Karim, Azharul This ﬁle was downloaded … · 2021. 1. 16. · 1 Non-destructive investigation of...

This may be the author’s version of a work that was submitted/acceptedfor publication in the following source:

Rahman, Mohammad Mahbubur, Joardder, Mohammad Uzzal Hossain, &Karim, Azharul(2018)Non-destructive investigation of cellular level moisture distribution andmorphological changes during drying of a plant-based food material.Biosystems Engineering, 169, pp. 126-138.

This file was downloaded from: https://eprints.qut.edu.au/117264/

c© Consult author(s) regarding copyright matters

This work is covered by copyright. Unless the document is being made available under aCreative Commons Licence, you must assume that re-use is limited to personal use andthat permission from the copyright owner must be obtained for all other uses. If the docu-ment is available under a Creative Commons License (or other specified license) then referto the Licence for details of permitted re-use. It is a condition of access that users recog-nise and abide by the legal requirements associated with these rights. If you believe thatthis work infringes copyright please provide details by email to [email protected]

License: Creative Commons: Attribution-Noncommercial-No DerivativeWorks 4.0

Notice: Please note that this document may not be the Version of Record(i.e. published version) of the work. Author manuscript versions (as Sub-mitted for peer review or as Accepted for publication after peer review) canbe identified by an absence of publisher branding and/or typeset appear-ance. If there is any doubt, please refer to the published source.

https://doi.org/10.1016/j.biosystemseng.2018.02.007

https://eprints.qut.edu.au/view/person/Rahman,_Mohammad_Mahbubur.htmlhttps://eprints.qut.edu.au/view/person/Joardder,_Mohammad.htmlhttps://eprints.qut.edu.au/view/person/Karim,_Azharul.htmlhttps://eprints.qut.edu.au/117264/https://doi.org/10.1016/j.biosystemseng.2018.02.007

Non-destructive investigation of cellular level moisture distribution and morphological 1 changes during drying of a plant-based food material 2

M.M. Rahman1, Mohammad U.H. Joardder1, and M.A. Karim1* 3 4

1School of Chemistry, Physics and Mechanical Engineering, 5 Faculty of Science and Engineering 6

Queensland University of Technology, 7 Brisbane, Queensland, Australia 8

9

*Author to whom correspondence should be addressed; 10

E-Mail: [email protected] 11

12

13

14

1

Abstract 15

This study investigates the complex microstructural changes and cell-level water 16

transportation in plant-based food materials during drying, using X-ray micro-computed 17

tomography (X-ray µCT). The investigations were performed on apple tissue to uncover the 18

cellular level moisture distribution and the structural changes during convective drying at 19

50 °C, 60 °C, and 70 °C. Image analysis revealed that significant changes occurred in 20

moisture content, and cell and pore size distribution with drying time and temperature. The 21

moisture content determined using the X-ray µCT images was compared with that 22

determined by the electronic moisture analyser (EMA) and good agreement was found. The 23

results show a strong relationship between drying temperature, pore formation and 24

deformation of the food material. At high drying temperature, the pore formation increased, 25

which led to reduced shrinkage of the food material. The porosity of a sample of dried apple 26

increased by 35% as drying temperature increased from 50 °C to 70 °C. However, a 27

significant amount of cell rupture was observed during drying at the higher temperature. The 28

cellular level moisture distribution profile confirmed that a traceable amount of water was 29

still present in the centre cells of the tissue although the sample was deemed dried from the 30

bulk moisture analysis. The findings of this study substantially enhance our understanding of 31

instantaneous cellular level moisture distribution in a food sample over the time of drying. 32

Keywords: Food drying, cell rupture, porosity, transport, X-ray micro-tomography, water 33

distribution. 34

35

36

37

38

2

1. Introduction 39

40

The principle purpose of food drying is to remove moisture from the food material to increase 41

its shelf life while maintaining the quality of the product. Drying of plant-based food 42

materials is a complex process as it involves simultaneous heat and mass transfer and micro-43

level changes (Kumar et al., 2012; Rahman et al.,2016a). The microstructural changes 44

critically influence the overall transport process and the physical behaviour of food materials 45

(Rahman, Joardder, Khan, Nghia, & Karim, 2016). Moreover, moisture transport through the 46

intercellular spaces, and cells and cell walls of food materials occurs at the micro-level 47

(Aguilera, Chiralt, & Fito, 2003; Rahman, Joardder, Khan, Nghia, & Karim, 2016). 48

Therefore, it is essential to understand the micro-level transport mechanism and the 49

microstructural changes that take place during drying. 50

Plant-based food materials consist of cellular tissues, and the tissue consists of cells and 51

intercellular spaces (Joardder, Kumar, & Karim, 2015). A major component of plant-based 52

food materials is the water that is distributed in the microstructure. There are two types of 53

water in the food microstructure, which are classified based on their spatial locations. These 54

are free water (intercellular water) and bound water (intracellular water) (Khan, Wellard, 55

Nagy, Joardder, & Karim, 2017a). The bound water is located inside the cells whereas the 56

free water is located in the intercellular spaces. Research has shown that about 85% to 95% 57

of the water is bound in food materials, and the rest of the water remains in the intercellular 58

spaces and cell walls (Khan et al., 2016). The transport of bound and free water govern the 59

overall drying process (Rahman et al., 2016b), and therefore, it is crucial to understand the 60

water characteristics and their effect on the evolution of food microstructure in order to make 61

3

an accurate prediction of energy requirement and the quality changes during food processing 62

especially drying. 63

The transport of water inside food tissue can take three pathways during drying, namely 64

symplastic, apoplastic and transcellular transport (Joardder, Kumar, & Karim, 2015). When 65

the sample has high moisture content, liquid water flows due to dominating capillary forces. 66

