WL) and solids gain (SC) behüviours were

described. The behaviours were compared in two ways:

First. through behaviour in terms of rate of water loss or rite of solids gain.

Second, through the amount of water lost or solids gained after one hour of treatment.

Data were taken from existing studies published in the litrrature on the kinetics of mass

transkr in fruits and vegeiables. Graphical results were scünned and standardisrd for

interpretation. The following cnteria were followed in the srlection of data:

Information about the following should be available:

materials

1. initiai state/pre-treatment of the raw materials

2. initial composition of the plant maienal

methodology

I . shapddimension of raw material

2. concentration/composition of osmotic solution

3. time of treatment

4. temperature and other conditions of treat ment

calculation/equation for water loss or solids p i n

tabulated (numerical) andor plotted (graphical) results

To limit the variability brought about by different conditions of treütments. the

data sets used for the anülysis were those of materiais that have more or less the same

experimentûl conditions (e.g., same temperature, solute concentration).

The distribution of the data was taken and tabulated and the rate of water loss or

solids gain from initial time, !=O to equilibrium time. t== was described ns "fast".

"average" or "slow". Equilibrium in this case is the time when the rnüterial has stopped

losing more würer. In the case of solids gain. it is the time when the müterial hüs stopped

güining sol ids. The classification was done using Microsoft Excrl with the "li~stugraiti

analysis" tool thai creates a set of evenly disiributed bins between the data's minimum

and maximum values. Behaviour in ternis of nmount of wnter lost or solids gained iifter

the first hour of treatment time was described. The first hour of osmotic dehydration is

very important because at this point, most of the water loss or solids gain hüs occurred.

This part narrowed down the classes of materials described abovc. A p i n . uïirip

histogram analysis, the materids were either described to lose (or gain) a "substiintial".

"average" or "limited" arnount of water (or solids) after one hour.

After trying many different equations and models lu: well as curve tïtting toois

that would allow the cornparison of different data sets. a simple model proposed by

Azuara et al. (1992) was tried. This model has proven to fit most of the water loss and

solids gain data gathered from the literature (Appendix A).

By defining WL = fraction of water lost at time t, WL, = fraction of water lost at

eqiiilihriiim, and WS = fraction o f water that can move out but remains inside the material

at time. t then the relationship:

WS = WL- -WL (3.1 )

so that WS+ O when r+ - Since WL, has a high fixed value at any temperature and concentration and WL

;ind WS sire functions of the rate of water loss and time then WL will increase with

increüsing tirne whiIe WS decreues. The inverse relütionship brtwern WL ;iid CI'S i t ) i i l i l

then be represented by a parameter K as a function of time and rate of WL:

By assuming that water loss is only a function of time since most experiments arc

carried out at constant temperature and concentration. a simple function for K in içrms of

time ( t ) and a constant (S,) related to the water loss would be:

K = S,t

By substituting for K and WS, the equation for WL becomes:

The fraction of water lost by the material rt tirne. t can be predicted if the values

for S, and WL, are known. These can be calculated using linear regression with

experimental data obtained during short-time period. The linear fomi of equation 3.4 i s

then:

and similarly for solids gain (SG):

And its linear fom:

The values of S, and SZ were related io the rate of diffusion ut' iiiarcricii pci i m i i

time. The higher these values. the faster the rate of m a s trünsfer.

Following this model. sets of published osmotic dehydrütion data from litrrature

were taken and tested. For data published as graphs. data points were tiikrn as follows:

Graphs were scanned with U l e d PhotoIrnpact and savrd as ".jpg" files. Using

image analysis progams (Mocha and SigrnaScan Pro 3, Jandel Scientific). they were

recalibrated individually and x. y dimensions were taken for each particular point in the

graph. "x" corresponds to time in minutes. and "y" corresponds to either WL or SC. The

numerical data were then exported to Excel (Microsoft) and t N L and r/SG were

calculated. These values were plotted against time and linear regression was applied to

each curve. Corresponding values for r', dopes and y-intercept were taken. Values for S,

32

and S2 were computed and tabulated dong wi th experimental and predicted equi l i brium

values.

For datü that were not very smooth. simple smoothing techniques were used.

These include either ii linear or ü polynomial regression. Smoothed datü were

recülculated and plotted as above.

For the above procedures. precision of the scannine and point evalo:ition were

affected by the following factors:

I . Precision of the operator taking the points (such as if the exact centre of the points

were being taken). To check for this. the süme point w u tüken severtil tirnes and wtis

found precise. Zooming the graphs while clicking on the points wüs vrry helpful for

this operation.

2. Precision of the calibration procedure: x. y and z points should be properly positioned

during cülibration.

3. Precision of the graphs including the scale and the points themselves. This depends

on how well the graphs were constnicted and on its presentation or printing on the

paper. It was noticed that occasiondly, actual numerical values quoted in the

discussion did not correlate well with the points that were presented on the graphs.

For a nurnber of data sets analysed. the üuthors were contactrd for the actual

experimental values obtained. For those that we were not able to contact or have not

responded, the scanning and point evaluation rnethods had to be rmployed. The

SC hematic diagrm for the procedure is in Figure 3.1 .

Plant Material r - l L

Determine mass transfer behaviour in terms of rate of water loss

J Determine amount of water lost after i hour of treatment

Describe eüch class and type üccording tu müss trünsfer behaviour

I

Figure 3.1. Schematic diagram for classification of mass transfer behaviour.

1 l

II DESCRIPTION

3.3. Results and Discussion

3.3.1. Description of mass transfer behaviour

Dozens of journal articles were read and summarised. Those that met the srlection

criteria above were compiled. Pertinent data necessüry for the analysis were tabulated.

Due to the differences in experimental conditions. the number of data sets that could be

reliahly cornpared with each other was very limited. Since the üim of this phase of the

study w u to clÿssify different plant materials. the variability should only corne from the

intrinsic qualities or characteristics of the raw materiül themselves. This hm posed a

difficult problem and it was still necessary to üccount for these variations in treütrnent

conditions. To limit these. the solute composition and concentration usrd in the studirs

should be sucrose or mostly sucrose with Brix or prrcentape soluble solids { B S S ) values

ai around 60-70. The temperature of treatment should also br üround 10-30 "C so the

rtTects of heat could be negligible.

The size and dimensions of the samples were ülso difficult to limit. Most studies

have used practically every shape and size possible for any mütrriül so conip;trisim in

ierms of this was very difficult. The only comparable data that could be taken from this

was the amount of surface area exposed to the osmotic solution.

Some studies also followed wüter loss behiiviour only so thex cuuld nor bc

compared with solids gain values of the other materiais.

In Table 3.1 and Table 3.2, results of %WL md %SC ünülysis for .;ix di f f r r rn i

materials and treatments are shown. These values were takrn after lineür regression of

each plot of rimc/WL (or rime/SG) versus tirne (Appndix A). Values of the sloprs and y-

Table 3.1 Results of %WL analysis

Table 3.2 Results of % SG anal ysis

Cherry

Apricot a

Apricot b

0.0250

0.0215

0.0 198

Cherry

Apricot a

Apkot b

Appie a

Apple b

40.00

44.44

50.5 1

&

/S

b Material SG at'ter 1 hour

Y@

O. 1576

0.0400

0.0438

0.0453

0.0380

2.67 13

3.7786

3.3 193

skpe SG ,

%

6.35

25 .O0

22.83

22.08

26.32

1.56E-04

9.92E-05

9.94E-05

14.39

1 1 . 1 1

14.59

16.2620

1.1013

1.2377

1.91 13

0.9680

1.62E-04

6.05E-04

5.90E-04

3.95E-04

6.54E-04

1.52

16.8 1

f 6.86

14.54

19.70

intercepts (b) were tabulated to get the values ai equilibrium as well as the constants S,

and SI.

From equation 3.5, WL, is the reciprocal o f the dope (l/slope) whilc SI is the

reciprocal of the product of the y-intercept: SI= ( I / (b x WL,)). The stime ç~ilcdütions

were done for SG (from equation 3.7). SC, = ( l/slope). S2 = ( l/b x SG,)). Values of WL

and SG after one hour were taken frorn the table. In Table 3.3 is a tabulated data for WL,

and SC, as welI as SI and Sz values that includri osrnotic conditions for e x h material.

To compare the rates of water loss. S I values were taken and arrünged in

decreüsing order. The same thing was done for S?. Using histogrüm iinülysis. the mrdinn

value was calculated and the distribution of the values based on this was determined.

Values falling within the median range were described as "average" miitrrials. Those near

the highest were described as "füst" and thosr nrar the lowest value wrre drscribd ris

"slow". The snme analysis was done for the values for the amount of WL and SG aftrr

one hour. Materials whose WL or SG values after one hour are within the median range

are classitïed as exhibiting "average" behaviour. Those above it lire "substüntialtt and

below it are "limited".

It can be seen in Table 3.4 that Apple "a" exhibits f a t and substüntirl behaviour

in ternis of water ioss. Apple "a" was treaied with 704 sucrose üt 30°C. Apple "b" was

treated with 60% sucrose at 20°C. The small differences in concentration and treatment

temperature could explain some of the differences in their behaviour. In terms of size and

dimensions of the samples, ihere is a big difference. Apple "a" was peeled and cut to

Table 3.3 Osmotic conditions of materials and description of their m u s transfer behaviour

MATERIAL OSMOTIC SOLUTION T Dimensions WL, Rate of

"C W U s

Peac h 50%w/wCornSymp/Sucrose.70Bx 25 2cmthickslicrs 0.470 3.17E-04

Cherry 50% wlw Corn Syrup/Sucrose, 70 Bx 25 1.8 cm diam. 0.400 1 S7E-04

l ~ ~ r i c o t a Iducrose, 70 Bx 1 25 1 1 cm thick halves 10.441 1 .OOE-04 L ' + Sucrose' 65'2 25 1 cm haIves 0.505 1 .O()E-O J Apricot b Bx

Apple ü Sucrose. 702 30 2X 1 cm 0 .4~~0 1 3 1 E-03 disks

Apple b Sucrose. 60% 23 0.559 2,458-0.1 3-4 mm

s l ices

Se, Rate of

S GIS

l ~ e a c h 150% wlw Corn Syrup/Sucrose. 70 B X ~ 25 1 2 cm thick slices 10.049(1.35~-03

lcherry 150% wlw Corn Syrup/Sucrose. 70 B X ~ 25 1 1.8 cm diam. (0.064) 1.62E-04

L c o t a I ~ i c r G 7 0 B; 1 25 1 1 cm thick hiilvrs10.25016.05~-04

Apple b Sucrose, 6 0 4 23 34 mm thick s l ices

0.763 6.5 jE-0,

circula disks of 2.0-cm diameter and 0.5-cm thickness. Apple "bu was peeled. quartered

and sliced to 0.3-0.4-cm thickness pieces. In tems of size. Apple "bu is much bigger and

has a larger surface area available for mas transfer. In tems of amount of wiiter los[ after

one hour, both materials were classitïed. substantial. There is a difference in solids gainrd

after one hour with Apple "a" as average while Apple "b" has substantiiil behaviour.

These resiilts show that even within the same kind of materials. vüriability in behaviours

exists. These two apples are of different varieties, Apple "a" is a Golden varirry while

Apple "bu is a McIntosh. The samples where the tissues were ülso tüken fiom in the apple

could account for the differences in behaviour as observed by Mavroudis ( 1996).

Peaches showed opposite behaviours in rates of wciter loss and solids gain. They

showed an average, substantial behaviour in terrns of water loss. In terms of solids gain.

they showed a fast, average behaviour. Peaches used for this study wrrc lye peelcd and

cut in halves. This essentially increased the amount of surface ürra i t has üvailüble for

mass trrinsfer. Peach has around 8 9 4 moisture: the highest ümong the materials tiiialysrd

here. This means that it has the iowest concentration of solids and hris the biggest

concentration gradient with the osmotic solution. This big difference between

concentrations increases the rate of mass transfer.

In terms of solids gain (Table 3.5). however. peach is a fast. average materilil.

However, it has the lowest value for SG, The low ümount of solids gained could ülso

explain for their high rate of solids gain. The quantity and speed of water coming out w u

so big that it hm hindered the entrance of solutes in the materiai. This behaviour is a

complex one and ments more explanation as to why there is a slow and limited

movement in tems of the solute and much more in terms of the water.

Table 3.4 Description of water loss behaviour based on SI

Material 1 CIass

Apple a 1 Füsi 1 Substantial 1 Peach 1 Average ( Siihstantitil 1 Apple b Average Substantial

Cherry Average Average

Apricot b Slow Average 1

Apricot a Slow Li mi ted

Table 3.5 Description of water loss behaviour based on S2

Fast 1 Average

Apple a L

Apple b

Apricot b

Apricot a

Cherry

Average

Average

Average

Substrintial

Substantial

Substantial

Average ,---

Slow

Substantial

Limited

The relationship between the rate and amount of mass transfer should he undcrstootl

further. This attempt to understand this relationship will be further discussrd Iüter as the

other materials and their behaviours are anal ysed and reviewed.

Going back to Table 3.4 and Table 3.5. apricots "a" and "b" have dow wütsr loss

rates. The amount of water lost wüs from limited to average. In terms of solids gain. both

apricots rxhibited average behaviour. These results show that there is n o sigiificani

difference between two materials with the same size and dimensions. The small

difference between a limited riite for apncot "a" and an average rate for apricot "b" for

water loss is most likely due to the difference in composition of the osinotic solution.\.

Apricot "b" was treated with a solution çontaining fructose. glucose and suçrose. Apriçot

"a" was treated with sucrose solution only.

Due to the effects of size and geometry on the values of S, and 4. the rates of

mass trünsfer were expressed in tems considering the dimensions of the tissue and the

O the volume ;inci parameters. S. and WL or SG üt equil ibrium. This involved approximiitin,

areas of the materials involved during the process. Approximation was done as best as the

method of sample preparation was described in the literature. However. there may still

have been discrepancies in interpreting the description of the initial geometries of the

samples. Some of the varieties of the materials used are not üvailable in North America

and the bases for some of the dimensions used were those of products available here.

A relationship for the flux. J , was derived as follows:

And with linear regression,

Where:

LI, = 1 1 and u, =-

WL, * S WL,

(3. IO and 3.1 1 )

Substituting values for a and al

Water loss is therefore. equal to:

The derivative of this equation is:

Since the flux density of water. J,,, is the rate of diffusion of the substüncr prr unit

area or ( VA) (dWUdt), where A is the area of the materials across which the substance is

diffusing (Nobel. 1983). For an initial mass of material. mci. the reltitionship from

Equation 3.1 4 can be expressed as:

JWL J,,,A -=- dt m,

By replacing Equation (14) in (15) and rearranging, the flux c m be expressrd as:

At the initial time, t = O, when the structure is still unaltered. and for t = O

Then,

Substituting for cru.

The value for the mus. >no, since it is not usually available can be tüken from the

product of the volume. Vo , and the densi ty, p:

For solids gain, the equation for the tlun of the solids is:

Equations 3.19 and 3.20 give values for the actual riites of mus trdnsfer for water

and solids through the surface of the tissue during osmotic treatment with consideration

for the geometry and volume involved in the process. Using thex equütions. a different

behaviour was apparent than those derived from the S parameters. In Table 3.6 and Table

3.7. J,b. and Js vaiues of the six materials are shown. Apple "a". whiçh had rxliibitrd the

fastest rate based on the S parameters has the second highest rate of diffusion for waier

Table 3.6 Results of water flux ünalysis

I~aterial 1 WL , ( Si 1 Volume 1 Area 1 Density ( MIA 1 J

Peach

Cherry

Apple b 1 0.56 1 2.458-04 1 5.000E-06 1 4.5 16E-03 1 800 1 0.80 1 I .I 1 E-O4

Apple a i

Table 3.7 Results of solids flux aniilysis

0.47

0.40

I~aterial( SG , ( SI ( Volume 1 Area 1 Density 1 M/A 1 J , /

0.49

3.178-04

1 S7E-04

1.3 1 E-03

Cherry

Apncot a

6.78 1 E-05

2-77 1 E-06

Apricot b

1.57 1 E-06

0.063

0.250

Apple a

Apple b

1 .û45E-02

2.8598-04

i

- -

0.228

9.425E-O4

1 HE-04

6.05E-04

0.22 1

0.263

10 12 1 6.57 1 9.77844

5.90E-OJ 2.4436-06

1 O67

787

2.77 1 E-06

2.443E-06

3.95E-û4

6.558-04

1 0.34 1 6.48E-04

1.4668-03

1.3 1

2.8598-04

1.4668-03

1.57 1 E-06

5.0ûûE-06

8.4 I E-04

1 O48

1 O67

1048

9.425E-04

4.5 16E-03

1 -75

10.34

1 -75

2.3jE-04

787

800

1.06E-04

2.64E-O4

1 -3 1

0.89

1.l4E-O4

1 S3E-04

but the lowest rate for solids. Peach has again the highest rates for both wiiter and solids.