The bound water has lower diffusivity than the free water when the cells are intact (Fanta et 67

al., 2012). Therefore, the required energy for removing the bound water and free water from 68

the food microstructure is different. Bound water removal also affects the microstructure of 69

the food materials (Khan, Wellard, Nagy, Joardder, & Karim, 2017). Consequently, 70

knowledge of exact proportion of free and bound water is important and an appropriate non-71

destructive means of bound water measurement is crucial as destructive methods lead to 72

inaccurate measurements (Léonard et al. 2005). 73

There are several methods in the literature for determining cell-level water content in a fresh 74

food sample, including differential scanning calorimetry, bioelectrical impedance analysis, 75

dilatometry, thermogravimetric analysis, and nuclear magnetic resonance (NMR) (Khan & 76

Karim, 2017). The different types of water content inside food materials during drying were 77

determined by Khan et al. (2016a) using the NMR technique. However, the cell-wall 78

structure, cellular water, and intercellular water distribution cannot be identified clearly by 79

this method. Moreover, Khan et al.’s determination of various water contents relied on many 80

assumptions. To date, no research has been conducted to investigate the distribution of 81

cellular water (intracellular and intercellular water) during drying of food material. 82

It has been anticipated that the cell rupture takes place under certain drying conditions (Khan 83

et al., 2017; Rizzolo et al., 2014). Cell rupture depends on internal thermal stress that first 84

develops near the surface and gradually penetrates to the centre of the sample during 85

4

convective drying (Khan et al., 2017). In other words, the entire food sample does not reach 86

breakdown temperature at the same time. Therefore it is a logical assumption that the cells 87

may collapse progressively from the surface to centre (Achanta & Okos, 1996; Askari, 88

Emam-Djomeh, & Mousavi, 2009; Gumeta-Chavez et al., 2011; Khan et al., 2017; Rahman 89

et al., 2015; Riva, Campolongo, Leva, Maestrelli, & Torreggiani, 2005; Yang, Di, Jiang, & 90

Zhao, 2010). Khan et al. (2017) reported that cells rupture at different stages of drying. 91

However, this progressive rupture was assumed, based on the pattern of the intercellular 92

water content. No clear evidence of cell rupture during drying was presented. 93

More complete microstructural information regarding the porosity and the cell rupture of 94

plant-based food is required to improve the physical quality of dried food. Although the 95

investigation of cellular level changes is critical to anticipate the overall shrinkage and 96

porosity of the food materials, only a limited number of studies have been found that dealt 97

with cellular level transport during drying (Joardder, Kumar, Brown, & Karim, 2015; 98

Karunasena et al., 2014; Khan et al., 2017). Most of the porosity measurement techniques 99

reported in the literature (e.g. solid displacement method) are destructive and invasive 100

(Joardder, Kumar, Brown, et al., 2015; Joardder, Kumar, & Karim, 2017). Therefore these 101

methods are not suitable to determine the instantaneous morphological changes during 102

drying. The limitations of destructive and invasive methods justify the importance of non-103

destructive and non-invasive methods. 104

Khan et al (2017) reported the NMR technique to be a suitable technique to uncover the 105

cellular level water distribution. However, this technique produces a large amount of noisy 106

data, and variations in cellular level water distribution cannot be readily identified. X-ray 107

µCT has proved to be valuable in the study of plants and plant-based food materials, 108

reflecting anatomical details of the entire tissue, especially the microstructure and the water 109

5

distribution (Herremans et al.,2015; Mendoza et al.,2007). X-ray µCT is the most suitable 110

technique to address the elucidation of the cellular level transport mechanism during food 111

drying (Lim & Barigou, 2004; Rahman et al., 2016). 112

The earliest applications of computed tomography were in medicine. However, it has rapidly 113

become a very useful tool in physics (Wildenschild, Vaz, Rivers, Rikard & Christensen, 114

2002), biology (Momose, Takeda, Itai & Hirano, 1996), materials science (Salvo et al., 2003) 115

and multiscale engineering (Torre, Losada & Tarquis, 2016; Wood, Zerhouni, Hoford, 116

Hoffman & Mitzner, 1995). Recently, micro-tomography, based on micro-focus X-ray 117

sources, has become a relatively common tool for the characterisation of agricultural food 118

products (Donis-González, Guyer, Pease & Barthel, 2014; Schoeman, Williams, du Plessis & 119

Manley, 2016; van Dael et al., 2017) including apple (Almeida, Lancha, Pierre, Casalinho & 120

Perré, 2017; Diels et al., 2017; Si & Sankaran, 2016; Verboven et al., 2013), banana 121

(Madiouli et al., 2011), mango (Cantre, Herremans, Verboven, Ampofo-Asiama & Nicolaï, 122

2014), grains (Neethirajan, Karunakaran, Jayas & White, 2006; Zhu et al., 2012), kiwi 123

(Cantre et al., 2014), pears (Cantre et al., 2014; Muziri et al., 2016), carrot (Voda et al., 124

2012),and cucumbers (Donis-González, Guyer, Pease & Barthel, 2014). Today, commercially 125

available X-ray µCT allows non-destructive 3-D reconstruction of food materials at a spatial 126

resolution of a few micrometres (Verboven et al., 2008). 127

X-ray µCT was applied for the quantification of 3D pore space in fresh apple tissue 128

(Mendoza et al., 2007). Moreover, the X-ray µCT was used to characterise the multiscale gas 129

transport pathway in fresh apple and pome fruit, (Herremans et al., 2015; Verboven et al., 130

2008). Microstructural characterisation of mango ripening was also investigated by the X-ray 131

µCT (Cantre, Herremans, et al., 2014). 132

6

However, the use of X-ray µCT for determining the micro-level changes during food drying 133

is limited in literature. This technique was used to determine the impact of freeze drying on 134

the microstructure of dried carrot, but the real-time moisture distribution inside the 135

microstructure during drying process was not investigated (Voda et al., 2012). Therefore, the 136

evolution of micro-level changes during drying could not be understood. Limited research 137

has been done regarding the non-invasive investigation of the cellular level transport 138

mechanism during drying of food materials (Almeida et al., 2017; Rathnayaka, Karunasena, 139

Senadeera, & Gu, 2017). Moreover, much of the research failed to elucidate the role of food 140

microstructure changes on the drying kinetics (Schoeman et al., 2016). Therefore, there is a 141

critical need to use X-ray µCT to investigate cellular level phenomena during drying of plant-142

based food materials. 143

The primary objective of this work is to investigate experimentally the instantaneous 144

morphological changes, real-time cellular level moisture distribution, and cell and pore size 145

distribution during drying of plant-based food materials. 146

2. Materials and methods 147 148

2.1 Sample preparation and the drying experiment 149

Granny Smith apple was used for the experiments. The fresh apples were collected from the 150

local market and stored in a refrigerator at 4 °C to keep them fresh before the drying 151

experiment. Prior to the drying test, the samples were taken from the refrigerator and kept at 152

the room temperature for an hour to achieve thermal stability. The samples were then cut as 153

cylindrical slices from the middle parenchyma region (2 cm to 3 cm under the peel) of the 154

apples with the dimension of 1 mm thickness and 5 mm diameter. For each drying 155

experiment, six samples were used, and each sample was taken from the same region of six 156

different Granny Smith apples. In order to investigate the influence of sample size on the 157

7

morphological changes during drying, a larger sample with the dimension of 20 mm diameter 158

and 10 mm thickness was also prepared from the middle parenchyma region. 159

160

The dryer was run for 30 min before the experiment, to allow the system to reach steady 161

state. The drying experiments were carried out in a laboratory-scale temperature-controlled 162

cabinet-type convective dryer. The dryer had an electric heater and a fan to produce 163

controlled perpendicular hot air flow across the samples throughout the experiment. The 164

temperature of the dryer could be adjusted within the temperature range 30 °C to 70 °C. To 165

ensure uniform heat transfer, the samples were placed symmetrically on the same tray in 166

close proximity to the central axis and air inlet vent of the equipment. The samples were 167

placed in the tray only after the dryer reached a steady state condition. The drying 168

experiments performed were based on the American Society of Agricultural and Biological 169

Engineers (ASABE S448.1) standard at three different temperatures 50 °C, 60 °C and 70 °C. 170

This range of temperatures is widely used in the drying industries for the maximum retention 171

of quality attributes (Tsotsas & Mujumdar., 2011). During each experiment, air velocity was 172

0.7 m s-1 and relative humidity ranged between 60% and 65%. The samples were taken out 173

from the dryer every 30 minutes for X-ray µCT experiments. Before placing them into the X-174

ray µCT, the samples were kept in a desiccator to avoid the natural rehydration from the 175

ambient humidity. The samples were then scanned using a Scanco µCT40 high-resolution 176

desktop µ-CT system (Scanco Medical, Brüttisellen, Switzerland) with a resolution of 6 µm. 177

After scanning, the samples were placed back into the dryer, and the scanned images were 178

saved for further investigation. The moisture content of the sample at the different stages of 179

drying was measured by EMA (KERN_MLA, Germany). The moisture analyser was 180

calibrated by the AOAC method (Official method 1995) (Ahn, Kong, & Kim, 2014). The 181

initial moisture content of the fresh apple sample was determined to be 90% wet basis. 182

8

2.2 X-ray micro-tomography 183

The Scanco µCT-40 was used in this study to investigate the micro-level transport 184

phenomena. The moisture content obtained from the X-ray µCT method was compared with 185

the results from the moisture analyser to justify the applicability of the approach. X-ray µCT 186

uses x-rays to create 2D cross-sections of a physical object that are then used to create a 187

virtual model (3D model) without destroying the original object. It is able to scan the entire 188

sample to obtain micro-level information such as moisture distribution, cells, and pore size 189

distribution without the need of serial cuts or chemical treatment. Therefore, the same sample 190

can be analysed at different stages of the drying process. The scanning was operated at 55 191

kV, and the images were taken through 0° to 360° of rotation. The detector of the X-ray 192

tomography is a 2D camera with the resolution of 4069×4069 pixels and spatial resolution of 193

6µm. The samples were rotated horizontally between 0° to 360° angle to get the 2D scans at 194

different vertical positions. The minimum distance between two scans is 6µm. The images of 195

the middle part of the sample were considered for the analysis. The reconstruction of the 2D 196

images was performed with the built in software packages that uses the back-projection 197

algorithm. The 3D analysis can also be performed from the 2D cross section image stacks of 198

the sample by the Itanium2 based ‘Open VMS’ software package supplied with the Scanco 199

µCT-40 system. However, 2D analysis was performed in this study to get a clear observation 200

of the cross section of the sample. 201

2.4 Image processing 202

The greyscale images obtained from the X-ray µCT were transferred to a MATLAB image 203

processing environment for further processing. Adjustment of the greyscale image contrast 204

was performed by the “imadjust” function of MATLAB. The image processing involved 205

noise elimination and segmentation. The steps involved in the proposed image processing 206

method are presented in Figure 1. 207

9

208

Figure 1. can be inserted here 209

210

The noise elimination was performed by intensity thresholding and filtering. Segmentation 211

was performed after noise elimination to characterise solid materials in the microscopic 212

images. For this purpose, the watershed algorithm was used (Ng, Ong, Foong Goh, & 213

Nowinski, 2006). It was found that the cell wall exhibited a higher intensity value compared 214

to the water-filled cells. The difference between intracellular water and the intercellular water 215

was classified by the manual segmentation process. For the improvement of the classification, 216

a global threshold value was defined based on the intensity of the images. The cells and the 217

intercellular spaces were identified from the reconstructed greyscale images. The 218

classification of the cell wall, water-filled cells and intercellular spaces was performed based 219

on the intensity variation. 220

2.4.1 Measurement of cellular level moisture content 221

Image processing using MATLAB to determine moisture content was performed by creating 222

the region of interest in each part of the scanned image. To determine the moisture content 223

from the micro-tomographic images, the grey level intensity of each section of the image was 224

analysed. The process of determining the moisture content from the tomographic images is 225

available in the literature (Léonard, Blacher, Marchot, Pirard, & Crine, 2005) and the 226

following equation is commonly used: 227

Moisture Content =(Grey level)Dry solid−(Grey level)wet solid

(Grey level)Dry solid−(Grey level)water )3( 228

In equation (3) the (Grey level)Dry solid represents the grey level of cell wall, 229