Apricots have essentially the same behaviours based on tluxes as they were based on the

S parameters. Again, the cherry and üpple "a" seem to have 1i randoiii bcliavioui ~ i i l i

respect to ntes of diffusion or mass transfer fluxes for water and solids. The descriptions

based on the fiuxes and the amount of water lost and solids saineci rire shown in Table 3.8

and Table 3.9.

3.3.2. Classification of plant materials

The pre-clÿssitïcütion of the mass transfer behaviour of the six different materials

is in Table 3.10 and Table 3.1 1 , The classification for the behaviour of the rates of mass

transfer was based on the values of J,, or J , .

Materials belonging to Class I (fast) were those that lose water and r e x h

equilibrium fast. Class iiI rn;lterials (slow) wrre those that lose water at a vrry slow rate

that rquilibrium is reached after a long time or not at dl. Class II materilils rxhibited

water loss on an average rate that fall between Classes I and [II.

Those that lose the most or substantial arnount of water after the first hours were

categorked lis Type A materials. This was followed by those which lose an average

ümount of water as Type B. Those which [ose the les t or limited amount of wtiter are in

Type C. Although it was expected that Class 1 materials would likely lose the rnost

amount of water, this classification together with the first one would accommodate for

whatever variations there were. The same classification was done for solids gain where

"rate of water loss" was chmged to "rate of solids gain". Similady, the "amount of water

lost after one hour" was changed to "amount of solids gained after one hour".

45

Table 3.8 Description of mass transfer behaviour büsed on tluxrs. J,,

Peach

Apple a

picot b 1 Slow 1 Averrige 1

Cherry

Apple b

I ~ ~ r i c o t û 1 Slow 1 Limited 1

F;ist

Fast

Table 3.9 Description of mass translér behüviour büsed on fluxcs. J ,

f Substantial

Substantiai

Average

Slow

Average

Substantiül

Average

Substantial

1

Apple b

Apricot a

Peach

Apricot b

Apple a

Cherry

Fast

Average

Average

Average

Su bstanrial

Substantial

Slow

Slow

Substantial

Limi ted

Table 3. IO Classification of water loss behaviour based on tluxes

III

Class 1. Type A materials lose a substantial amount of water after one hour of treatment and has a very

Fast rate of writer diffusion.

example: peach. apple (Golden var.)

Class II. Type A materials lose water at an average

rate but lose a su bstantial amount of water after one

hour of treatment.

Class Ill, Type A materials lose water at ri slow rate

but lose a substantial amount of water after one

hour of treütment.

example: apple (Macintos h)

Clus 1. Type B müteriüls lose an average amount of

water after one hour of treatment and has a very

fast rate of water diffusion.

Clüss iI. Type B materials lose water at an average rate, also lose an average rimount of writer after one

hour of treatment.

example: cherry

Class III. Type B mareriüls lose water at a slow rate

but lose an average amount of water after one hour of

treatrnent.

example: apricot

Class 1. Type C materials lose a limited amount of water after one hour of

treatment and hüs a very fast rate of water diffusion.

1

C l a s LI. Type C materials lose writer rit an avemge i rate and lose ti lirniteti i

amount of w t t ' r ;it'tt'r ont' hour of treatment.

Cliiss III. Type C m:iieritilh lose wiiter rit a slow mtt '

and lose ii liinited amourit of watcr after one hour of

Ireatmen t.

Table 3.1 1 Classification of solids gain behaviour based on tluxes

Clüss 1. Type A materials gain a substantial amount of solids after one hour of treritment and has a very

h s t rate of solids diffusion.

Clüss II. Type A materials gain solids at an average nite but gain a substantial amount of solids after one

hour of treatment.

example: apncots and apple (Macintosh)

Class III. Type A materials gain solids at a slow rate

but gain a substantial amount of solids after one

hour of treatment.

example: apple (Golden)

Class 1, Type B materials gain an average amount of

solids after one hour of treatment and ha'; an average rate of solids

di ftùsion.

exümple: Peach

Class II, Type B materials gain solids at an average rate. and gain an average amount solids after one

hour of treatment.

Class HI. Type B materials gain solids at a slow rate

but gain an average amounl of solids after one hour of

tresitrnent.

1 Type C I

gain a limited ümount of 1 solids : i k r one hoiir of

trtxtinent aiid lm ;i h i c rate of solids diffusion. ;

Class gain solids water at / an average rate and gain i t 1

limited amount of solids rifter one hour of t reritment . 1

examplr: cherry l

Class III. Type C materials gain solids at a slow rate

and gain a limited amount of solids after one hour of

treatmen t.

The classification done here is just preliminary and based only on the materials

studied here. Also. there is still a need to verify these methods. The models that were

used have to be experimentally verified with osmotic treatments done within the same

conditions for al1 materials. A real comparison could be done only when dl othrr

variables are controlled. If this classification works. then i t will also allow the pi.edictioii

of mus trtinsfer behüviour of üny plünt materid based on the initial characteristics of the

material.

This method of classification. however. could be applied and adidüpted to sets of

data without limit to the geometry of the samples. This is an üdviintage becüuse the nature

of the plant tissue's geometry could not always br controlled. Small fruits likc çiirrrirs

and blueberries. for example. cünnot be cut and sliced further.

From this classification. the identification of ihr tissue structure and propertks

that control m a s transfer behaviour still cannot be ascertained. tt is still unclear ris to

what rxiictly rnakes one materiai behave differently than the othrr. Again. the

complexities of the structure and the variabilities between materials and even betwern

vürieties or cultivars rnake characterisation difficult. To make a more meaningful

chüracterisation, it is necessary to study the plünt tissue funher. This will be done in the

following chapter where kinetics studies on apple tissues iit different sucrose

concentrations will be discussed. This ntxt chaptcr will also verify the method of

classification by developing methods that will characterise the mass transfer behaviour of

apple tissues.

4. CHARACTERISATION OF MASS TRANSFER BEHAVIOUR OF APPLE TISSUES

This phase w il1 characterise mass transfer behaviour of plant tissues during

osmotic dehydration at n macroscopic level. The study has been focused on üpple tissue

because of its interesting cellular structure and high porosity. As well. osmotically

dehydrated apples have high processing value and end use.

For this phase. the following are the objectives: first, is to pre-treat the sample by

vacuum infiltration thus, eliminating most of the air. Second, to determine the kinetics of

water loss and solids gain during osmotic dehydration of apple tissues. And lastly. to

characterise the mus transfer behaviour of apple tissues at different sucrosr

concentrations based on the data acquired from the kinetics study. The rnodels and

methods used in the classification of mass trrinskr behaviour of dit'ferent m;iteririls t'rom

the previous chapter will also be tested as to its validity in this phase.

The purpose of vacuum infiltration is to decrense the porosity or air spacc in the

tissue and to increase the turgidity of the cells. As has been previously mentioned. air

space accounts for as much as 25% of the total volume of the applr. Vacuum infiltration

measurements also gave a rough approximate of the porosi ty of the apples being studied.

Studying the kinetics of water loss and solids gain provide a very good

description of how the tissue behaves during osrnotic treatment. This study takes us from

the cellular approach to what could be termed as the "slice approach": studying bigger

pieces of tissue as it is in actud processing.

Hence, this phase is essentially a "macroscopic" study. The term "mücroscopic" is

being used to difierentiate from the "rnicroscopic" work that has been done using laser

50

scanning confocal microscopy and image analysis in the next chapter. With these two

methods, the research hopes to provide a more detailed quantitative description of the

behaviour of apple tissues during osmotic dehydration.

This characterisation aims not only to quantify mass transfer but also to quantify

al1 the other changes in the slice. These mostly include changes in the physicül propenies

of the materia! such as density. thickness and surface area. These propenies when

measured could give more detüils on the quantification of the slices' behüviour. Most of

the methods used to measure these properties were developed especiall y for this study or

adapted from previous similar studies.

4.2. Materials and Methods

4.2.1. Sample preparation

Granny Smith apples were purchased from a local supermarket in Guelph.

Ontario. They were refrigerated at 4" Celsius until needed. These apples were products

of Chile and the same lot of apples was used throughout the entire experiment except for

the second mn which had to be repeated and ünother box was purchased.

Tissue sarnples were taken as follows (flow diügram in Figure 4.1 ): each apple

was sliced crosswise, tÿking the widest part of üpproxirnately 2.5-cm in height using a

sharp kitchen knife and a d e r as a guide. A 2-cm diameter cork borer was used to take

four cylindncal samples about 90-degree angle from each other. Using a hand-

microtome, and a sharp single-edged blade. about 10 to 15, 2-mm slices were taken from

each of the cylinders. Each slice had dimensions of approximately 2 centimetres in

2.5 cms 7

Figure 4.1 Flow diagram for sample prepüration

(Iilustration by Julie Jee)

diameter by 2 millimetres thickness. The slices were kept covered in a petri dish on ice at

approximately JUCelsius until al1 the samples were sliced. When ail samples were sliced.

they were weighed and immediately immersed into a 2% ascorbic acid solution for 5

minutes to prevent enzymatic browning from occurring. After the üscorbic acid

treatment, the slices were blotted dry and weighed again.

4.2.2. Vacuum intiltration measurements

A preliminary experiment was done on apple tissues through vacuum treatment

and infiltration. This was done for two main reasons: first. to approximate the porosity of

the tissue iind second. to determine the optimum conditions for pre-treüting the .;iimplr\

to full turgor before osmotic dehydration studies. A vüriety of treatment and infiltration

t imes were conducted.

The samples were treated as foliows: tap water was drgassed overiiiglii aiid dieri

kept sealed and cold at 4"Celsius in the labontory refrigerator until use. The slices wrre

placed in a vacuum flask and the previously degassed tüp watrr was üdded just cnough to

cover the samples. The flask was attached to a vacuum source iind ri vacuum was applied

to take the air out.

For vacuum detemination merisurements, different times of treatnient and

infiltration were done. For al1 other experiments, the vacuum was broken after 15 minutes

and the samples were kept in the water for another 15 minutes to al

infiltrate the tissues. After this. the snmples were blotted and weighed

samples were ready for osmotic treatment. The following properties

ow the water to

agüin. Thrn the

were measured:

moisture content, insoluble solids and density. These values were needed to approximate

the volume of air (and porosity) in the tissue.

4.2.3. Kinetics of water loss and solids gain

To get a thorough description of the applr tissue's behüviour during rhr ire;iiiiir.ni.

as well as to facilitate the compiitation of w;iter loss and çolicls $;lin. the fnllowing

properties were determined:

4.2.3.1. Measurement of properties

r Moisture Content - by oven drying the tissue siimples. tlir inuss of rotal

solids left can be determined. By subtracring this t'rom ihc O[-ipiniil

mass before drying. the umount of rnoisture is known. This value wris

used for calculating water loss.

i Insoluble Solids - this technique. ülso by oven drying the filtrüte after

homogenising and filtering the samples, gives a rough estirnate of the

amount of soluble solids in the material. This is ;i more accurate

technique than refractometry, which gives Brix values to the first

decimal point only.

k Sugar Content - using High Performance Liquid Chromatogmphy

(HPLC), the actual sucrose that is in the slices ut uny timr during

osrnotic treatment can be determined. As well, the concentration of

other sugars such as glucose and fructose can be meuured. Solids gain

values were cdculated from the mass of sucrose.

't Density - density values were calculated by measuring the volume of

the slices using a pycnometer. From this. the porosiiy of the r IWC ;ir

known.

k Thickness and Surface Area - using a calliper and image ünalysis.

measuring thickness and surface iirea of the slices after eüch irelitment

gave an approximation of the shrinkage as well i ls the direction o f

shrin kage.

4.2.3.2. Osmotic solutions

Osrnotic solutions were prepared using tap wüter and sucrose (Lrintic fine white

sugnr from Toronto. Ontario). Tap wüter was used insteiid of distillrd water becausc i t

has a closer composition to the cell's water than distilled water. terms of minera1 content.

at least. Vürious concentrations of solution were prepüred ( 10. 20. 30. 40. 50. ;ind 60%

wlw). About i kg of each concentration was prepüred and 5.0 g (0.5%) of nscorbic x i d

was iidded to each solution. The solution was mixed using a stirring rod unti l the sugiir

and ascorbic acid were both dissoived. The solution was adjusted to a temperüture of 20"

Celsius and the Brix was measured. The solution was then refrigerüted at 4" Celsius until

use.

Just before each run, the solution was heated and then kept on a hot pla te stirrer at

30" I 2" Celsius during osmotic treatment. The solution was continuously rigitated in the

glas beaker using a magnetic stirrer in order to circulate and homogenise the solution.

The samples were protected from the effects of agitation using a plastic mesh basket

inside the 2-L giass beaker. The set-up is illustrated in Figure 4.2.

mügnetic stirrer

Figure 4.2 Set-up for Osmot ic Dehydrdtion Treritinrni

The sample to solution ratio was about 3:20. After the 3-hour run and completion

of ail sampling, the solution was cooled back down to 20" Celsius and the Brix was

measured and recorded again.

4.2.3.3. Sampling procedure

1.2.3.3.1. Kinetics strcdy

For rach of the concentration nim. 299 slices were usrd per solurion

concentration (six runs in total). For every run. five replicates were perfomed üt O tirnr

and three replicates were performed for the other sampling tirnes. After the vacuum

preconditioning treatment. 65 slices were taken out for the O time. "fresh" simple

determination. The rest of the slices were immersed into the osmotic solution bath. At

the various sampling times ( 15. 30.45.60. 120. and 180 minutes). 39 \oinpls\ WIY iahcii

out and carefully blotted dry with paper towels to remove rxcess moisture and syrup. The

slices were then distributed for the determination of the following: inoisture content.

insoluble solids, sugar content. density. thicknrss and surface area. Ttir h~iiiplirig Jiü, * r m

is shown in Figure 4.3.

4.2.3.3.2. Procedurrsjor analysis

4.2.3.3.2.1. moisture content

After the blotting of the apple slices, the samples were distributed to labellecl. pre-

weighed aluminum dishes. Three siices were placed into each of the dishes. These

samples were then immediately weighed and placed in a dessicator until al1 the other

moisture samples were ready. Once the sampling had been completed, they were taken to

Figure 4.3 Sarnpling Diagram for Kinetics Study

apple slices m 1 weighed

vacuum infil trated

weighed ii

1 slices distributed ar:

al1 masses from 60 slices=rn, 1

osmotic solution

I ( 10,20,30,40,50 or 60%)

(continued next page)

39 slices taken at each tirne: 1 5 , 30.45.60, 120

and 180 minutes

siimples distri buted as: Ï

a11 masses frorn 36 slices=m,

LEGEND:

t = time MC = Moisture Content IS = Insoluble Solids SU = Sucrose Content p = Density SA = Surface Area and Thickness m, = mass at time, t m, = mius at time, O

the vacuum oven to be dried. The vacuum oven wüs set iit a pressure of 27-28 inches Hg

at a temperature of 70' Celsius for 6 hours. The samples were then rernoved from the

oven and placed back into the desiccator to be cooled for at l e s t half an hour at room

temperature. The sarnples were then weighed and recorded

4.2.3.3.2.2. insoluble solids

The prepared samples were distributed into labelled. pre-weighed 50-mL plastic

vials. These samples were weighed again and recorded. and then frozen tit -19" Celsius in

the laboratory freezer for later ünalysis. Once they were ready for ünülysis. the samplrs

were thawed and the viüls were half fillrd with distilleci water. The siimplrs wrrr

homogenised using a disrupter. Polytron 2000. at a dial speed of 4 for about 15 to 30

seconds or until noticeably homogenised. With ii wüsh bortle. ü n y snmple

residue/insoIuble particles still remüining on the disrupting probe was rinsed off back into

the plastic vial.

The samples were then filtered through labelled and pre-weighrd 7~-rniii.

Whatman no. 4 qualitative filter püper. A Buchner funnel and a vacuum source wüs usrd

io facilitate the filtration. Agüin. a wash bottlr wüs used in order io rinse out ;il! of the

insoluble particles out from the via1 ont0 the filter paper. Filter püpers were then blded

and placed on aluminum dishes. They were plüced in a dessicator until al1 the samples

were ready. The samples were then dried in the vacuum oven uiider the .*aine coiidiiioii.r

as the moisture samples: set at a vacuum pressure of 27-28 inches Hg iit ;i temperature of

70° Celsius for 6 hours. Upon drying, the samples were plriced in the desicçiitor for hülf

an hour and then weighed and recorded.