(Grey level)wet solid represents the intracellular water and (Grey level)water represents the 230

10

intercellular water. The intracellular and the intercellular water contents were obtained from 231

the X-ray µCT images by selecting the region of interest (ROI) manually and applying 232

equation (3). Each of the ROI was selected and analysed manually. The selected ROI in the 233

cellular region provides the information of intracellular water and the selected ROI in the 234

intercellular region provides the information of the intercellular water. The proportion of 235

intracellular water and intercellular water can be expressed by the following relationships 236

Proportion of Intracellular water = Intracellular water Intracellular water+Intercellular water

× 100 (4) 237

Proportion of Intercellular water = Intercellular water Intracellular water+Intercellular water

× 100 (5) 238

2.4.2 Measurement of equivalent cell diameter 239

The equivalent diameter of each region was calculated using the following relationship: 240

Equivalent diameter =�4×𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝜋𝜋

(6) 241

The areas of the cells and intercellular spaces were obtained from the pixels in the X-ray µCT 242 image. 243

2.4.3 Porosity measurement 244

The porosity of the samples at different stages of drying was determined from the X-ray µCT 245

data. The porosity was calculated using the following relationship (Joardder, Karim, Kumar, 246

& Brown, 2015): 247

Porosity = total volume of intercellular spaceTotal volume of the sample

(7) 248

2.4.4 Measurement of cell rupture 249

Digital image correlation (DIC) was used to measure the amount of cell rupture during 250

drying. The DIC technique compares a series of digital photographs at different stages of 251

drying. This technique directly shows the cellular level evolution of food samples to sub-252

11

pixel accuracy. The DIC analysis was performed in MATLAB (MATLAB 2015b) based on 253

the algorithm developed by Pan et al., (2009). The DIC was performed by registering two X-254

ray micro-tomographic images of the sample during drying process. One image was taken 255

from the fresh sample which was considered as reference image and the other images were 256

taken at the different stages of drying for comparing with the reference image. The pixel and 257

the intensity of the images were kept consistent by adjusting the grey level histogram for the 258

effective performance of the DIC process. The ruptures of the cellular structure were 259

evaluated based on the image morphology analysis concept adopted from Lecompte et al. 260

(2006). The morphology analysis is an image processing technique which considers the shape 261

of the elements in the images. The shape of the cells and the intercellular spaces were found 262

from the pixel of the image. The morphological operation was performed by estimating the 263

size and the shape of the corresponding cell or intercellular space in MATLAB image 264

processing toolbox. The detail information regarding the calculation of the structural 265

morphology can be found in author’s previous publication (Rahman et al. 2018). The 266

evaluated ruptures from different sample were compared for the error analysis. 267

2.5 Statistical analysis 268

The experiments were repeated six times for each drying condition. The results were 269

expressed as mean and standard deviation (Swasdisevi, Devahastin, Sa-Adchom, & 270

Soponronnarit, 2009) corresponding to the moisture content and the equivalent diameter of 271

the cells. Moisture profile measured by the moisture analyser and X-ray µ-CT was compared 272

by the coefficient of determination (R2) and standard error of estimation (SEE) analysis. The 273

coefficient of determination (R2) was calculated using the following standard equation: 274

𝑅𝑅2 = [𝑛𝑛 ∑𝑋𝑋𝑋𝑋−(∑𝑋𝑋)(∑𝑋𝑋)]2

[𝑛𝑛 ∑𝑋𝑋2−(∑𝑋𝑋)2][𝑛𝑛 ∑𝑋𝑋2−(𝑋𝑋)2] (6) 275

12

where 𝑅𝑅2 is the coefficient of determination, X is the moisture content determined by the 276

moisture analyser (dry basis), and Y is the moisture content determined by the X-ray µ-CT. 277

The SEE was determined using the following equation: 278

𝑆𝑆𝑆𝑆𝑆𝑆 = �∑(𝑊𝑊−𝑊𝑊� )2

𝑁𝑁−2 (7) 279

where W is the moisture content data, 𝑊𝑊� is the average value of the moisture content data, N 280

is the number of data points. 281

3. Results and discussions 282

3.1 Moisture distribution in fresh sample 283

A tomographic image of the fresh apple slice showing the water distribution is presented in 284

Figure 2. In the figure, the darker colour represents the lower intensity, while the lighter 285

colour represents the high intensity of the µ-CT image. The intensity difference between the 286

intracellular and intercellular water is significant as the concentration of intracellular water is 287

higher than the intercellular water. The intensity difference between the intracellular water 288

and the cell wall is also substantial as the cell walls are made of solid materials. Therefore, 289

the presence of cell walls, intracellular water, and intercellular water can be determined from 290

the X-ray µCT technique. It can be determined from Figure 2 that there was little water (5–291

7%) present in the intercellular space, while about 90% to 95% of the water was inside the 292

cells surrounded by the cell membrane and wall. The greyscale bar on the left side of the 293

image represents the concentration of moisture content throughout the sample. When the 294

greyscale value is 1, it is considered that there is strongly bound water entrapped in the cell 295

wall. On the other hand, the zero-greyscale value represents the void section inside the tissue. 296

It is interesting to note that the moisture distribution in fresh apple tissue was not uniform 297

throughout the whole sample as some pore space was evident in the fresh sample. 298

299

13

Figure 2 can be inserted here 300

301

3.2 Changes in moisture distribution during drying 302 303

A relationship between the bulk moisture content and the cellular level moisture distribution 304

during drying in an apple sample at 60 °C is presented in Figure 3. It is clear from the figure 305

that the reduction of bulk moisture is accompanied by the reduction of intracellular water. 306