4.2.3.33.3. sugar content (with HPLC)

Three slices were placed on previously weighed 50-mL vials and frozen until d l

the other samples were ready for analysis. When al1 the samples hüd bern prepared. 10

mL of distilled water was added to each via1 using a precision pipette. Samples were

homogenised using the Polytron 2000 üt the speed setting of 4 for 15-30 seconds.

Sarnples were then filtered using a syringe and n 0.2 pm pore size. 27-mm diameter nylon

filter. Using a micropipette. 50 pl of the tiltered sample was plücrd in n 1-inL insrrt.

This was diluted with 150 pl of 80% acetonitrile. The insens were placrd inside a spring

that was placed in a 2.5 mL via1 covered with septa caps for HPLC ünalysis. Standards of

sugars used in the run were fructose-glucose-sucrose solutions with the following

concentrations: 2.5. 1.5 and 10.0 rng/mL, respectively. A low concentration standard wns

also prepared with approximateiy half the concentration of rüch of the supars.

HPLC set up was as follows: the system was a Waters 700-WISP with ;t Waters

4 10 Differentiai Refractive Index Detector. The çolumn used w u ;L Joiics iiiiiirio culuiiiii.

1M25348. which was 150 mm long. 4.6 mm in diümrtrr. Sample sizc was 1 0 iiL witli

rate of acquisition at 2.00 points1 second for 10 minutes. Flow rate wiis 2.00 n iUmin i~ i r .

From the readings given by the HPLC which were compüred with the conçenrrcirions of

the known standards, sucrose concentration was calculated taking into nccount the

dilution rüiio, the mus of the sample and the moisture content (detailrd procedure ih un

Appendix B).

4m2m3.3.2a4m density

A custom-made pycnometer was made with a 50-mL Erienmeyer tlask. a rubber

stopper with a small hole ai the centre and a glus capillary just big enough to fit in ihe

hole. The stopper was marked around where it closes with the mouth of the flask to make

sure that the wüter level is always the same. Density was meüsured using d~aiilicd wi~ittr

at 20 OC (for detailed procedure, refer to Appendix C).

4.2.3.3.2.5. thickness and surface area

After each sampling tirne. one slice was taken and placed on a Whatrnan # 1 filter

paper. The slice was lifted up righi nway and the miirk of the slice'ç edge left ' i o n the paper

was traced with a Pen. The same slice was measured for thickness using a Vernier

calliper. The filter paper with the traced edges was scanned using ii tlatbed scanner

(Microtek) and saved as JPEG (".jpgU) files. Using SigrnaScan Pro 3. the images were

calibrated and the edges on the scanned images were trxed ügain. From this. the surface

area of the slices was determined. Whrn multiplied by the thickness value. the volume of

the slice could be determined.

4.3. Results and Discussion

4.3.1. Vacuum infiltration

Results for vacuum infiltration rneasurements are shown in Table 4.1. A number

of vacuum treatment and infiltration times were done. This was essentially a pre-

trentment of the apples that will be used in later studies as well as to determine the

porosity of the apples used. Measurements on moisture content. insoluble solids, soluble

solids and density were made. The values are averages of 3 replicates (3 sl ictislreplicate 1.

in percentage, with their conesponding standard deviations.

To calculate for the volume of air, the above values were expressed as weiglit

fractions (Table 4.2). The standard deviations for the miss of the slices were within the

Table 4.1 Vacuum Infiltration Measurements on Granny Smith Apples

Time of treatment/ infiltration (minutes) Moisture Content,% Total

Insoluble Solids, % Solu bie Solids, % Density. $/ml

O/O

87.2 +,O.OS 12.8

1.28 f0.09 1 1.52 f0.02 0.888 t0.024

I /O

90.1 20.02 9.90

90

89.8 k0.002 10.20

1.18 f0.19 8.72 f0.02 0.993 f 0.043

5/30

90.0 I 10.04 9.99

l .O6 i0.07 9.14

k0.002 1.015

10.044

15/15

89.5 40.03

5/60 1 15/0

I .O8 10.05 8.28 f0.02 0.991

k0.030

9.96 8.80 1 10.5

90.4 tO.O1

91.2 k0.02

1-20 j f0.05 j 9.30 1

kO.002 1

1 .O4 f0.09 8.92

k0.02 0.995 1 .O23 . I .O04 ' k0.02 1 20.002 1 c0.002 1

0.98 k0.07 7.82

k0.001

range of + 0.06 to 0.16.

The volume of air was computed as follows:

Vd,. = VI, + li, + V.1 + Vu,,

Where:

V = volume of slice

V,, = volume of water in the slice

V,, , voiume of insoluble solids in the slice

Vt, = volume of soluble solids in the slice

V,,,, = voiume of air in the slice

Since density, p =mïiss / voliimc. the mus of the slicr. n i = Vp. tlien tlic vdurnt.

of the slice can be expressed u:

Volume of air is then,

Porosity wüs calculated by dividing volume air by the volume of slice.

Table 4.2 Porosity Measurements on Apple Tissues

Tirne of Treatmenthnfiltration

(min) Average mus. grams

Average volume. @m.'

Density (slice), @rn3

Density (w)

Density ( d i s )

Vol. Air/slice. cm"

Porosi ty

The volume of air in the apple tissue could be assumed eqiiivaleni ro the \wI i i i i i c

of the intercellular spaces in the tissue. This c m be illustrated üs follows:

Figure 4.4 Cells in fresh tissue and the distribution of main cornponents in the tissue

After vacuum treatment. the tissue will look like this:

writer in-

air uut

Figure 4.5 Cells in vacuum-infiltrated tissue

Insiiiublcli

Solubles

Figure 4.6 Cells in water-filled tissue

When the vacuum source was broken, the space that the air has left was infiltriteci by

water. The amount of intercellular space (as air) decreased as air was being sucked out of

the tissue. Thus, the cells will look like that in Figure 4.6. The protoplut pressed against

the ceil wall as its turgidity increiised. Very little amount of extra-cellular space hris

remüined. if any.

Frorn Tahle 4.2, the values of porosity have gre;itly decre:isrtl :is wciiiim

treatment time was increased. This wiis consistent with the expectad brhÿviour. The vülur

for porosity of the fresh (time=O) sample is lower than the reponed value for Granny

Smith (0.18. Fito et al., 1993). Mavroudis' ( 1996) study reported a value of 0.35 for the

outer tissue and 0.14 for the inner tissue. The samples used for this study were taken just

about the middle of the apple diameter so the value 0.119 fiills just within the ranges of

the other results.

Reeve ( 195 1 ) used vacuum infiltration to measure intercellular sp;ices of different

varietles of apples. He found out that there wüs no significiint difference whrn sümplcs

"ive more were infiltrated for more than 15 minutes. Samples infiitrated less thiin ihnt ,i

variable results. According to that study too. it takes another 15 minutes iit iiimospheric

pressure for samples to show complete translucrncy. At this point. the w t r r ihiit hüs

replüced the air has entered the cells due to osrnosis and hüs caused the cclls to brcome

very turgid (as shown in Figure 4.6 above). These conditions were used 3s rhr in i i id prc-

treatment of the apple slices that were used in the following osmotic dehydrütion studirs.

4.3.2. Kinetics of water loss and solids gain

Waier loss values were calculated frorn the moisture content of the apple slices

determined rfter each sampling time (for the cornplete method of cülculation. ser

Appendix D).

Water loss is defined as:

m, WL = ww, - wiv, - mu

Where:

WL = water loss, the amount of water lost at any tirne, t

WwO = weight fraction of water rt time O

Ww, = weight fraction of water at time t

rno = mass of sarnple at time. O

m, = mass of sample ai time. r

Solids gain w u calculated as:

l i t , SC = ws, -- Wv,,

111,

W here:

SG = solids gain. the amount of water lost at iiny tirne. t

Wso = weight fraction of solids at time O

Ws, = weight fraction of solids at time t

mo = mass of sample ai time, O

mt = mass of sample at time, t

The cornplete meihod for ciilculating SC is ülso in Appendix B. Cülculated

percent water loss ( WL X 100) values were plotted against time Figure 4.7 Propagation of

errors gave % emr values from 0.09 to 2.18 (error values are tabulüted on Appendix E).

At sucrose concentration of 10%. WL started at 3.57% rifier 15 minutes of

treatment and increased until 60 minutes. After an hour. the trend hüd changed and there

were considerable fluctuations until 180 minutes of treatmrnt. There are a k w possible

causes for this fluctuation. First. is the vnriability of the tissue itself: this is variability in

the tissue's response to the osmotic solution. Second. the concentration of the sucrose

solution is too low to drive the water out. Most of the water iost cm be ;~t tnbuit 'd LU W;L[CI-

lost from the surface cells only. The panicular apples used here have an nverücgr soluble

solids content (after vacuum treatment) of 8.30%. This concrnts;ition is o ~ i l > ~ 4ightly

lower thün the 10% osmotic solution. In this case. very little water should be going out of

the tissue to establish equilibrium. If the osmotic solution has only a slightly highrr

concentration than the tissue, then how was 17% water loss attuined in th15 c i id ' Thth

behaviour would be agüinst the luws of osmosis that stated that writer movrs f s m ü

region of higher chemical potential to a region with a lower chemical potential. In this

case a concentration as low as 10% seemed sufficient enough to drüw watrr out. One

possible exphnation for this occurrence is the effect of the pressure inside the crll. This

pressure could be strong enough to push water out of i t beyond the threshold of

concentration limits.

At 2096, where this concentration is higher than the tissue's concrntration. water

loss was still fluctuating during the first 30 minutes of treatment. After that. a sready

increase of water loss was observed throughout the rest of the run. A maximum of 18.2%

l O t 1 1 1 1 1 1 T

0 20 40 60 80 100 120 140 160 180 200

Time - - (minutes)

O 10 - - O20 A30 X40 X50 060

Figure 4.7 Kinetics of wüter loss of apple slices in different sucrose concentrations

WL was attained after 180 minutes of treatment.

A predominantly increasing trend was observed in al1 the other concentrations

(30.40,50 and 60%) from 15 minutes of treatment to 120 minutes when values strirted to

taper off and the curve behaved asymptotically until 180 minutes as rquilibrium was

üpproached.

Solids gain values were similarly plotted (Figure 4.8) and the same behaviour was

observed in terrns of trends between concentrations. There were ;ils0 fluctuations in the

10 and 208 sucrose treatments. The higher concentrations have increasing values of

solids gain as treatment time continued.

The solids measured here pertain to sucrose only becüuse of the assumption thüt

only sucrose moved into the cell and there w u no leaching of solids from the tissue.

Also. it was assumed that there was no hydrolysis of the other sugars present in the

samples. This was highl y possible since the temperature and the acidity of the treÿtment

were low enough.

It was observed that after 15 minutes of treütment, both %WL and %SG values at

60% are lower than those obtained from the 50% treatment. This was possibly dur to

boundary layer of solution at the interface of the slices and the solution. This hoppened

because the speed of agitation that was applied during the treatments were constant so at

higher concentrations, the actual velocity of agitation actually decreiises as the

concentration of the solution increases. However, as more water cornes out, this was

washed out and the iransfer of both components retumed to a normal rate.

Time (minutes)

Figure 4.8 Kinetics of solids gain of apple slices in different sucrose concentrations

4.3.3. Inverse polynomial fitting

To get more meüningful information. and to verify the methods of classification

that were previously done. the mode1 of Azuarci et al. (1996) used in Chüpter 3 wüs

applied to ihese data. Again. tirne over WL (or SG) was plotted agninst time (Figure

4.10). As was t/SG (Figure 4.12). The mode1 Fit the sets of data very wcll rxccpr for rhr

WL results from the 10% treatment ($ = 0.79). Othcr thiin thi*. the coi.rt.l;iiiiui

coefficients were in the range of 0.93 to 1 .O for WL and SG (cornplrtr r rsul ts are in

Appendix F). The îïttings for the SG values were rspecially very p o t i r v e i i ur 10%

(0.99).

As in the case of the litrrature data from Chüpter 3. ~ h c iriiidel *A:, ii*ccl i o

estimate values at equilibnum as well as the S parameters to describe rntes of mass

transfer. The results for this analysis are in Tüble 4.3 for WL and Tüble 4.1 for SG.

Comparing S, values for water loss. the table shows thüt the 50% treütmrnr hiis the fiistrst

rate of diffusion among the six sucrose concentrÿtions. This was followcd by 30. 40. 60.

20 and tïnülly. 108. This means that very high (60%) aiid very low concrntr;itions ( 10

and 20%) were not effective in facilitating water loss in apple tissues. The high viscosity

of the 60% suçrose solution could contribute to the low rate of mass transfer. The

concrntrated solution could have blocked the surface of the sümple and this hindered the

outward rnovement of the water. Ar the same time. very low concentrations were not

enough to drive water out of the material. In ternis of Sz. a slightly sirnilu behaviour is

observed. Too high or too low sucrose concentrations mean lower rates of water loss or

solids gain.

Time (minutes)

Figure 4.9 Inverse polynomial fitting of water loss vs. time at different sucrose concentrations

Figure 4.10 Inverse polynomial titting of solids gain vs. time ai different sucrose concentrations

Table 4.3 Results of % WL ünalysis

Table 4.4 Rrsults of 56 SG anülysis

i

S b Concentration WL ;ifter 1 hour

SG ;iftete 1 h w r

'ïr l

2.8 1

6.3 1

1 1.63

19.72

24.1 O

3 1.79

slope

S2

0.027

0.047

0.041

0.052

0.029

0.023

WL,

b

6.842

2.383

1.478

0.721

0.855

0.859

Concentration

9%

1 O

20

30

40

50

60

dope

O. 187

0.1 13

0.060

0.037

0.025

0.0 19

SG-

%

5.34

8.85

16.60

26.68

39.79

52.45

The flux of water and solids (J , , and Js) were also computed for these data. As a

review. again, the S constants are releted to the rate of water loss or solids gained where

I/S, and 1- are equivalent to the time required for half of the diffusiblr müterial (water

or solids) to diffuse out or in (Azuara. et al.. 1996). The S parameters buiçall y putain to

how fast equilibrium is attained. On the other hand. J , and J, . refer to the ümounr of water

or solids, respectively. crossing a certain areü of siimple per uni t tiiiic iNohlc. IL)sZi.

These are the m a s transfer fluxes for water and solids. respective1 y .

Values for J,,. are tribulared for etich sucrose concentration rire in T~iblc 4.5 iirc

highest at 50 and 6 0 4 . Values üt 30 and 40% are quire similar and values dccrrasrd froin

there as concentration decreased. In t ems of solids gain. the J, values (Table 4.6) wüs

highest at 40%. then at 50 and 60%. which are almost the same and cigain dc~ rca \ i c~ i ;t[ 30

to 20% and the lowest was at 10%.

From the tables, it is noticeable that at some concentrations. 3 hours wris not

sufficiently long enough to relich equilibrium. both for WL and SG. The purpose of this

experiment was to study the kinetics of water loss and solids gain only ai short tiines and

to be able to predict equilibrium values from these data. From the shon-time rxperiment.

the predicted equilibrium values should be close enough. Azuara et al. ( 1996) proved this

by limiting the times of longer experiments and then using their mode1 to predict

equilibrium vaiues.

The predicted values were not far from the experimental values. From these data.

the predicted value at equilibnum for 10% is higher than that for 204. However, the

caiculated values from equations 4.5 and 4.6 were plotted together with the experimental

Table 4.5 Resuits of wiiter tlux aniilysis

Table 4.6 Results of solids tlux analysis

Concentration

'30

1 O

20

30

4 0

50

60

W L

Wkg

0.22

0.2 1

0.31

0.38

0.45

0.58

Concentration

%

1 O

20

si

/ s

2,4E-04

3.8E-04

1.2E-03

9.7E-O4

1.4E-03

7.OE-04

SG,

k g h

0.05

0.09

Volume

m'

6.28E-07

6.28E-07

6.28E-07

6.28E-07

6.28E-07

6.28E-07

S2

/s

4.6E-04

7.9E-O4

Area -!

rn-

7.540E-04

7.540E-04

7.540E-04

7.54OE-04

7.540E-04

7.540E-O4

Volume

m"

6.28E-07

6.28E-07

Density

kg/rn'

100 i

100 1

1001

IO01

100 1

100 1

Area

m- 7

7.540E-04

7,540E-04

iM/A

m

0.83

0.83

0.83

0.83

0.83

0.83

Density

100 1

1 00 1

J w

ks/c ni2$ 1

4.4E-O5

6.6E-05

3.1E-O4

3.1 E-O4

5.2E-04

3.4E-O4

M/A

kg /mhkg/m2

0.83

0.83

J s

kg/(m2s)

2.OE-05

5.8 E-05

values of water loss and solids gain in Figures 4.1 1 and 4.12 and these plots proved that

the mode1 gave a good fitting to the expenmentiil data.

The expected trend of increasing rates of mus transfer as the concentration of

osmotic solution increases was not exhibited. The resistances in the material and in the

solution have to be determined to account for this discrepancy. This will be discussed

more later in the chapter.

Results from the other determinations are as follows: for density of the slices.

there was an increasing trend as time of treûtment and concentration increüsed (Figure

3.13) for most concentrations. Values ;it 10% tluctuüted more than from the orher

concentrations. For surface area meüsurements. values fluctuated from 10 to 30% (Figure

4.14). From 40 to 60%, the surface üreü of the slices decreaseci as time of osmotic

treatment increased. There was not much difference in surface area values between these

concentrations. Thickness measurements (Figure 4.15) did not show ;iny signil'icmt ticiid

as time and concentration of treatment progressed.

The volumes of the slices were determined via two methods. One method wris

taken from the density measurements using the pycnometer (Appendix CL The other

meihod was taken by multiplying the surface ÿrea and thicknrss values. Thesc two

meihods were compared to determine which one is more üccuratr. The grriph in Fi yiirc

4.16 shows the volume of an apple slice taken from surface areü rind thickncss

measurements plotted against time. The volume measurements from pycnometry were

plotted against time and shown in Figure 4.17. It can be observed thüt at O time, thcre is

more dispersion of data in Figure 4.16 compared to Figure 4.17. This is an indication of

the variability due to the inconsistency and lack of precision in the method. To determine

Figure 4.1 1 Experimenial and calculated water loss d u e s nt different sucrose concentrations

Figure 4.12 Experimental and caiculated solids gain values at different sucrose concentrations

l 0.95 i l

O 20 40 60 80 100 120 140 160 180 200

Tirne (mintues) - --- -- - --- - - A -

O 10% 020% A30% X40% X50% 060% -- A - --

Figure 4.13 Density of apple ~lices during osmotic dehydration at different sucrose concentrations

Time (mintues)

Figure 4.14 Change of surface u e a of apple slices during osmotic dehydration nt different sucrose concentrations

Time (minutes)

Figure 4.15 Thickness of apple slices during osmotic dehydration at different suçiosr concentrations

Figure 4.16 Volume of an apple slice from area and thickness measurements at different sucrose concentrations

Figure 4.17 Volume of an apple slice measurrd with pycnometry iit different sucrose concentrations

the correlation between water loss and volume of the slice, the sets of data frorn the two

volume measurements were plotted against water loss (Figures 4.18 and 4. IL) ). From

these two plots. there is a higher correlation between volumes meüsured h-om

pycnometry (Figure 4.18) than frorn volumes calculated from area and thicknrss (Figure

4.19). These plots also show t h t as water loss increases the volume of the slice

dccretises.

Results of al1 these determinations are tabulated in Appendix F.

4.3.4. Development of a thermoàynamics approach for snalysis of equilibrium conditions in the tissue

Equilibrium is estüblished when the chemical potential of water inside the cçll (cl

rquals the chemical potential of the osmotic solution (son:

p: = A*'' (4.7)

This rquation is also related to the activity of water. ci,,. because as irs chemical

potential decreases. its activity also decreases (Nobel. 1983) hence:

.wf - '. - Wll p;" =A,,. = q,* - q,.

Also, at equilibrium. these equations are in turn related to the mole fraction. .r of the

solids where the mole fraction of the solids in the osmotic solution is equal to the mole

fractions of the soluble solids inside the cell. By cissuming that there is no loss of

membrane pemeability and that no solute has leaked out during osmotic treatment, then

the equilibrium conditions inside the ce11 will depend only on the initial composition of

the material. By further assuming that the soluble solids are contained within the cell only

0.35 r I I

O IO 20 30 40 50 60

Water Loss (%)

Figure 4.18 Volume of an apple slice from pycnometry vs. wüter loss values

O 10 20 30 40 50 60

Water Loss %

Figure 4.19 Volume of an apple slice from area and thickness rneasuremrnts vs. water loss values

(such that the extracellular spüce contains just air and water at the beginning). then only

the osmotic solution has entered the material. So. at equilibrium, the extracellulür spüce is

tilled with the osmotic solution (water and sucrose) and air. Hence. at equilibrium. the

composition of the liquid phase in the extracellular space is equal to the composition of

the osrnotic solution:

The relationships above are true for rhe 30% to 60% sucrose solutioiis. For the

10% and 10% sucrose solutions. the mole frxiion is lower thm rhe plrisniulyw iiidr.

fraction. For values of x, c 0.0 134.1, the mole fraction inside the cells ciin be estimated

from the extemal mole fraction of the soiids in the solution by rissuming a linear

relationship between mole fraction and turgor pressure,

and thus,

The other basic assumptions used for this approach to describe the equilibrium

conditions in the tissue are as follows:

1 . Extemül pressure is zero

2. Solutions with same solute mole fraction have the s m e wüter activity

3. Pressure inside cells is proportionai to mole fraction

Fresh tissue has P = 0.5 MPa. fully turgid P = 1.7 MPa (a range close to

the one presented by Linn and Pitt. 1986). The fresh tissue hiis ü mole

fraction xV, = 0.0131. The mole fraction for the fully tur~_iJ t ishi i t . ir i... =

0.0 124. This leads to a plasmolysis mole fraction .Y,, = 0.0 1344.

Cell membrane is semi-permeable. i.e. only water cm cross it. Therefore.

iill the sucrose gained is in the extracel Mar spcice.

In the fresh tissue. the amount of extracellular solution is nrgligiblr.

The tissue infiltrated with water is fully turgid.

The equation for water loss at any tirne. t is:

so the mass of water equals:

and the mus of soluble solids at time, O is:

and,

rn; = rn; = w , : ~ = W,yrn,

w here:

nt,, = initial mass

m, = final mass ( m a s at equilibrium)

m , = m a s of water

W,, = weight fraction of water

MW = molecular weight of water

m, = rnass of soluble soiids in the celi

W, = weight fraction of soluble solids in the ce11

n, = number of moles of soluble solids in the cell

n, = molecular weight of soluble solids in the cell

tn, = mnss of sugar

W, = weight fraction of sugar

M, = molecular weight of sugar (e.g. sucrose)

If the average molecular weight of the solutes in the cell is known. it is possible to

calculate for the weight fraction of soluble solids at ryuilibrium considerine th^

n, being the number of moles of solutes and so. the müss of wütrr in the ce11 is

then equal to:

O I - X m;, = * X

(1 8) M W

The value of M, used was 178 gramslmole (Mazzanti. 1000) and for M,. i s 18

grams/mole.

The mass of water et equilibrium is the total müss of wliter in thc rntr;icrllulu

spüce ( e x ) and inside the cell (c ) . In order to know the extent of shrinkage o~ i t l

dehydntion, it is necessüry to partition the water between these two compiirtrnents:

w rsc r n , = m,, +mW (4.19)

From the following two equations:

w,"" - - nt; e rc erc r-rc and rnhl = m,; +m, rn; + m r

(4.20 and 4.2 1 )

the amounts of water in the extracellular space and inside the cell cün be çülculüted from:

and the ümount of solids in the extrücellulür spüce is:

The rnolecular weight of sucrose is 342 grarns/rnole. Since the m u s of solids in

the extracellular space is related to solids gain (SG) as follows:

e.rc - "ir - mOSG..

T h e mass of water in the extracellular space cm ülso be calculatrd as:

and again. the müss of water in the ce11 is:

rn'. W = m; -rnVC

t - i 2 3 ) .

The partitioning of water between the cell and the extracellulür space is shown in

Figure 4.20. As expected, the mass of water inside the ce11 decreases as rquilibriurn is

achieved and concentration increases. The water in the extracellular space increases as

the cell shrinks away from wall thus making the extracellular space bigger. The average

93

% Osmotic Solution

O water in extracellular space . water in cells - - - .- ----A - -- - - . . . - - - - - . . - - .. - - -

Figure 4.20 Partition of water in the tissue at equilibrium between the extracellulür space and the cells

volume of the tissue decreases as dehydration increases so the increase in the volume of

the water in the extncellular space and the space itself goes down. The niole fraction

inside the cell can now be calculated,

The changes in the masses and volumes of the tissue components and structures

are in Table 4.7. The values of water loss and solids gain from the inverse polynornial

regression and from equilibrium thermodynamics when compared shows chat the latter

method gives lower values than the previous method.

The initial rnass of writer in the tissue is mi,.,, = rri,,W,,.,,. Theret'ore, the w m r th;it

is not in the cells is m,,,. = tn,,.,, - m,,.,. Where is this water then'? Part of it is still in the

extracellular space. and part of it went out of the tissue as wüter loss. Sincr the

extracellular solution must have the same composition of the osmotic solution. the

proportion between water and sugar in the extracellulür space is fixed for ciich osmotic

treatment. Therefore, the more sugar is in the extracellular space. the more watrr hüs to

be there. Since the total water that is not in the cells. i.e. m,t,... is fixed. the more sugx

there is in the extracellular space. the less water loss. More sugar gain impl itls less wükr

loss, and vice-versa.

The value of m,,,/ni,, is the maximum hypothetical water loss possible. in the rvetit

that only a negligible amount of extracellular space was left. This would ülso mran a

negligible solids gain. Since this is not the reality, the actual water left in the tissue will

be some fraction of this hypothetical maximum, and a determined ümount of sucrose will

Table 4.7 Computed equilibrium values at different sucrose concentrations

Concentration of osrnotic solution, BX~ 10 1 20 1 30 1 40 1 50 1 60

Molecular weight of soluble solids. Mss,@mol

Mole fraction of osrnotic solution. X , 1 0.0058 1 0.0 130 1 0.022 1 1 0.0339 1 0.0500 10.073:

178

Weight fraction of solution 1 O. 1 O00 1 0.2000 1 0.3000 1 0.4000 1 0.5000 (0.60~ Mriss of sample. mo. grams 1 0.575 1 0.579 1 0.572 1 0.540 1 0.569 1 0.560

- --

Weight fraction of water, W,,, 1 0.90 1 r 0.902 1 0.907 1 0.903 1 0.906 1 0.905 Weight fraction of soluble solids, 1 , ,,, 1

Initial wnter in the tissue, m,,, gram 1 0.5 180 1 0.5228 1 0.5 187 1 0.4877 1 0.5 1 52 10.5065

Water loss (WL), independent regression Solids gain (SG,), i ndependent regression

X,y, in equilibrium 1 0.0 1 28 1 0.0 1 34 1 0.022 1 1 0.0339 1 0.0500 10.073: Mass of soluble solids, initial. Msso,grams

0.0494 0.0470 0.0465 0.0468 0.0459 0.0447

Grams of water in cells 0.3845 0.350 0.2085 0.1 349 O.OH87 0.0572

0.218

0.053

Maximum hypothetical WL 1 0.232 1 1 0.2982 1 0.5422 1 0.6532 1 0.7205 10.803 1 1

0.210

0.089

Water not in the cells. grüms (either exc. or lost)

Fraction of water Iost 1 0.6095 1 0.5708 1 0.4999 1 0.4890 ( 0.563 1 (0.h-lh31

Extracellular water, grams 1 0.0521 10.0741 10.1551 1 0.1802 10.1865 ~ o . I . ~ w /

0.3 15

0.166

1 \

O. 1334

Solids Gain, 1 0.010 1 0.032 1 O. 1 16 1 0.223 1 0.328 1 0.426 1

0.38 1

0.267

O. 1727 0.3 102

Extracellular sucrose,grams

Water Loss,

~ o t a l water In the tissue (grarns) 1 0.4366 1 0.4242 1 0.3637 1 0.3 152 1 0.2748 10.2 1631

0.462

0.398

0.3527

0.0665

0.271

Error, W L

Error, SG,

/weight fraction of water in the exc 1 0.1 194 1 0.1748 10.4265 1 0.57 18 1 0.6789 10.73541

0.577

0.524

- -- -

0.0058

0.141

0.4369

O. 1202

0.319

0.0185

0.170

0.0000

0.013

0.4497

- -

Weight fraction of water in the ce11

O. 1865

0.423

0.0001

0.0022

0.2386

0.519

0.8806

0.0002

0.WL 1

0.8252

0.0002

0.0005

0.5735

0.0000

0.0000

0.0002

0.W 1

0.1282 0.32 1 1 0.2646

be linked to that water. In al1 cases, the value of this maximum rn,,,/m,, wris ribove the

W L detemined from the independent regression.

4.3.5. Effects of solution and material resistances on mass transfer coefficients and fluxes

It w s previously discussed that the viscosity of the highly concrntr;iteti .;iiliition\

could affect the initiai rates of m a s transfer. Other external resistances in the solution are

diffusivity of the solute and the density of the solution. To determine the effects of these

factors, the values of the Reynolds number (Re). the Schmidt (Sc) nurnber and the

Shenvood number (Sh) were dso cornputed for each of the concentration of the sucrosr

solutions used. In order to correct for these factors and give ii better correlation betwcrn

concentration and the rates of osmotic dehydration in terms of the S and J pammerrrs.

mass transfer coefficients from the solution and the materinl were computed. The

development of these equations is detailed on Appendix H.

In Table 4.8, the values for the above numbers are summ;irised for e x h

concentration in mass ternis. The relationship of density is directly proportioniil to

concentration as shown in Figure 4.21. The sarne is true for the logarithm of the viscosity

plotted against the molality of the solution (Figure 4.22) as studied by Bohoun et. al

(1997). This plot also shows the big increase in viscosity from the 50% to the 604

sucrose solution. The diffusivity of the sucrose solution decreases as its concentration is

increased (Gekas and Mavroudis. 1998 and Bohoun et al.. 1997'). lis expected. Thiil c m

be seen in Figure 4.23. The mass innsfer coefficient. k, ', has the same relationship w ith

the sucrose concentration as the diffusivity (Figure 4.24).

97

Table 4.8 Effects of solution and material resistances on mass transkr

Molarity= 0.489 kmollm3

Pressure= 1700 kPa I

Molarity t=0= iipproximately O 1

L= 0.02 rn Diarneter of the slices

rn/s Superficial velocity owr the ' v= O. 1 SI ices I

Concentration

(%) w/w

Concentration

(9%) W/W

10

20

30

40

50

60

Density

(kg/m3)

Mass C

(kg/m3)

104

2 15

337

469

613

769

Viscosity

(mPa*s)

Sc

2.OE+03

3.7E+03

7.3E+03

1.8E+04

5.8E+04

3.1E+05

Diffusivity

( m2/s )

Molürity

(krnollm') 1

ReL

1 990

1 426

932

528

240

76

Sh

3,64E+02

3.76E+02

3.83E+02

3.85E+02

3,84E+02

3.75E+02

kc'

(m/s )

9.1 E-06

7.2E-06

5.6E-06

4.1 E-06

2.8E-06

1.6E-06

O 200 400 600 800 1 000

Mass concentration, k g h 3

Figure 4.21 Density as a function of sucrose concentration

moles sucroselkg w a t e r

Figure 4.22 Log of viscosity as a function of molality of suçrosr soluiioii

O 200 400 600 800 1 000

Mass concentration, kglm3

Figure 4.23 Diffusivity as a fuunction of sucrose concentration

O 200 400 600 800 1 000

Mass concentration, kglm3

Figure 4.24 Mass transfer coefficient (k, ') of different sucrose concentrations

The first point measured at 15 minutes from the 60% treatment wiis lower than

that measured from 50%. This has affected the calculations of the J v;iiuts, beci iux of ttîe

much lower y-intercept value. The J values for water and solids are thus. highrr ot 50%

than üt 60%. The speed of agitation applied for all solutions wüs constant so. at highrr

concentrations, the rate of agitation was slower than at lower concentrations because of

increasine + viscosity. The high viscosity of the solution hÿs created a laver of solution at

the surface of the slices which explains the higher masses obtained for this pürticuliir tirne

of sampling. This is especially tme for solids gain. This has in turn made the slope of the

r/WL line steeper and thus. the estimated WL, and SG, was much higher. although srill

lower than the cstimated values at 50% (brcausr the rest of the points from the other

sümpling times were ülright).