307


309

In the initial drying stage, most of the water remains inside the cells. In the literature, the 310

intercellular water is considered as loosely bound water, and therefore it is assumed that the 311

energy requirement for removing intercellular water is low (Khan et al., 2017b; Rahman et 312

al., 2016). The intercellular water migration leads to the fast drying of the intercellular region 313

in the apple sample. As drying progresses, the concentration of intracellular water also 314

decreases. In the first phase of drying the reduction of overall moisture content is significant. 315

In this phase, the transport of cellular water occurs through the cell membrane and cell wall. 316

As drying continues, heat is transferred from the peripheral cells to the internal cells and 317

creates the pressure gradient between intracellular and intercellular spaces (Khan et al., 318

2017b). This phenomenon leads to rupture of the cell membranes of the apple tissue. Rupture 319

of cell membranes facilitates migration of cellular water (both intracellular and intercellular). 320

A clear representation (with larger image) of the cellular level water distribution and the cell 321

rupture at different stages of drying at 60°C is presented in Figure 4. Analysis of the cell 322

rupture is presented in section 3.4. In the last phase of drying (200–350 min), the centre cells 323

contain more intracellular water than intercellular water. Intracellular water is removed in the 324

14

last stages of drying (Khan et al., 2017b). Most of the energy required for the drying process 325

is for the removal of the intracellular water. 326


328

The overall bulk moisture contents during drying at 50 °C, 60 °C, and 70 °C were calculated 329

from the tomographic images using equation (3). For the validation of the proposed method, 330

the moisture content determined from the tomographic images was compared with the results 331

obtained by the moisture analyser. The comparison is presented in Figure 5. A good 332

agreement has been found between the moisture content determined from the tomographic 333

images and the EMA. The coefficient of determination (R2) and the SEE were 0.998 and 334

0.001 respectively. 335

336


Figure 6 shows the evolution of intercellular and intracellular water in the apple sample 338

during drying at 50 °C, 60 °C and 70 °C temperatures. It is clear from the figure that the 339

drying temperature has a major influence on the transport of intracellular water. At the lower 340

drying temperature (50 °C) the intracellular water decreased slowly, as shown in Figure 6a. 341

This is because the cell membrane remains intact during the process and it is called the 342

intracellular transport process (Halder et al. 2011). The loss of intracellular water was 343

significant in 60 °C and 70 °C temperature as shown in Figure 6b and c. This is because 344

significant cellular rupture occurs at higher temperatures, which facilitates the rapid 345

migration of the intracellular water. It was found from the literature that when the 346

15

temperature is above 50 °C, the cell membrane starts to break (Khan et al. 2017; Halder et al. 347

2011). 348

349


3.3 Morphological changes 351

The structural change of food material is the result of cell rupture and shrinkage of the sample 352

during the drying period when significant loss of water takes place (Devahastin & Niamnuy, 353

2010; Ramos, Branda, & Silva, 2003). There is a fine difference between cell rupture and 354

shrinkage of the sample. Cell rupture and shrinkage of the sample are directly related to the 355

cell and the pore size distribution. The cell and pore size distribution inside the sample at 356

different temperatures and drying times is illustrated in Figures 7–9. In these figures, blue 357

bars represent equivalent cell diameter, and the brown bars represent the equivalent diameter 358

of the intercellular spaces. The equivalent diameter of cells and intercellular spaces were 359

obtained from the image analysis by using equation (6). The highest values of equivalent 360

diameter for cell and intercellular spaces were around 310 µm. Therefore, the size 361

distribution of cells and intercellular spaces is presented between 0 µm and 320 µm with an 362

equal interval of 20 µm in order to investigate the evolution of cell and intercellular spaces 363

during drying. In Figures 7-9, the x-axis represents the equivalent diameter of cells and 364

intercellular space and the y-axis represents the percentage of the cells and intercellular 365

spaces fall within a certain range. 366

The size distribution of cells and the intercellular spaces during drying at 60 °C is presented 367

in Figure 7. In the fresh sample, the cell size was larger than the pore or intercellular space, as 368

shown in Figure 7a. It can be seen in the figure that most of the cells had equivalent 369

diameters in between 181 µm and 220 µm while the equivalent diameter of the intercellular 370

16

space mostly varied from 50 µm to 220 µm. It was found from the literature that food 371

materials shrink without cell collapse in the first phase of the drying process. As the drying 372

progressed, the cell size reduced slowly while the pore size distribution changed rapidly 373

(Figure 7b). The diameter of most of the cells remained in between 120 µm and 180 µm. 374

According to the literature, the water migration process in this stage is mainly the free water 375

removal (Khan et al. 2017). During the drying process, negative pressure is created inside the 376

cells due to the removal of water, which is the major reason behind the cellular level 377

deformation (Joardder, Karim, Kumar, & Brown, 2016). Owing to ongoing moisture 378

migration, the collapse of cells and intercellular spaces increased in the later stages of drying. 379

At this stage, the equivalent diameter of most of the intercellular spaces was higher than 100 380

µm and the majority were in between 120 µm and 140 µm. The pore size distribution was 381

greater than the cell size distributions at the later phase of the drying process shown in Figure 382

7c and d. 383


The temperature has a great effect on the size distribution of cells and the intercellular spaces 385

during drying. Figure 8 shows the size distribution of cells and intercellular spaces at 386

different stages of drying at 50 °C. When the apple was dried at a lower temperature (50 °C), 387

there was less cell collapse but more shrinkage of the tissue due to the prolonged drying time. 388

After 60 min of drying, the equivalent diameter of the cells was in between to 120 µm and 389

140 µm. It is clear from Figure 8a that the equivalent diameter of the intercellular spaces did 390

not increase significantly in this phase of drying. At the later phase of the drying process 391

(Figure 8b and c), the equivalent diameter of most of the cells dropped to 100 µm. On the 392

other hand, the diameter for most of the intercellular spaces did not increase over 150 µm. 393


17

Figure 9 shows the size distribution of cells and intercellular spaces at different phases of 395

drying at 70 °C. As shown in Figure 9a, after 60 min of drying time the diameter of 35% of 396

the cells reduced to 120 µm, but the diameter of 38% of the intercellular spaces remained 397

between 120 µm and 150 µm. As drying progressed at high temperature, the size distribution 398

of cells and intercellular spaces also changed significantly. Figure 9 b and c represent the 399

morphological changes in the apple tissue in the later part of drying (200 min and 300 min 400

respectively). The size of the intercellular space was larger than the cell size. At this phase of 401

drying the diameter of more than 40% of the cells dropped below 100 µm. On the other hand, 402

the diameter of more than 60% intercellular space increased to over 150 µm. The results 403

imply that drying at low temperature leads to smaller pore size while drying at high 404

temperature leads to larger pore size. The comparative study of the porosity evolution in the 405

apple sample at different drying temperatures is discussed in section 3.5. 406


3.4 Analysis of the cell rupture 408

As shown in this study, the rupture of the cellular structure takes place during drying. Drying 409

temperature significantly contributes to the cell rupture. The drying parameters like air flow 410

and humidity usually play a less significant role in the dehydration process (Kumar et al. 411

2015). Figure 10 shows the percentage of cell rupture at different temperatures and different 412

stages of drying. It is clear from the figure that cell rupture increased significantly with 413

temperature. After drying at a temperature of 50 °C, around 22% of the cells of the dried 414

sample had ruptured, while at 60 °C and 70 °C the cell rupture levels were 70% and 82% 415

respectively. 416


18

The rupture of the cellular structure at different stages of drying at 60 °C temperature is 418

clearly visible in Figure 4. Figure 11 shows X-ray µ CT image of the larger sample of apple 419

microstructure dried at 60 °C. It is clear from the analysis that cell rupture is mainly assisted 420

by the temperature. It was found that when the experiment was conducted with the larger 421

sample at 60 °C, the drying time extended up to 540 min. Therefore, it takes more time for 422

cell rupture to occur during drying. However, the cell rupture followed similar trends in 423

breaking sequence during the high-temperature drying process. A significant amount of cell 424

rupture was still observed in the micro-tomographic image of the dried apple sample. 425


427

3.5 Porosity 428

The porosity of the samples was calculated from the images using equation (7). The porosity 429

of the plant-based food materials also showed an interesting relationship between moisture 430

content and the drying temperature. Figure 12 shows the changes of porosity of apple sample 431

at different drying temperature against the moisture content. It is clear from the figure that 432

drying the food material at high temperature led to higher porosity. 433


It is shown in Figure 12 that a decreasing trend of porosity was found at the beginning of 435

drying and just before an increasing trend started at a moisture content of 0.7. After that, 436

porosity kept increasing steadily with moisture content until the end of the drying process. 437

The drying temperature influenced the overall structure of the food materials by contributing 438

to the pore formation inside the sample. From the figure, it can be seen that the porosity 439

19

increased by 35% with an increase of drying air temperature from 50 °C to 70 °C. The 440

mechanism of the pore formation is discussed by the authors in their previous work (Joardder 441

et al., 2016). This higher porosity in the case of drying at higher temperature can be referred 442

to a shorter time required for drying. 443

4. Conclusion 444

This paper presents the analysis of cellular level water distribution, morphological changes, 445

and cell and pore size distribution of apple samples during drying using a non-destructive X-446

ray µCT method. It has been found that the cell and the pore size distribution of the food 447

material change significantly over the drying time. The investigation also showed that the 448

transport of water inside the cellular tissue of food material is governed by the drying 449

temperature. At the first stage of drying, most of the water is bound water while in the later 450

stage of drying most of the water becomes ‘free water’ as cells are ruptured, and intracellular 451

water migrates to intercellular spaces. Another striking finding of this study is that high-452

temperature drying leads to less shrinkage despite a significant amount of cell rupture at a 453

higher temperature. Other process parameters like the air velocity and the air humidity have a 454

lower effect (compared to temperature) on cell rupture as the drying process is not 455

significantly influenced by these two parameters (Kumar et al., 2015). This study also found 456

that porosity is influenced by the drying temperature. The porosity of the dried apple 457

increased by 35% with the increase of temperature from 50 °C to 70 °C. The findings of this 458

study will enable future researchers to better understand the relationship between drying 459

conditions and micro-level transport phenomena. In addition, the findings will assist food 460

engineers in the development and validation of an accurate transport model, which will lead 461

to the design of an energy-efficient food drying system. 462

20

Acknowledgement 463

This research is supported by Queensland Government Advanced Queensland Fellowship. 464

The authors acknowledge the facilities and the technical assistance of the Institute of Health 465

and Biomedical Innovation. The authors also gratefully acknowledge the support of Dr. 466

Marie-Luise Wille. 467

References 468

Achanta, S., & Okos, M. R. (1996). Predicting the Quality of Dehydrated Foods and 469 Biopolymers — Research Needs and Opportunities. Drying Technology, 14(6), 1329-470 1368. doi:10.1080/07373939608917149 471

Aguilera, J. M., Chiralt, A., & Fito, P. (2003). Food dehydration and product structure. 472 Trends in Food Science & Technology, 14(10), 432-437. doi:10.1016/s0924-473 2244(03)00122-5 474

Ahn, J. Y., Kil, D. Y., Kong, C., & Kim, B. G. (2014). Comparison of oven-drying methods 475 for determination of moisture content in feed ingredients. Asian-Australasian journal 476 of animal sciences, 27(11), 1615. 477

Almeida, G., Lancha, J. P., Pierre, F., Casalinho, J., & Perré, P. (2017). Physical behavior of 478 highly deformable products during convective drying assessed by a new experimental 479 device. Drying Technology, 35(8), 906-917. 480