However. the values of J, and J , derived h m the previous çhüptrr (equlitions

3.18 and 3.19) could be used to describe the mas trünsfer phenomenon ;it civrry sucrose

concentration. By convening the rnass units to molar units. the values of the correction

factor. 0 cülculated from R (flux ratio) were also converted to molar units. The value of

8 decreases as concentration increases (Figure 4.25). These were likewise used to

compute the corrected mass transfer coefficient, kmr. The coefficient. K, is the overall

diffusive m a s transfer coefficient between the solution and the extracellular space of the

tissue. It is composed of two more mass transfer coefficients: km., which is the corrected

mass transfer coefficient for the boundary layer of the solution. and knr which is the mass

transfer coefficient for the extracellular space of the tissue. Thrse K.,. k,k, and ka , wrre

rnultiplied by mole fractions as driving forces to give the fluxes in kmolelm's. In Fisure

4.26, the plot of k, and kz (corrected) for water flux are plotted against concentrition.

1 O3

Concentration, % (w/w)

Figure 4.25 Theta values of different sucrose concentrations

O kx dot

O kx

O --

O 10 20 30 40 50 60 70

Concentration, %

Figure 4.26 Mass transfer coefficients. k, and ker of different sucrose concentrations

This plot shows that there is a smdl difference between these two coefticients. whiçh

means that the effect of the bulk flow on the mass transfer coefficient of the solution is

minimal. The difference in the two values increases as concentration increases. which

is crue because the bulk tlow increases with concentration.

The only way that: I l

K, k ; b l

is that:

This makes sense. since we only know the fluxes nt the interthce (.r,j. Now. since the

mas transfer of sugar happens only in the extracellular space. the actual driving force is

the one between the solution and the pure water in the extracellular spüce. not x,.,,,?

Therefore:

In Table 4.9, the results for the calculations of the equations ribovc are presrnted.

B y plotting these different mass transfer coefficients against sucrose concentration, the

mass transfer behaviour of apple samples at the six concentritions studied cm be

obsewed. In Figure 4.27, the plot of Kx vs. concentration is shown. There is an increase

of this coefficient from 10 to 50% but ai 606, there is an apprirent decline. In Figure

4.28, kM vs. concentration was plotted. Since both kM values from the two methods of

Table 4.9 Mass transfer coefficients and behaviour of apple tissues at different sucrose concentr;itions

MASS 1 MOLAR

iteration

1 % 1 kmolelrn2s ( / kmolelrn's / krnole/rn2s i ki ,k, x, 1 1

Concentration, %

Figure 4.27 Overall mus tnnsfer coefficient. K r üt different sucrosr concentrations

0.00E+00 - - - - - - - - - -

O 10 20 30 40 50 60 70

Concentration, %

Figure 4.28 M a s trünsfer coefficient of the material. ku at different sucrose concentrat ions

computations are equal, the plots are aiso equal. This plot is very similar to Figure 4.26

(K, vs. concentration) in behaviour. First, there was an increasing trend from IO-50%. ;tricl

then a steep drop at 60% to r value even lower to that of the kM at JOi&. This indicates

that the effect of the material on mass transfer decreases üt higher concentrations due to

the effects of the resistances from the solution. However. the viscosity of the solution had

increüsed by a factor of 10 from 50% to 60% and the rate of agitation wüs not high

enough thus. the decrease in the kLw values.

The value of the bulk tlow ( J , + 1,) were also cornputrd for r x h concentration.

The J , value is negative because the equation was forrnulated based from the solution's

point of virw and so. there is loss of solids and a pain in wiiter from the iiiatmal irito itie

solution (Bird. 1956). In Figure 4.29, the bulk tlow value WLS plotted iit every

concentration while in Figure 4.30. bulk tlow wüs plotted ügainst the mole fraction of the

solution. The bulk Flow at 30 and 40% is iilmost the same with 40% rvrn going just

below the 30%. Then. it goes up ai 50% and goes bück down agüin at 60%. This is of

course the sarne behaviour as the initial flux of writer (J,J earlier described.

The most important relationship of the bulk flow and the mass transfer coefficient

cm be used not only to characterise the behaviour of apple tissues at different sucrose

concentrations but also for classifying the different materials analysed in Chapter 3. This

was done by calculating for the ratio, 4 which is the ratio of the bulk flow transport to

diffusion transport defined as follows (Bird. 1956):

O 10 20 30 40 50 60 70

Concentration, %

Figure 4.29 Bulk flow vs. sucrose concentration

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

Mole Fraction of Solution, x,

Figure 4.30 Bulk flow vs. mole frxtion of solution

It should be noted, again, that Jw and J< were converted from mass to molar values by

dividing them with the molecular weights of water and solids. For water. it is 18 g/mole

and for solids, the molecular weight of sucrose was used which is 342 grams/mole. This

is the sarne as the unit of k, since # is a dimensionless number.

To describe the actual rnass transfer phenornenon at the interface üt time=O. the

value of Q wüs corrected as:

The mole fraction at the interface. .r, was calculatrd using recursive iteration with

the corrected rnass transfer coefficient. k*,.

The values of eC. are ûIso tabulated in Table 4.9. These values were plottrd againsr

sucrose concentration (Figure 4.3 1 ) and from this. the mass trnnsfer pheiioiiiciin coii Id hc

described. This ratio describes the bulk tlow (water loss) trünspon ovrr the diffiisîon

(solids gain) for every material at the initial time. If this ratio is greater rhün 1 . t h m is

more dehydration thün impregnation of solids. The higher this value is theii. ilic t awr tlic

rate of dehydration is. If this ratio is less than 1 , there 1s more iinpregnation than

dehydration because diffusion dominates over the bulk tlow. Al1 the a for ;dl

concentrations is less than 1. This means that for this kind of appies. the rate of

impregnation is dways higher than the rate dehydration. There is no linear correlation

between concentration with this ratio. This means that higher concentrations do not

always mean a more effective dehydntion.

The value of &. indicates what concentrations should be used on a material

depending on the final attribute that is desired. For example. if the final product is a

113

Figure 4.3 1 Mass tnnsfer behaviour of üpple at differeni sucrose concentrations

dehydrated candied fruit, the airn of osmotic dehydration would be the infusion of sugar

so a concentration that effects impregnation more than dehydration shoiild hs diosen. l t

the aim of the process is only to decrease the moisture content. ü concentration that

fiwours bulk flow over the diffusion of solids should be used. Hence. this kind of anal ysis

is applicable to any miiterial and is very useful for determining procrssing parameters.

In summary. this rnethod has enabled the charicterisation of the mass transfer

behaviour using the initial fiuxes of both the water and the solids. This rnethod w u also

based on the tissue's behaviour by using the rates of mass transfer (.Si ~ind S?) and the

values üt equilibrium (WL, and SG,). as well as the initial conditions of the tissue

(density. mass and surface area of m a s transfer). Al1 these parimeters are rasily

meÿsurable using the revised inverse polynomial model.

This method has also made it possible to combine the mass transfer behüviour of

both water and solids which is true, since this is a counter-current mass transfer

behaviour and the behaviour of the solids also affect the behaviour of the wiiter and vice

versa. By calculating for the niio of bulk flow over diffusion. i t is possible to predict rhr

extent of osmotic dehydration and thus. characterise the mass trans fer brhiiviour of plant

tissues at any concentration.

4.3.6. Classification of mass transfer behaviour of plant materials besed on ratio of bulk flow transport to diffusion transport

The efforts to characterise mass transfer behaviour of üpple tissue at different

sucrose concentrations have shown that the previous methods of calculating J , and J ,

values from the inverse polynomial tïtting were very useful. By using thrse fluxes to

calculate for #'.. the mass transfer behaviour was successfully drscribed ot any

concentration frum 1 0-60%.

This method was dso used to classify the different materials described in Chapter 3. The

J,,., J , and #c values of the apples studied here were also included in the classification. The

value of k., at 60% sucrose concentriition was assumed for al1 the rnaterials, This is a

rough estimate since not 211 materials were treiited with rxactly 60% sucrose solution. It

w u previously mentioned that the osrnotic solutions have concentrations of about 60-70

Brix and some were not pure sucrose. For preliminary classification and for the purpose

of demonstrating the method. a k., value of 4.9 X 10" kmole/rn2s and for 6010 sucrose

concentration üt ü typical mixing velocity (O. I m/s) wüs assumed for d l the rnatericils.

This concentration has a mole fmction x, of 0.0732. The mole t'rxtion ;it the interface. .r,

was calculiited using recursive iteration with the corrected m u s transfrr coefficient. k*, .

The values for these cziiculations are shown in Table 4.10.

In Figure 4.32. the A. values were plotted as column bars for each material. The y-

axis was then divided inio three to classify the seven materials. The same way of

classification was followed as in Chapter 3. Materials with @.> 1 are in Clüss I ("fast").

Those with 4),. < I are in Class III ("slow"). Those with 4 values üround I tvvrre classifiecl

üs having "average" rate of dehydntion and are in Clüss II.

As with the behaviour of the Jw and Js and also S?, Peach still exhibits a "fast" rare

of mass transfer. However. Cherry has a faster rate of dehydration than Peach. Apple "a"

followed in Class II with the two Apricots. They have an "average" rate of dehydrition.

Both Apple x and Apple b are at the end of the graph in Clüss III and cliissitïed as "slow".

Table 4.10 Mass transfer coefficients and behaviour of different materials at 60% sucrose concentration

Material ( MASS 1 MOLAR

Recursive iteration

Peach

Cherry

Apricot a

Apricot b

Apple a

Apple b

Apple x

4.9E-05

4.9E-05

4.9E-05

4.98-05

4.9E-05

4.9E-05

4.9E-05

1 .O6

0.73

0.07

0.09

0.95

O. 13

0.37

5.2E-O5

3.6E-05

3.5E-06

4.2E-06

4.6E-05

6.3E-06

1.8E-05

5.4 1

5.98

1 .L 1

0.98

i .35

0.4 1

0.5 t

@<. > 1 Clriss I ( h s t

qic = 1 Clliss 11 (average)

#{. < I Clriss III i slow J

Figure 4.32 Mass transfer behaviour of various plant materials at 60% sucrose solution

This value accounts for ail the rnass transfer resistances in the materinl anci the

solution on the fluxes of water and solids and thus, gives an over-al1 description of the

tissue's behaviour.

As with the behaviour of the Jw and Js and also S2, Peach still exhibits ii "t-iist ' i.:iiC

of rnass transfer. However, Cherry has a faster rate of dehydration than Peach. Apple "ri"

followed in Class II with the two Apricots. They have an "average" rate of dehydration.

Both Apple x and Apple b are at the end of the graph in Clas III and classificd as "slow".

It is clear from Figure 4.31 that there are differences in mass transfer beliaviour

between materials, as well ns between different varieties of the same materials (such ris

the apples). From a macroscopic point of view, this method is very useful for predicting

behaviour for the purpose of developing processing designs and parameters according to

the desired quality of the end product (such as if it should be dehydrated or impregnated).

4.4. Summary

This phase of the study has provided information about the behaviour of apple

tissue üt a "slice" level during osmotic dehydration. In tenns of water loss and solids

gain, this behaviour has been related to the concentration of the osmotic solution. The

tïtting of the inverse polynomial model ( t N L or t/SG) to the kinetics data was able to

give good estimated values for data at equilibnum as well as o description for the rates

and fluxes of mass transfer from 1040% sucrose concentrations. The estimation of the

initial flux of water or sugar tnnsfer based on the initiai panmeten such as mus. area,

volume and density of the slices allows for the estimation of the equilibrium values.

However, the values at higher concentrations, 50 and 60% did not follow the expected

trend.

The methods used to deterrnine WL and especially SG were very good for this

type of studies. This study has also shown that the other methods developed or iipplied

here for other determinations (such as insoluble solids. surface area. thickness and density

measurements) have to be improved to get more precise and accurate information.

The methods that were developed have also enabled the classification of the mass

transfer behaviour using the initial fluxes of both the water and the solids. This method

was based on the tissue's behaviour by using the rates of mass transfer (Si and S2) and the

values at equilibrium (WL, and SG,). The vwiability of the material has also been

accounted for by the initial conditions of the tissue (density. rnüss and surface a r a of

mus trünsfer). Al1 these parameters are easily measurüble using the revised inverse

polynomial fitting. This method has also made it possible to combine the m u s trünsfer

behaviour of both water and solids, since this is a counter-current mass transfer behaviour

and the behaviour of the solids also affect the behaviour of the water and vice versa.

The method that was developed for characterising müss transfer behaviour of

ripple tissues in varying concentrations of osmotic solutions is also applicable for

classifying behaviour of different materiais at one concentration. Therefore. this meihod

cün be used both ways. Thus. the behaviour of different materials üt different

concentrations can be described and predicted.

In terms of describing the actual phenornenon at the cellular level. however. rhrrr

is still a need to observe and measure these changes that will allow a more iiccurüte

method of characterising mass transfer behaviour. These studies should be done as close

as possible to the actual events that occur during osrnotic drhydration. As (i first strp to

redise this, methods that allow the observation and measurement of mass transkr

behaviour of plant tissues at the cellular Ievel were developed. These are the next

objectives of this research and will be discussed in the following chapters.

5. DEVELOPMENT OF mTHODS TO OBSERVE AND MEASURE PLANT TISSUES DURING OSMOTIC DEHYDRATION

S. 1. Introduction

Plant tissues. when placed in hypertonie solutions, undergo plasmolysis that cün

be observed directly under light microscopy. The usual phenornenon obsrrved during

plasmolysis is the separation of the cytoplasm from the cell wall. due to the removal of

water from the protoplast (Frey-Wyssiing iind Muhlethaler. 1965). Although rradily

observed undrr the microscope. however. the quantitative changes that occur during rhr

process are nor easily rneasurable. Light microscopy only works wsll on vrry thiii

sections of tissues. The actual physical cutting of the material causes disruption on the

tissues right away thus, lirniting the area available for observation and more imponantly.

for measurement. Using Scünning Electron Microscopy (SEM). For example. would

require materials to be frozen and embedded. These processes agüin. will drstroy the

tissue. Therefore, a method that allows the observation of thick sections of fresh or

"minimal1 y processed" tissue is necessary.

When the laser scanning confocal microscope (LSCM) was first introduced. i t

generited much excitement and enthusiasm among biologists. LSCM is a form of light

microscopy in which white or a narrow range of wüvelengths of laser light excites ü

specific tluorescent material (Vodovitz et al., 1996). It is usually used in the tluorescrncr

mode for imaging biological objects of various types, but cün also br used in the

brightfield reflection mode for imaging of objects of di fferent forms (S heppürd. 1 994).

The microscope can slice very clean, thin optical sections out of thick fluorescent

specimens (houe, 1990). It also has the capability to obtain 3-D images of biological and

other microscopic structures (Sheppard, 1994: Samarabandu et al., 199 1 ) and offers the

122

tremendous advantage over the fluorescence microscope by discriminating out-of-focus

background fluorescence (Wells et al. 1990). The microscope works very well with k s h

tissues as well as with embedded samples.

The stacks of samples taken at differenr depths is known as a z-series and can be

reconstructed to produce a 3-D image or object of the original structure ~hüt can be

rotated or sliced in any direction (Vodovitz. et al.. 1996).

The LSCM systern is a powerful tool in biological research such üs microstnicture

characterisation, morphogenesis, ceIl differentiation, tissue organisation and embryo

development. It has also been useful in other applications in material sciences and in food

research (Samürabandu et al., 199 1 ; Vodovitz et al., 1996).

This phase of the study was conducted to develop a method thüt will allow both

the observation and the meuurement of plant tissue sümples during osmotic drhydrstion.

The specific objectives of this study are: First. to be able to utilise the üdvmtagrs of the

laser scanning confocal microscope system in acquiring images of different plant tissues:

second is to develop a method of treating the tissues by using fluorescent dyes und the

osmotic solution that will show the changes in the cells during the treatment: and Iastly.

to develop an image analysis sysiem to quüntify these changes in the two main

components of the cell (the cell wall and the protoplüst) üs related to the aniouni ~ind r:itt.

of water loss using image analysis. The protoplast is contnined within the crll wül l and

made up of different structures and regions like the cytoplasrn, nucleus and othsi

inclusions. The cytoplasm makes up the main mass of the protoplast and since i t is the

only structure thüt an available fluorescent stain (that works for the purposrs of this

study) highlights as a test for membrane viability, this was assumed to be the same area

(or volume) as the protoplast.