Askari, G. R., Emam-Djomeh, Z., & Mousavi, S. M. (2009). An investigation of the effects 481 of drying methods and conditions on drying characteristics and quality attributes of 482 agricultural products during hot air and hot air/microwave-assisted dehydration. 483 Drying Technology, 27(7-8), 831-841. doi:10.1080/07373930902988106 484

Cantre, D., East, A., Verboven, P., Araya, X. T., Herremans, E., Nicolaï, B. M., . . . Heyes, J. 485 (2014). Microstructural characterisation of commercial kiwifruit cultivars using X-ray 486 micro computed tomography. Postharvest Biology and Technology, 92, 79-86. 487

Cantre, D., Herremans, E., Verboven, P., Ampofo-Asiama, J., & Nicolaï, B. (2014). 488 Characterization of the 3-D microstructure of mango (Mangifera indica L. cv. 489 Carabao) during ripening using X-ray computed microtomography. Innovative Food 490 Science & Emerging Technologies, 24, 28-39. 491

Devahastin, S., & Niamnuy, C. (2010). Modelling quality changes of fruits and vegetables 492 during drying: a review. International Journal of Food Science and Technology, 493 45(9), 1755-1767. doi:10.1111/j.1365-2621.2010.02352.x 494

Diels, E., van Dael, M., Keresztes, J., Vanmaercke, S., Verboven, P., Nicolai, B., . . . Smeets, 495 B. (2017). Assessment of bruise volumes in apples using X-ray computed 496 tomography. Postharvest Biology and Technology, 128, 24-32. 497

Donis-González, I. R., Guyer, D. E., Pease, A., & Barthel, F. (2014). Internal characterisation 498 of fresh agricultural products using traditional and ultrafast electron beam X-ray 499 computed tomography imaging. Biosystems Engineering, 117, 104-113. 500 doi:http://dx.doi.org/10.1016/j.biosystemseng.2013.07.002 501

21

Fanta, S. W., Vanderlinden, W., Abera, M. K., Verboven, P., Karki, R., Ho, Q. T., . . . 502 Nicolaï, B. M. (2012). Water transport properties of artificial cell walls. Journal of 503 Food Engineering, 108(3), 393-402. 504

Gumeta-Chavez, C., Chanona-Perez, J. J., Mendoza-Perez, J. A., Terres-Rojas, E., Garibay-505 Febles, V., & Gutierrez-Lopez, G. F. (2011). Shrinkage and deformation of Agave 506 atrovirens Karw tissue during convective drying: Influence of structural arrangements. 507 Drying Technology, 29(6), 612-623. 508

Halder, A., Datta, A. K., & Spanswick, R. M. (2011). Water transport in cellular tissues 509 during thermal processing. AIChE Journal, 57(9), 2574-2588. 510

Herremans, E., Verboven, P., Verlinden, B. E., Cantre, D., Abera, M., Wevers, M., & 511 Nicolaï, B. M. (2015). Automatic analysis of the 3-D microstructure of fruit 512 parenchyma tissue using X-ray micro-CT explains differences in aeration. BMC plant 513 biology, 15(1), 264. 514

Joardder, M. U., Karim, A., Kumar, C., & Brown, R. J. (2015). Porosity: Establishing the 515 Relationship Between Drying Parameters and Dried Food Quality: Springer. 516

Joardder, M. U., Karim, A., Kumar, C., & Brown, R. J. (2016). Pore Formation and 517 Evolution During Drying Porosity (pp. 15-23): Springer. 518

Joardder, M. U., Kumar, C., Brown, R. J., & Karim, M. (2015). A micro-level investigation 519 of the solid displacement method for porosity determination of dried food. Journal of 520 Food Engineering, 166, 156-164. 521

Joardder, M. U., Kumar, C., & Karim, M. A. (2017). Food structure: Its formation and 522 relationships with other properties. Critical reviews in food science and 523 nutrition, 57(6), 1190-1205. doi:10.1080/10408398.2014.971354 524

Joardder, M. U., Kumar, C., & Karim, M. A. (2017). Prediction of porosity of food materials 525 during drying: Current challenges and directions. Critical Reviews in Food Science 526 and Nutrition, 1-12. 527

Karunasena, H., Hesami, P., Senadeera, W., Gu, Y., Brown, R. J., & Oloyede, A. (2014). 528 Scanning electron microscopic study of microstructure of gala apples during hot air 529 drying. Drying Technology, 32(4), 455-468. doi:10.1080/07373937.2013.837479 530

Khan, M. I. H., Joardder, M. U. H., Kumar, C., & Karim, M. A. (2017a). Multiphase Porous 531 Media Modelling: A novel approach to predicting food processing 532 performance. Critical reviews in food science and nutrition, 1-19. 533

Khan, M. I. H., & Karim, M. A. (2017). Cellular water distribution, transport, and its 534 investigation methods for plant-based food material. Food Research International, 99, 535 1-14. 536

Khan, M. I. H., Wellard, R. M., Nagy, S. A., Joardder, M., & Karim, M. (2016a). 537 Investigation of bound and free water in plant-based food material using NMR T 2 538 relaxometry. Innovative Food Science & Emerging Technologies, 38, 252-261. 539

Khan, M. I. H., Wellard, R. M., Nagy, S. A., Joardder, M., & Karim, M. (2017b). 540 Experimental investigation of bound and free water transport process during drying of 541 hygroscopic food material. International Journal of Thermal Sciences, 117, 266-273. 542 doi:https://doi.org/10.1016/j.ijthermalsci.2017.04.006 543

22

Kumar, C., Millar, G. J., & Karim, M. A. (2015). Effective diffusivity and evaporative 544 cooling in convective drying of food material. Drying technology, 33(2), 227-237. 545 doi:10.1080/07373937.2014.947512 546