The advantrge of LSCM in this kind of study is its ability to reconstruct a 3-D

result thus allowing a more in-depth study of the changes that occur in the cells.

5.2. Materials and Methods

5.2.1. Dye selecüon and development of protocol for acquisition of images

For the following experiments. the LSCM systm usrd wüs ii BIO-RAD MRC-

600 that uses a Krypton-Argon Mixed Gas Laser and has three rmission pctlikh a1 488 nm

(blue). 568 nm (green). and 617 nm (red). It has a Nikon Optiphoi-2 t'liioreiceni

microscope with objectives of IOX and 60X rnagnificütions. All images were captured by

the computer using 10X objective and photomultplier (PMT) detector I (green) or 2

(red). The computer also took the serial optical slices a[ different intervüls (as specified

below) and using a Kalman 3 filter with enhüncements done through the softwiire.

5.2.1.1. Preiiminary test on onion rpiclennis

As a prelirninary test to detemine which dyes could be usrd. trials were

conducted on different müteriüls. Onion was chosen because it hüs a very rhin Iayrr of

epiderrnis and thus. easier to section.

The onion (White) was cut vertically into big slices. A 1-cm diameter cork borer

was used to measure and cut the same area of the tissue. The thin epidermd layer wüs

carefully lifted out with a pair of forceps and mounted on a glass slide with a few drops

of water (to precondition the tissue) and cover slip. The slide was then placed under the

microscope. Excess water was taken out with a piece of filter paper and one to two drops

of dye solution of Fluorescein Diacetate (FDA) prepared from a stock solution ol' I O iiiM

DMSO and then diluted to 1 :200 with distilled water was then added. Aî'ter 2 minutes.

the dye was taken out with filter paper while sucrose solution (30 and 60%) wüs dropped

on to the tissue to cover it. The tissue was then cover slipped-mounted with the same

sucrose solution and plnced under the microscope 5 minutes later. Images were cüptured

using a z-step of IO and 20 pm intervals.

5.2.1.2. Dyr seleetion und drvclopnirnt of protocol jbr cipplr. porriro ciritl pnir tissr tes

For apple (var. Granny Smith). potato (var. White). pear (var. Bosch). tissue

sections were taken as follows: two crosswise slices of 2 çms in hcight wr r r iakrti f r m

the fruit using a sharp knife and a d e r . A 1-cm diameter cork borer was usrd to takr 2 X

I cm cylindrical samples. Sections of about 500 pm were then taken usins a hand-held

microtome (designed and constructed iit the Tool Shop. School of Engineering. Li of

Guelph). The tissue sections were then placed inside a small. plastic covered container

with about 5-ml of FDA for 2-5 minutes. The tissue sections were then transferred to

another container with a 0, 10,20,30.40, 50 or 60% sucrose solution (of about 0.5-2 ml)

and left to equilibrate from 5- 10 minutes. Images were taken right after dye treatment (as

"fresh" sample) and after each sucrose treatment with 0% being a "blank" sample. Other

dyes used were methylene blue + azure b, methylene blue, neutral red. calcofluor and

acid fuchsin. One to two drops were placed on top of the tissue from 2-15 minutes and

then imnged and subsequently treiited with sucrose. Different combinations of dye

solutions. amount and time in the dye solution as well as time of sucrose treatments were

tried to get the best image of the cells and its ce11 walls and cytoplasm.

5.2.2. Development of methods to measure volumes of structures

5.2.2.1. Mcrnrrul methods

.4 nurnber of image pcessing and iinalysis programs were enplored to determine

the best way to extract the volumeiric information thüt can be taken from the images.

These include availiible softwares in the market such as Mochü and SigrnaScan Pro

(Jandel Scientitïc. CA. USA). The images wcre enhanced using different filtering and

rhresholding techniques. Boundary enhancement methods were drveloprd to iinprovr

edge detection for volume measurements.

Images from the BioRad microscope were süved using COMOS as .pic files. This

file format is speciiïc for the microscope and most commerciül programs are not capable

of opening it. However. a freeware software called Confocal Assistant is avüilüble and

with this. initial image processing was possible and the images could be saved latrr as

typicül bitmap (".bmpM) or ".jpg" images.

These images are cornposed of stacks of opticül slices as the microscope took

slices through the depth of the tissue. Each optical slice or a z-strp image could be

processed and analysed individuülly. The series of slices could also be projected in

different angles and directions to produce a three-dimensional imüge. A projectrd image

is called a z-projection. It is then possible to analyse the reconstmcted imüge for volume

anal ysis.

A method using SigmaScan Pro was designed to measure surface arra and

perimeter of the cells. This is essentially a manual method of tracing and filling riich cell.

The method involved the use of Confocal Assistant to open each image in the stück as

well iis to do a z-projection of this images. Going from these 2 formats of images allowed

a better and clearer picture for detecting the edges of the cell with the naked rye.

Different approaches were trird for analysis. One approüch was to select 1 cells ründornly

from the z-projection ("the k e l l manual method") and then going through the stack to

select the slice(s) thüt gives the best images of the chosen cells. The best image here

means that the edges are well defined enough for the eye to drtect during the tracing of

the edges. These slices are then convened to bitmap images and imported to SigmaScan.

With SigmaScan. the images are calibrrted in microns. The original size of the images

upon acquisition was 768 X 5 12 pixels. The conversion from pixel to micron whish was

tûken from the microscope was 1.OW pixel = 1.601 microns. Image sizr in microns is

then 1230 X 820.

Another approach wüs to trace and fil1 al1 cells that could possibly bcr procrssed.

These include processing cells on the 2-projected image as well as measuring from rüch

individual slice in the stack using either one or ail of the slices (as ü projcctrd i r n q c l .

The cells were ülso divided into "whole" cells and "partial" cells. Whole cells are thosr

that appeür whole in the image. Partid cells are those that have been cut off from the

image but are still physically whole from an X-Y dimension.

Upon filling, which could be done on individual cells or on the whole image after dl crlls

have been traced, measurements of area and perimeter were iiutomütically done hy the

software. The values for each cell is tabulated in ii data worksheei that could be exponrd

to Excel or converted and saved as text or in ASCII fom for statistical ünalysis using

SAS.

There were various problems encountered with the manual method of

meüsurements. The method was very tedious considering the nurnber of images that need

to be analysed. To address this. an automated method was developed in collaboration

with researchers from the Department of Cornputing and Information Sciences at the

University. As part of a course requirement in a course. a student of Dr. Sirfiin Kremer.

Damiaün Hübets. took on the project of developing an image iinalysis systrin for these

particular images. The projeci was designed to develop a program thüt should be able io

answer these needs: tïrst, the capücity of reading the .pic files origindly iicqiiii-cd fi-om

the microscope. Second, to enhance the quülity of the images. Third. i t sliouid bt: capable

of detecting the edges of the cells automaiically. Founh. to allow the three-dimensionül

reconstruction of the optical slices and ficilitate the automatic measurcnicnt ot' cc1 l

volumes.

Oiher requirement for the program was that it should br capable of workiiiy undei

the Windows environment. The purpose of this is to make the program applicable not

only for the needs of this research but for it to be useful in other studies that will require a

similar program.

5.3. Results and Discussion

5.3.1. Microscopy method

5.3.1.1. Orlion epidennis

Fresh onion epidermis treated with FDA is shown in Figure 5.1. FDA highlights

the cytoplasm and could be seen pressed against the ce11 wall. After treatment with 20%

sucrose solution, the cytoplüsm could be seen breaking i i w y from rhc ccll ivall ~ i h ~ i

darker outline around the cells (Figure 5.2). When the tissue was placrd in 60% sucrose

solution, the shrinkage was more apparent (Figure 5.3). These results show thüt FDA is ü

very good dye that could be used to show plasmolysis using the LSCM systciii.

FDA is a vital stain for plant tissues used to permeate ce11 membranes. Viable

cells immediately begin to display a bright-green fluorescence within elich çell (termed ris

fluorochromasiü). According to the concentration of FDA. the fluorescence increcises

linearly with time until saturation. Injured or dead cells will not exhibit fluorescence iind

those which become mechanically damaged rifter üccurnulriting fluorescence product

immediately releüse the dye (Kasten. 198 1) . This could be seen in Figure 5.2. tokrn riftrr

treatment in 20% sucrose solution. where the inviable cells appecired as n dürk

background against the fluorescent ce11 membranes. Rotmnn and Papemaster ( 1961. as

cited by Kasten, 198 l ) , were able to prove that cell membranes are permeable to FDA.

5.3.1.2. Apple. peur und potuto

The preliminary mn with these 3 materials showed that at least 2 dyes could be

used to image the ceIl wall and the cytoplasm separately. Treating the tissues with both a

ceIl wall and a cytoplasm dye (like methylene blue + azure B (MB) and FDA) together

Figure 5.1 Fresh onion epidennis treated with FDA

Figure 5.2 Onion epidemis stained with FDA after treatment in 20% sucrose solution

Figure 5.3 Onion epidermis stained with FDA after treatment in 60% sucrose solution

130

Figure 5.1

Figure 5.2

Figure 5.3

posed problems because the FDA fluoresced much brighter than the MB causing the cell

wall "invisible" because of its intensity. Calcofluor. another ce11 wall stain, wüs good on

its own as well as acid fuchsin and methylene blue. It was however, the mechylene blue +

azure B in 0.1% sodium bromate that gave a clearer image for the cell wiills of dl four

materiais. FDA worked very well for irnaging the cytoplasm of apple. rutabaga and par .

An image of pear tissue stained with FDA is shown in Figure 5.4. The cells are

intact and whole whereas in the tissue treated with 60% sucrosr (Figurc 5.5 1 tlic L C I I \

have started to disintegrüte.

For potato, methylene blue was also usrd to stain the cell wülls (Fipire 5.h and

Figure 5.7): however: a different dye for the cytoplasm was usrd. The presrncr of ihs

starch granules inside the protoplast was a problem because it tended io take in FDA and

hence. the image is mostly that of highly fluorescent stürch granules. After a few trials.

neutrd red, another viable stiiin proved to be a better stain in highlighting the cytopliism

without much interference from the starch granules (Figure 5.8 and Figure 5.9). However.

previous studies have indicated that neutral red cannot get through the ceIl membrane and

ended up getting stuck in the ceIl walls. These images therefore are more likely that of

ce11 walls chan that of the cytoplasm. Methylene blue and FDA ülso worked very well on

apple tissues. Images of which could be seen later in this discussion.

Figure 5.4 Fresh pear tissue stüined with FDA

Fi yre 5.5 Pear tissue stained with FDA after treatment in 60% sucrose solution

Figure 5.4

Figure 5.5

Figure 5.6 Cell walls of fresh potato tissue stained with Methylene Blue + AzureB

Figure 5.7 Cell walls of potato tissue stüined with Methylene Blue + AzureB niid ireatrd with 60% sucrose solution

Figure 5.7

136

Figure 5.8 Cell wdls of fresh potato tissue stained with Neutral Red

Figure 5.9 Cell walls of potato tissue stained with Neutral Red and trsatrd with bOk. sucrose solution

Figure 5.8

Figure 5.9

5.3.2. Image analysis system

5.3.2.1. Munual measlirement of'ureas

An example of a stack of optical slices taken from an apple tissue stained with

FDA and treated with 40% solution is shown in Figure 5.10 and Figure 5.1 1. These were

the first 7 slices (Figure 5.10) and the 4 slices taken from the middle of the stiick (Figure

5.1 1). There were 16 images taken in al1 and these could be seen in the montage in Figure

5.12. The change in the visibility and the resolution of the cell structure and the changes

in them could be seen as imaging progresses through the depth of the tissue. These

images were dl taken 10 pm apart from each other. When these images cire projrcted

together as one. one on top of the other following the same order üs the acquisition. i t

becomes the image in Figure 5.13. This is essentiall y a three-dimensional image on n 2-

dimensional format. In this image. the arrangement of the cells in this section of the

tissue. with respect to each other could be seen more cleuly. These image-processing

manipulations were done using Confocal Assistant. The stacking of the images were donc

using Macromedisi Freehand 8. This can also be done using CorelDrüw: the skewing of

the images to give a stacked appearance wüs just done for presentütion purposes.

The above z-projected image however posed problerns during image anülysis and

measurement. The stacking of the images caused cells to overlap with eüch other.

Although it was possible with Confocal Assistant to project the images with a 2-distance

of 1 to 10 pixels, then filling up the gaps with or without interpolation. the rcwltiiig

image was not clear enough for measurement and other analysis work.

Another program, Optimas 6.0 was also explored while SCION Image and

ImageTool 2.00 were tried for other image analysis work. With these programs. the

139

Figure 5.10 A stack of 7 optical slices of apple cells stained with F ü A and treated in 60% sucrose solution

Figure 5.10

141

Figure 5.1 1 A stack of 4 optical siices of apple cells stained with FDA and treüted in 60%

sucrose solution

142

Figure 5.12 A montage of 16 optical slices of üpple cells stained with FDA and rrrütrd in

60% sucrose solution

Figure 5.1 2

Figure 5.13 A z-projected image of the 16 optical slices from Figure 5.12

Figure 5.13

automation of the analysis did not seem to be very feasible because the images Iack

uniform greyscale and background variations in illumination. The stück of opticül

sections was difficult to process using these programs and eiich slice hiis to be süved

separately. Aiso. the projected image from Confocal Assistant cannot be rvaluated by

software that are commercially available. or at least with those that we have access to.

These programs were nnt really capable of three-dimensional reconstruction and voli~rne

measuremen t.

Nevertheless. the 2celI manual method of tracing and tïlling was applied to a

number of acquired images of apple tissues. This was done to get preliminüry resulis of

the changes in surface area and perimeter of the cells as well as to determine rffects of

the order of sucrose treatment. This will be discussed in more detail in the nex t section.

5.3.2.2. Automnted image en huncenients ïind volunie upproximution

An image processing system was specifically designed and developrd fo i the ccll

images ücquired for this reseürch. This system is capable of detecting ce11 edges. of

identifying and interpolüting missing boundaries in three-dimension and most

imponantly. it does three-dimensional reconstruction of the optical sections and could

calculate the volume of the cells. All these steps could be programmed to be

automatically perfonned through the stack of images.

This program which is currently called ".PIC Editor; as of this time. is only

capable of opening and saving files with extension .pic. It has common edge detection

and filter functions like Gaussian and Laplacian. It is capable of doing rnorphology

functions like thinning, erosion and dilation. As weli, it has thresholding capability using

edge-based, statistical or optimised thresholding. The most important functions of this

148

system in relation to the needs of this study are the following: it is able to üutoinatr inost

of the image processing and analysis functions through the series of images using the

built-in macro program. Volume values. although in pixels could be me:is~ired righi

away. A three-dimensional view of the measured cells is also availüble for better

observation.

.4 typical cell edge detection and volume approxiinatir>ii piuxdurr ih Jrrcribed

below:

1 . A stück of images is opened up in the program. The image should have

already been pre-processed in terms of brightness and contrüst using Confocal

Assistant (Figure 5.14). this pnrticular image is thnt of the cell wülls of npple

after treatment in distilled water). To speed up the procrss. ii macro for image

processing could be progrÿmmed and saved under the Macro editor progrüm

included in the software. Using this macro. the procedures could be done

eiiher on one image or through al1 the images in the stack. Processing

functions include image smoothing (Figure 5.1 5 ) , thresholding by edge base

detection (Figure 5.16) and thinning of the boundaries (Figure 5.17).

2. After the Macro program has finished runiiing (i t will tüke a few seconds for

the whole process to be executed through the stack), a ceIl could be manually

selected by clicking it with the mouse pointer within any part of its rdge

(Figure 5.18). This will label the cell with a number (e.g. "cell i n ) . A "fit"

menu will then pop out of the screen by right clicking within the crll. By

Figure 5.14 Pre-processed image of apple cell walls treated in distilled wliter.

Figure 5.1 5 Srnoothed image of the same image frorn Figure 5.1 4

Figure 5.14

Figure 5.1 5

Figure 5.1 6 Smoothed image using thresholding by edge-base detection

Figure 5.17 Smoothed image with thinned boundaries

Figure 5.16

Figure 5.17

Figure 5.18 Fitting the "spider" üt the centre of a cr l l

Figure 5.19 A cell with its edges detected by the spider (arrow)

Fipre 5.18

Figure 5.19 (arrow points to the centre of the spider)

3. clicking on the fit command. a developed algorithm called the "spider" will

explore the boundaries within the edge of the selected ceIl (Figure 5.19). This

procedure will be done on each selected and labelled ceIl through the stiick of

images (Figure 5.20). The spider will also detect missing boundaries or gaps

in the cell's edge büsed on surrounding points in üII dimensions ( X . Y and 2).

The spider then interpolates d l the points it has detected in the cell and uses

this to reconstruct a three-dimensional view of the cell. The reconstruction

could be viewed üs sarnple points (Figure 5.21) or as ri wire Rame (Figure

5.22) and could be moved and rotated in different axes and dimensions.

4. The volume is approximated from this reconstructed image by tessrllation.

The value is given in pixels for each cell and should be importrd to ri

spreadsheet like Excel and converted to microns (Figure 5.23).

Procedures 2 to 4 above could be done simultaneously on more thtin one ceIl. This

way. the analysis could really be done much Faster. The system howevrr. for now. works

rffectively only on the cell wall images. These images have inore wrll-defii~rd

boundaries than the cytoplasrn images where edges start to disappear ~ L Ï cells shrink

more.

Al1 the procedures developed above (from image iicquisi tion to iniugc miil y5 ia.

both manual and automated methods) were üpplied on o number of images of applr

tissues that have undergone osmotic treatments in different concentrations. This will he

discussed in the next section.

Figure 5.20 Multiple cells with spider-detectrd rdges (iirrows)

Figure 5.2 1 Three-dimensional reconstruction of spider-detected cells frorn Figure 10, in sample-points view

Figure 5.20

Figure 5.21

Figure 5.22 Wire-frame view of the three-dimensional reconstruction of the saine çrlls in Figure 5.2 1

Figure 5.23 Volume measurements resuits window on top of the three-dimensional reconstruction w indow

Figure 5.22

Figure 5.23

5.3.3. Application of developed methods on apple tissues

5.3.3. / Objectives

A cell undergoing osmotic treatment such as that done for this study iooks

cell wall

W b = o t i c (3 - solution

extrü-cellular protoplut space

Figure 5.24 A cell undergoing osmotic dehydrtition

like the tïgure above. After treatment with distilled water. the cell becornes turgid ns

water penetrites into it. After putting the tissue in an osmotic solution. the c r l l shrinks

and the protoplast pulls away from the ce11 wall (plasmolysis).

The most common methods of approximating volume changes in crlls is by

measuring the cell's length or breadth and by wuming that i t is proportionid to its

volume (Meyer, 1952). This assumption is not true for al1 cells. especiülly for

plasrnolysed cells where shrinkage c m occur in any direction (concave. convex or

irregular plasmolysis).

The methods that were developed in the previous sections of this chapter üilow

the measurement of actual volumes from the stack of images that were taken kom the

LSCM system. Since the tissue was preconditioned in distilled water More the

treatments. we c m assume that at the beginning, the volume enclosed by the ceIl walls is

rqual to the total volume of the celis. The movement of water out of the ceil c ~ i i i w h LI

161

reduction in the pressure exerted by the cell sap against to cell wall and the protoplast.

The elasticity of the ce11 wall has a lower limit and when this is reached. it stops

shrinking. However, even the ce11 wall has stopped shnnking, the protoplast continues to

lose water since it is still in contact with the osmotic solution (Meyer. 1952). Putting the

solution back in water will cause it to gain water and recover its turgid state

(deplasmol ysis).

The experiments for this phase were designed to follow the changes in the

volumes of apple cells. The apple tissues were treüted in vürying orders of suçrose

concentrations. The changes in the volumes of the cells (total volume üs well as

protoplast volume) can then be approximated in ihree-dimensions. This gives

description of how the tissue behaves as a function of concentrition.

These experirnents were aimed. fint and foremost. at verifying the rnethods that

were developed to observe and quantify plant tissue behüviour during mrnotic

dehydration. Although care was taken in the design and implementation of the

experiments. this was still the initial complete application thiit was done and hencr. rhe

results were not accurate as expected.

A preliminary manual measurement method was also applied on the ncquircd

images. This was done to determine the effects of the concentration and the ordcr of

treatment on the surface area and the perimeter of the cells. These measurements were

also done io get an initial feedback on the design of the experiments. the limitations of

the method, and what aspects needed to be improved.

5.3.3.2 Image acquisition

After preliminary testing, a method was developed for the actual experiment to

yield the best possible images of the two cellulür structures. It wüs performrd as follows:

Two 500 pm-thick apple (Grünny Smith) tissue sections were tüken consecutivrly frorn

the same cylinder (Figure 5.25). The tissues were th rn prrconditioned in Jistil ld wiiicr

for 5 minutes. One section was randomly assigned for ceil wall andysis (Iübeilrd as

section "A") and the other for cytoplasm analysis (labelled ;is section "Bu). The first

section w u placed on a g l a s slide and 1-2 drops of methyiene biue + üzure B wüs uard

to stain it for 15 minutes. The other section was placed inside a small. plastic. covrred

container with 2 rnL of FDA solution for 5 minutes. The section w;is then cr~ver-4ippr.d

mounted on a glass slide and images of the "fresh" tissue were taken witli the LSCM

system. The same thing was done for the MB-treated section. Each of the sections wns

then placed into a small container with 3 mL of osmotic solution (O ( d i d l e d wntrri. 1U.

10. 30. 40 or 50% sucrose solution) for 10 minutes. Each section wüs covrr-slipped

mounted after each treatment and placed under the microscope for irnüging. The sections

were transferred to the next sucrose treatment immediately üfter imaging. The process

continued until the sixth and final treatment was done.

The same LSCM system was used for this experiment. Optical slices were taken

and stored at every IO-um interval using Kalman-3 filter at PMT 1. The order of the

sucrose treatment was randomly assigned using the Latin Square method (Table 5.1 ).

This was ernployed to ascertain and account for any effects of the order of

treatment on the tissue.

Figure 5.25 Procedure for sample preparation for sample treatment with LSCM

(Illustration by Julie Jee)

Table S. 1 Order of sucrose treatment

Six replicates were done with eiich replicate smpling taken from one iipplr fruit.

This means thüt the two sections of apple tissues were taken from only one cylinder

randomiy seiected from the four cylinders tÿken from each applr. For rlich replicütc. ii

whole run of experiment was done so twelve sets of images were taken (6 for ce11 wdl. 6

for cytoplasm). A total of 72 siacks of images were collected from rüch whole run. The

schematic diagram for the procedure is in Figure 5.26.

Images of the cell walls of the sarne tissue section stained with methylene blue +

uure b (MB) are shown in Figures 5.27a to 5.17g. The order of sucrosr solutiori

treatrnent for this particular trial was: fresh, 50%, 40%. 0%. 30%. 20% and lastly. 10%.

The cell walls did not seem to have changed much after each of the treütments.

Ce11 walls appeared t9 shrink after 40% treatment but "recovered" ;ifter 0% treatrnent.

They appeared to shrink slightly again after that.

165

Order of

Treatme n t

1

2

Replicate

1

50%

40%

Replicate

2

40%

30%

Repliciite

5

10%

O

Replicate

3

O

50%

Replicate

6

O

50%

Replicate

4

30%

20%

Figure 5.26 Schematic diagram for image acquisition of apple tissues during osmotic dehydration (single run)

distillcd watcr 15 minutes)

Meihylene Blue ( 15 minutes)

FDA (5 minutes)

Tiike exçess stain out with tissue DaDer I I

Place glas slide under the microscope after staining. Acquire images (= 8 minutrslsection). Tmnsfer each slice, sepiirately to ri small, plastic

container with 1 .O ml sucrose solution t'or osmotic treatment. Leave for 10 minutes.

Treatment 1 LI-=l

Figure 5.16 (continued)

Remount on g la s siide with cover slip. PIacc under the microscope after staining. Açquire images (= 8

minutes/section)

Place under the microscope after stüining. Acquirc images (= 8 minuteslsection)

- .. - --

Continue the same procedure until the 1 s t treatment

(treatment 6)

Aprroximate Total Time for 1 Run = 2.16 hours

Figure 5.27 Images of apple tissue (same section for al1 treatments) stained with MB nfter each of the 7 treütments. a) fresh b) 50% c) 40% d) O e ) 30% 0 20% and g) 10%

Figure 5.27. Apple tissue (same section for al1 treatments) stained with MB after each of the 7 treatments. a)"freshw b) 50% c) 40% d) 0% e ) 30% f) 20% and g) 10%.

Images of the tissue section stained with FDA and treated with each of the sucrose

solutions are shown in Figures 5.28a to 5.28g. Shrinkage of the cells can be observed

after treatment at 50 and 40% sucrose solutions. Treating the tissue from a 50% and then

40% sucrose solution to a 0% solution proved to be very damaging to the cells. This cün

be observed in Figure 5.28d. where although the cells looked whole again. the number of

fluorescent !i.e. live) cells appeared to have decreased considerabl y. This i s hecoiise nt'

the high stress conditions it had to undergo. As the treatments were continued. it beclime

quite difficult for the protoplasm of the cell to "recover" from this stress as ciin be seen in

Figure 5.28e, f and g as the cell membrane starts losing its integrity.

The stacks of images consisted of optical slices directly acquired from the LSCM

system. The cell wall stacks contain about 7-15 optical slices while the cell images

contain about 12-20 optical slices. Image and boundary enhmcements werr done to

improve edge detection. This involved, for the most part. the use of Confocal Assistant

4.02. Enhancements in terms of brightness and contrast were done to improve rdge

detection and image quality.

The stacks of images consisted of optical slices directly acquired from the LSCM

system. The cell wall stacks contain about 7-15 optical slices whilr the cc11 iitiligeh

contain about 12-20 optical slices. image and boundary enhüncements were done to

improve edge detection. This involved, for the most part, the use of Confocal Assistant

4.02. Enhancements in ternis of brightness and contrast were done to irnprove rdge

detection and image quality.

Figure 5.28 Images of apple tissue (sarne section for al1 treatments) stüined w ith FDA after each of the 7 treatments. a) fresh b) 50% c) 40% d) O e) 30% f) 20% and g) 10%

Figure 5.28. Apple tissue (same section for al1 treatments) stained with FDA after each of the 7 treatments. a)"'fresh" b) 50% c) 40% d) 0% e) 30% f) 20% and g) 101.

172

Manual measurements with SigmaScan Pro 3.0 were perfoned on the images.

The method involved manual trxing of the edges after calibrating each stück of images.

The program was then able to fil1 the area within the edges. Surface area. perimeter and

major length axis. were automaticall y calculiited afterwards.

The stacks of images were first projected as one using Confocal Assistant 4.02

with the z-projection command. This image wris saved as Bitmap (".brnpH) and using

SigmaScan Pro 3. the "whole" (cells thüt üppeür whole on the image) were Iabelled

(Figure 5.29).

Two cells were then randomly selected Rom each stack. Through visual

inspection, the opticai slice that gave the best image of the two selected cells was usrd for

analysis. These optical slices were saved as Bitmap (".bmpV) images and exportrd to

SigmaScan Pro 3. Using this software. the images were calibrated ( 1 .O00 pixel = 1.60 1

pm). The selected cells were then traced using the trace tool (Figure 5.30). Trricr

meaurement options were set to mesure area and penmeter.

SigmaScan calculates the (cross-section) area of the cell by surnming up the

number of pixels on the traced area. Since the images were cdibrütrd to microns. the

measured areü was already reported in square microns (pm2). Perimeter wüs calciilated as

the sum of the verticai. horizontal and diagonal perimeters. The vertical perimeter is the

sum of the distance of al1 vertical edge pixels. The horizontal perimeter is ülso the surn of

the distance of dl horizontal edge pixels and similarly, the diagonal perimeter is the surn

of the distance of al1 diagonal edge pixels. The data worksheets wrre saved üs t a i t files

and imported to SAS (Statistical Andysis System) for statistical analysis.

Figure 5.29 A z-projected image with the labelled cells.

Figure 5.30 An optical slice with one cell. labelled and traced

Figure 5.29 A z-projecteà image with the labeled cells

Figure 5.30 An optical slice with one cell, Iabeld and traced

Due to the complexity of the experimental design, a special SAS program wüs

developed by Dr. William Matthes-Sem from the Ashton Stütistical Centre, Dept. of

Mathematics and Statistics. University of Guelph. The experimental design was üIso

developed with his collnboration. There were two methods of statistical analysis done.

One was by averaging the two cells and the other was done without averaging the two

cells.

In brief. the results of the analyses are: without nveraging the two cslls. results

showed that there was no concentration efkct for both ceIl w d l and cefl s~irt'rict. iirca.

There w u no order effect. Results also showed thüt there was no need to analyse more

than two cells.

By averaging the two cells. there wÿs an order rffect for the surface arro ut' ttir

cell wail and the cell. There was no concentration effect on perimrter for the d l : therr

was a slightly increasing linear effect in concentration for cell wall perimrtrr,

Other preliminary conclusions include that there was considerable vüriobility

between replicates. The experimental design was the: the number of replicates çould be

increased but this may not really affect the results rxcept increiising the tiurnber oi

degrees of freedom. Cell (cytoplasm) results are questionable because of the difficulty in

defining the üctual edges after plasmolysis. There were five missing data points for this

set.

Due to these unfavourable results, a review of the methods of acquisition and

treatment was done. The staining method was of particular interest in this regard. A

number of stains were again tried to get images that would show the cell wall. the cd1

(cytoplasm), and the free space at one time. Sorne general stains to highlight the whole

tissue were used ülone and in combination with other specific stains. These include

Acriflüvine. Acid fuchsin. Sodium FDA. Tryphan blue. Sulfurhodamine. Nile blue.

Sudan IV and Methylene blue + azure B.

Sodium FDA proved to be a better stain than FDA in Lems o f stiibility. The dyrh

produced almost exactly the same results under fluorescence. Another background stain.

safranin. was also tried. The combination of safranin and FDA gave an image showing

both the cell wall and cytoplasrn in üpple tissues. The images however wrrr no[ very

clear due to ü number of background noises and artefacts. Another difficulty iliui u ~ i h

brought to attention was that the images captured on the cornputer were black and white.

Under the microscope, the tissues could be observed in colour and hrnce. the structures

were more detïned. These aspects of irnprovements need to be studied further.

As mentioned in the previous chapter. the .PIC Editor program is only applicable

to cell wall images. at the present time. These were the only images that could be

analysed for volume measurements. However. at the time that the volume analysis hüs to

be done. errors on measurements occurred. After a review of the prograrn. it hüs bern

decided that a thorough check and re-writing of the algorithms be dons to müke the

. calculations work. This will entail a few more months of work and something that could

not be included in this research. anymore. Also. even if the software still nerds

improvement for it to be able to measure the cytoplasrn images, it is to be reiterated that

there is also a need to acquire better images.

5.4 Summary

The problems that were encountered during image analysis provided ideas on the

kind of images that should be acquired and how the method of acquisition could be

177

improved. At this point. there are improvements in tems of tissue preparütion. dye

selection and treatments. LSCM system set-up and method of acquisition that are still

necessary. Improvement in terms of the following aspects could be very usefuI: hrst. the

method should be able to image both the cell wall and the protoplast ai the same time. It

should aIso be able to observe the same cells before and during the process. To get a

better three-dimensional reconstruction. more opticai slices should be tüken. This mems

that other fluorescent stains should be tried or the method of staining is improved to get

derper penetration of the stains. More enhanced images. either in colour or black and

white. with better resolution must be acquired. Coloured images showing both the cell

walls and the protoplast have been acquired but due to time constraints. the method has

not been thoroughly developed for application on the same kind of studies.

Despite of these shortcomings, these developed methods were still able to

irnprove the kind of images acquired on osrnotically dehydrated plant m;itrri;ils. The

information they provide gives a new dimension to the phenornenon of osmotic

dehydration at a cellular level. in qualitative and latet on. in quantitative aspects.

As more and more research is being done in the area of osmotic dehydration. i t

has become more apparent ihat the process is a cornplex one. This complexity is brought

about by the nature of the plant materials where this process h u been widely applied. The

simple behaviour of combined ~ubstances like water and sugar tum cornplex whrn plücrd

in a complex environment such as thüt of living plant tissues. Unlike other food

processing techniques where the viability of the material is destroyed upon treatment. in

osmotic dehydration. the material rernains vital for most of the time. This is tnie until the

ce11 has reached its lirnit to survive (this usually happens only when the concentration of

the solution and the process temperature are too high and the cell membrane is disniptrd).