Kumar, C., Karim, A., Saha, S. C., Joardder, M. U. H., Brown, R. J., & Biswas, D. (2012). 547 Multiphysics modelling of convective drying of food materials. Paper presented at the 548 Proceedings of the Global Engineering, Science and Technology Conference. 549

Lecompte, D., Smits, A., Bossuyt, S., Sol, H., Vantomme, J., Van Hemelrijck, D., & 550 Habraken, A. M. (2006). Quality assessment of speckle patterns for digital image 551 correlation. Optics and lasers in Engineering, 44(11), 1132-1145. 552

Léonard, A., Blacher, S., Marchot, P., Pirard, J. P., & Crine, M. (2005). Moisture Profiles 553 Determination During Convective Drying Using X ‐Ray M icrotomog The 554 Canadian Journal of Chemical Engineering, 83(1), 127-131. 555

Lim, K., & Barigou, M. (2004). X-ray micro-computed tomography of cellular food products. 556 Food Research International, 37(10), 1001-1012. 557

Madiouli, J., Sghaier, J., Orteu, J.-J., Robert, L., Lecomte, D., & Sammouda, H. (2011). Non-558 contact measurement of the shrinkage and calculation of porosity during the drying of 559 banana. Drying Technology, 29(12), 1358-1364. 560

Mathworks. (2015b). Retrieved from https://au.mathworks.com/help/doc-archives.html 561

Mayor, L., & Sereno, A. (2004). Modelling shrinkage during convective drying of food 562 materials: a review. Journal of Food Engineering, 61(3), 373-386. 563

Mendoza, F., Verboven, P., Mebatsion, H. K., Kerckhofs, G., Wevers, M., & Nicolaï, B. 564 (2007). Three-dimensional pore space quantification of apple tissue using X-ray 565 computed microtomography. Planta, 226(3), 559-570. 566

Momose, A., Takeda, T., Itai, Y., & Hirano, K. (1996). Phase–contrast X–ray computed 567 tomography for observing biological soft tissues. Nature medicine, 2(4), 473-475. 568

Muziri, T., Theron, K., Cantre, D., Wang, Z., Verboven, P., Nicolai, B., & Crouch, E. (2016). 569 Microstructure analysis and detection of mealiness in ‘Forelle’pear (Pyrus communis 570 L.) by means of X-ray computed tomography. Postharvest Biology and Technology, 571 120, 145-156. 572

Neethirajan, S., Karunakaran, C., Jayas, D. S., & White, N. D. G. (2006). X-ray Computed 573 Tomography Image Analysis to explain the Airflow Resistance Differences in Grain 574 Bulks. Biosystems Engineering, 94(4), 545-555. 575 doi:http://dx.doi.org/10.1016/j.biosystemseng.2006.04.013 576

Ng, H. P., Ong, S. H., Foong, K. W. C., Goh, P. S., & Nowinski, W. L. (2006, March). 577 Medical image segmentation using k-means clustering and improved watershed 578 algorithm. In Image Analysis and Interpretation, 2006 IEEE Southwest Symposium 579 on (pp. 61-65). 580

Pan, B., Qian, K., Xie, H., & Asundi, A. (2009). Two-dimensional digital image correlation 581 for in-plane displacement and strain measurement: a review. Measurement science 582 and technology, 20(6), 062001. 583

584 Rahman, M. M., Mustayen, A. G. M. B., Mekhilef, S., & Saidur, R. (2015). The optimization 585

of solar drying of grain by using a genetic algorithm. International journal of green 586 energy, 12(12), 1222-1231. 587

23

https://au.mathworks.com/help/doc-archives.html

Rahman, M. M., Mekhilef, S., Saidur, R., Mustayen Billah, A. G. M., & Rahman, S. M. A. 588 (2016a). Mathematical modelling and experimental validation of solar drying of 589 mushrooms. International Journal of Green Energy, 13(4), 344-351. Doi 590 :10.1080/15435075.2014.952425Rahman, M. M., Joardder, M. U. H., Khan, M. I. H., 591 Nghia, D. P., & Karim, M. A. (2016). Multi-scale model of food drying: Current 592 status and challenges. Critical reviews in food science and nutrition, 00-00. 593 doi:10.1080/10408398.2016.1227299 594

Rahman, M. M., Joardder, M. U., Khan, M. I. H., Nghia, D. P., & Karim, M. A. 595 (2016b). Multi-scale model of food drying: Current status and 596 challenges. Critical reviews in food science and nutrition, (just-accepted), 597 00-00. 598

Rahman, M. M., Gu, Y. T., & Karim, M. A. (2018). Development of Realistic Food 599 Microstructure Considering the Structural Heterogeneity of Cells and Intercellular 600 Space. Food Structure,15(15),9-16 601

Ramos, I. N., Branda˜o, T. R. S., & Silva, C. L. M. (2003). Structural Changes During Air 602 Drying of Fruits and Vegetables. Food Science Technology International, 9(3), 201–603 206. doi:10.1177/1082013030335522 604

Rathnayaka, C., Karunasena, H., Senadeera, W., & Gu, Y. (2017). Application of 3-D 605 Imaging and Analysis Techniques for the Study of Food Plant Cellular Deformations 606 during Drying. Drying Technology(just-accepted). 607

Riva, M., Campolongo, S., Leva, A. A., Maestrelli, A., & Torreggiani, D. (2005). Structure-608 property relationships in osmo-air-dehydrated apricot cubes. Food Research 609 International, 38(5), 533-542. 610

Rizzolo, A., Vanoli, M., Cortellino, G., Spinelli, L., Contini, D., Herremans, E., . . . 611 Verboven, P. (2014). Characterizing the tissue of apple air-dried and osmo-air-dried 612 rings by X-CT and OCT and relationship with ring crispness and fruit maturity at 613 harvest measured by TRS. Innovative Food Science & Emerging Technologies, 24, 614 121-130. 615

Salvo, L., Cloetens, P., Maire, E., Zabler, S., Blandin, J., Buffière, J.-Y., . . . Josserond, C. 616 (2003). X-ray micro-