Compared to some food processing techniques (e.g. thermal processing. drying.

pickling). osrnotic dehydration is quite new: largely becüusi: it is jusi a pre-proccssing

technique and is more useful for industrial applications. On the other hiincl. ilir Imi 01'

osmosis have been established for decades before it was thought that the phenornenon

(plûsmolysis) it cuused plant tissues could be used as a processing method. So. again.

studies on osmotic dehydration have focused buck on this phenornenon of plasmoly>~b.

Whüt has been thought to be a simple occurrence has becorne even more of an enigmü

(Oparka, 1994) to plant scientists as new questions anse as to the exact events thrit occur

during plasmolysis (as well as during deplasmolysis).

This reseiuch hm tried to understand and even to lessen the complexity of the

process of osmotic dehydntion by developing methods that brhg u s closer to the tictual

events during the process. Some of the methods developed to facilitate this understanding

are fairly new. Some of them have been adapted from previous studies and some of them

are applications of the same tned and tested methods.

By focusing on easily measurable properties and parameters. it was possible to

characterise the mus transfer behaviour of apple tissue at varying sucrose concentrations.

This method of characterisation also made it possible to classify the beliaviour of

different plant tissues. This research has proven that describing the inüss tninslkr

behüviour from a mücroscopic approach w u possible and very usrful although i t still

showed the need for microscopic methods.

Advünces in the fields of microscopy and image linülysi~ hiivc pruvidcd bt~ i s i .

techniques in observing and measuring plant rissues during the proçrss. Tlir irnnging

methods are more advanced thnn previous methods used. The LSCM s y stem tleveli~prd

here was a non-invasive. non-destructive technique. which has retained the viabi l ity of

the sarnples.

Still. there is ü lot to be improved as previously discussed. The trsting «i ilie

developed methods on other plant materials for prediction of equilibrium and estimation

of the rates of mass transfer has to be done. The debugging of the image anülysis program

to facilitate the volume measurements has to be finished while better images are being

acquired.

Other improvements that can still be done using these new techniques are

limitless and have to be explored. These improvements include "real time" imaging of

plant tissues in 3-dimensions. The image acquisition system to be used in this kind of

images has to be redesigned, as well. to accommodate these developments. Lastly. there

is still that need to quantify the actual changes in the cells during the process and relate

these to the kinetics of mass transfer.

The new methods developed have opened doors that will provide more w : i y IO

understand the process of osmotic dehydmtion even funher. The methods büsed on

macroscopic approaches to describe the mass transfer phenomena inside the plant ce1 1s

will still need more refinement to be truly applicable to any material. This liiis dso l rd to

developing rnicroscopic techniques, as it has been the iiim in this üreii to develop

mathematical and conceptual models based on rnicroscopic descriptions. This aim proved

to be a great one and as much as this research has opened a big door for this. there is still

a lot of work that needs to be done. However, a number of tirst steps have been taken

towards the direction at which research on this area must continue.

In the rneanwhile. the challenge to understand osmotic dehydriition from nll

aspects. at al1 levels. remains and continues to food scientists. food rnginerrs. plant

scientists und as well, io imaging experts.

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S tadelmann, E.J. 1966. Evaluation of turgidity , plasmolysis and deplasmolysis in plant cells. In Methods in Cell Physiology. Academic Press. New York.

Thomley. I.H.M. 1976. Mathematicai models in Plant Physiology. A quantitative approach to problems in plant and crop physiology. Academic Press (Inc.). London

Toupin,C.J.; Marcotte. M and Le-Maguer.M. 1989. Osmotically-induced m a s transfer in plant stonge tissues: a mathematical model. Part 1. Joumül of Food Engineering. 10 (2): 13-38.

Vodovitz, Y., Vittadini. E.. Coupland, J.. McClements. D.J. and Chinachoti. P. 1996. Bridging the gap: use of confocal rnicroscopy in food research. Food Technology. 6: 74-8 1.

Wells. S. and Johnson. lain. 1994. Fluorescent labels for confocül microscopy. In Threr- Dimensional Confocal Microscopy: Volume Investigation of Biological Systrms. John K. Stevens. Linda R. Mills and Judy E. Trogadis. eds. Academic Press. San Diego.

Wells, K. Sam, Sandison, David R., Strickler, James and Webb. Watt W. 1990. Quantitative fluorescence irnaging with laser scanning confocal microscopy. ln Handbook of Biological Coniocai Microscopy. James B. Pawlry. rd. Plenum Press. NY.

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APPENDIX A.1. WATER LOSS AND SOLlDS GAIN DATA OF CHERRY

Data No. Material Initial State Shape Size Solute Solu te Composition Temperature Ratio (Fruit to Syrup) Reference

7 Cherry TS= 1 8.76 Whole Ave. diameter 1.8 cm 50% wlw Corn Syrup/Sucrose 70 Bx 25 C 1 : 5

Giangiacomo et al. 1987

TIME 1 WATER LOSS 1 SOLIDS GAIN 1 t/WL 1 t/SG 1

VWL, t/SG vs. Time

minutes

APPENDIX A.2. WATER LOSS AND SOLIDS GAIN DATA OF PEACH

Data No. Material Initiai State Shrpe Size Solute Solute Composition Temperature Ratio (Fruit to Syrup) Reference

9 Peach TS= 1 3.87 Halves I cm thick 50% wlw Corn Syrup/Sucrose 70 Bx 25 C I:S

Giüngiacomo et al. 1987

1 TIME 1 WATER LOSS 1 SOLIDS GAIN 1 UWL 1 USC 1

t/WL, t/SG vs. Time

minutes

APPENDIX A.3. WATER LOSS AND SOLIDS GAIN DATA OF APRICOT A

Data No. 8a Material Apricot Initial State TS= 12-88 Shape SIice Size 2 cm thick Solute Sucrose Solute Composition 70 Bx Temperature 25 C Ratio (Fruit to Syrup) I : 5 Reference Giangiacomo et al. 1987

W L , t/SC vs. Time

TIME minutes

SOLIDSGAIN (%)

WATERLOSS (%)

t/WL t/SG

APPENDIX A.4. WATER LOSS AND SOLIDS GAIN DATA OF APRICOT U

Data No. Material Initial State Shape Size Solute Solute Composition Temperature Ratio (Fruit to Syrup) Reference

8b Apricot TS= 14.86 S l ice 2 cm thick Fructose + glucose + sucrose 65.2 Bx 25 C

1 : 5 Giüngiacorno et al. 1987

W L , t/SG vs. Time

TIME minutes

I minutes

WATER LOSS (%)

SOLIDS CAIN (56)

t/WL t/SG

APPENDIX A S WATER LOSS AND SOLIDS GAIN DATA OF APPLE A

Data No. Material Initial S tate Shape Size Solute Solute Composition Temperature Ratio (Fruit to Syrup) Reference

minutes

Circular disks d=2cm, t=OScm Suc rose 70% ?OC

1 : 20 Azuara et al .. 1 996

WATER LOSS 1 SOLIDS GAIN 1 üWL 1 t/SC

W L , t/SG vs. Time Apple a

APPENDIX A.6. WATER LOSS AND SOLIDS GAIN DATA OF APPLE B

Data No. Material Initial State Shape Size

S 1 ice 3-4mm

Solute Sucrose Solute Composition 60% (w/w ) sucrose Temperature 23 C Ratio (Fruit to Syrup) 1 : 20 Reference Hawkes, I. and Flink, J.M. ( 1979)

1 TIME WATER LOSS 1 SOLIDS GAIN 1 VWL 1 t/SG 1

W L , t/SG vs. Tirne Apple b

minutes

30 L

60

minutes

(W

22 32

(%)

i 8.50 19.70

1.36 1.88

1.62 3 .O5

APPENDIX B

Protocol for the preparation of samples and sucrose content measurements using HPLC

1 .Wei& three slices

2. Homogenize three slices in 10 ml of water

3 . Filter Hornogenate

For Treated Samples

4. Add 180 pl of 80% Acetonitrile into insert

5. Add 20 pl of homogenate into insert

For Standards and Fresh Samples

6 . Add 150 pl of 80-20 ACN to insert

7. Add 50 pl of homogenate to insert

vs

vw

m .f

0.0006 17

ww

Wwet

Cl th

& AîN80

Volume of sucrose in homogenized sample

Volume of water in homogenized sample

mg of sucrose in homogenized sümple

mllmg specific volume of sucrose in solution

weight fraction of water in slices

weight of slices

pL of homogenate to insen

p.L of 80% ACN to insert

193

DR dilution ratio

SC sucrose concentration from HPLC, mg/ml

Solids Gain, SG. = Wbr [2]-

APPENDIX C

Procedure for Determination of Density

A custom-made pycnometer was used for the determination. This was made up of

a 50-ml Erlenmeyer Flask. a rubber stopper with a small hole at the centre and a glus

copillary fi t through the hole in stopper.

Procedure:

1 . Weigh the pycnometer on ü top-loading balance. This is the weight of the pycnomrter.

2. Place 3 apple slices in the flask and weigh again. This is the weight of the siirnplr and

pycnometer.

3. Add distilled water (kept ai 20 OC), weigh. Record the weight of the pycnomrter +

sample + water.

4. Empty the pycnometer of its contents. Place distilled water (at 20 OC) and wrigh. This

is the weight of the pycnometer + water.

Notes:

1. Müke sure that the pycnorneter is alwoys completely dry befortr etich determina

The outside must dso be dried thoroughly before each weighing.

2. Make sure that the stopper is placed exactly ai the sume level before each wrighing.

Caiculations:

1. Volume of pycnometer = (weight of pycnometer + sample + water) - weight of

pycnometer

2. Mass of sample = (weight of pycnometer + weight of sample) - weight of pycnometer

3. Mus of water = (weight of pycnorneter + water) - weight of pycnometer

4. Volume of water = m u s of water since density of water at 20 "C is = 1 .O00 g/ml

5. Volume of sample = volume of water - volume of pycnometer

6. Density of sample = mass of sample ...................... volume cf ~ample

APPENDIX D

Determination of Water Loss

1 . Detemination of Water Loss:

Wüter loss was calculated using data frorn the detemination of moisture content. The

procedure for the determination are as follows:

a. Biot samples after each treatrnent

b. Place 3 slices on previously weighed aluminurn dishes

c . Weigh üluminum dish + sample

d. Place üluminum dishes in a vacuum ovrn for at leiist 8 hourh ict 7 0 C aiid 37

mmHg pressure

r. Weigh aluminum dish + sample

2. Calculations:

Mass of ~ ~ m p l e ~ ~ ~ ~ , ~ ~ ~ ~ ~ ~ = (Weight of rluminum dish + sample) - (Weight of Samp Warm drymg

Mass of ample(,^^,^,^,,, ML = (Weight of aluminum dish + sample )- (Wright of Sÿmple)aftcr drymg

M u s of water = Weight of sampletaco, dvin,, - Weight of ample,,^,, de,.,,

Water Loss. WL. = W,: -

APPENDIX E

Results of Kinetics Study

Table 1 . Water Loss Values and Corresponding Standard Errors

Table 2. Solids Gain Values and Corresponding Standard Errors

(min) 15

30

45

60

120

/TM'/ %SG 1 1 / Stdb 1 1 1 %SG (min) Error %SG Error %SG Error

QWL

3.77

8.56

5-92

11.74

17.38

Std. Error 0.09

0.14

0.16

0.25

0.49

180 0.38 14.56

BWL

8.21

7.39

9.87

12.69

13.31

18.20

S td. Error 0.20

0.13

0.21

0.34

0.34

0.51

QWL

14.90

22.72

22.00

26.68

29.90

28.52

Std. 1 Error nWL

Stdo Error 0.44

0.20

0.49

0.67

0.75

0.46

0.43

0.56

0.69

0.70

0.67 0.77

19.01

23.37

25.69

28.65

37.51

33.43

Q W L I Std- 17 1 Std. Error nWL Error,

25.60

31.51

35.63

39.35

42.39

42.95

0.68

0.50

0.60

0.99

1.04

0.87

19.97

31.19

41.17

41.38

48.49

50.49

0.73

0.88

2.113

1.79

1.97

1.851 1

APPENDIX F

Application of the Inverse Polynomial Mode1

Results of Time/Water Loss Analysis

Resulis of TirneNater Loss Analysis

60 %

0.75

Time (minutes)

15

30

45

60

1 20

180

dope

b

r2

3 .50

7.60

5.1 1

6.90

12.37

0.046

3.14

0.786

10%

3.98

40%

0.79

4.06

4.56

4.73

9.0 1

9.89

0.048

2.10

0.928

1

50%

1.17

1-90

1.87

2.49

4.06

5.67

0.026

0.86

0.992

40%

1 2 4

1.97

2 4 1

3 .O4

5.63

7.65

0.038

0.72

0.998

50%

0.59

20%

1.83

60%

1.34

1.59

1.6 1

1.89

3 .42

4.58

0.020

0.86

0.984

30%

2.34

3.57

3.94

5.17

9.42

12.37

0.06 1

I .48

0.993

Time (minutes)

15

30

45

60

120

180

slope

b

r2

30%

1 .O 1

1.32

2 .O5

2.35

4.0 1

6.3 1

0.032

0.44

0.993

10%

6.40

12.83

15.36

21.33

31.23

38.59

O. 186

6.86

0.960

0.95

1 .26

1.52

2.83

4.19

0.022

0.27

0.999

1.28

1.75

2 .O9

3.20

5.38

0.026

0.45

0.984

20%

4.23

5.72

7.29

9.52

16.18

22.97

0.1 13

2.39

0.999

0.95

I , O 9

1 .-CS

2.47 1

3 .56

0.017

0.41 1

0.997 1

Sucrose Content (grarns)

1 Time 1 10% 1 20% 1 30% 1 1 (min) 1 Mean 1 Std. Dev. 1 Mean 1 Std. Dev. [ Mean ( Std. Drv. 1

Time (min)

O 1 5

40%

9.2 1 E-OS 1.65E-04

6.19E-04 4.80E-03

Mean Std. Dev. 50%

4.07E-04 5.04E-O3

Mean 60% - ,

Std. Dev. Mean

6.03E-05 3.56E-04

Std. Dev.

4.16E-04 4.64E-03

5.36E-05 4.06E-O4

APPENDIX H

Equations for Determining Mass Transfer Coefficients for Different Concentrations of Osrnotic Solutions

(Developed by: Gianfranco Mazzanti and Marc Le Magurr)

The value of k at time zero is defined as:

This relationship was developed 3s follows: going bück to the definition of J. the initial

flux of water or solids and defined as:

dWL dSG J ,,, A = - for water. or J , A = - t'or solids

dt d f

or:

for water:

d M , J , , A =- CM, for waier. or J , A = - for solids

dt i f t

where the mass of water at any time, MY = M,W; - M,WL, and the derivative of this

equation is:

dMk -- d WL, - -Mt, - dt dt

which is equal to:

The rate of mass trünsfer at the initiül time is only affectecl by the coi~cetiii;iiiui~ i i i '

the water in the ce11 and in the osmotic solution. the density of the solution. the üreii of

the tissue and the mass transfer coefficient, k, thus:

dM '.+ = -~kp(w,U - w,;~'O) )

dt

At time. zero, the rate is equal to the initial flux of water (or sugiir):

then substituting M w / d t for Jtv becomes:

and the m a s transfer coefficient, k,, is:

Simplifying yields:

According to Geankoplis ( l983), for a laminar Flow p u t a subnicti-grd t'lrir wrtice.

the Sh = (k , '*L)/D = 0.664 RtF(112) ScA(l /3) f 35% where:

k, '( ~ ~ V t h 1 l.

Sh (Sherwood number) = 4,

P Sc (Schmidt number) = - PD,,,

The mass transfer coefficient of sucrose, k,., taken t'rom the Sherwood number

when divided with the total concentration of the solution becomes k,:

and Cr is defined as:

The effect of the water flux on the m a s transfer coefficient is corrected by

computing for the flux ratio. R (Bird. 1965):

x,=mole fraction at the interfiice

x,,=mole fraction of the solution

J, = flux of sucrose. krnollm's

J p flux of water, kmollm's

The correction factor, 8 , is defined as:

The corrected mass transfer coefficient. k,.= k,0 .This corrected mus transfer

coefficient is expressed in molar terms as km011 rn's.

Once the xi at the interface is known, then kL, which is the mliss transkr

coefficient in m a s tenns can be computed using ü similar equütion in müss b r m :

and its relationship with the flux. J:

xi=mole fraction of sucrose at the interface

x,,=mass früction of sucrose at the interface

x,= rnüss fraction of sucrose in solution

J, = mass flux of sucrose. krnollm's

I d mass flux of water, kmoI/m2s

That yields:

The resistance in the active layer in the material ( I/ku) affects the overdl

resistance during mass transfer since: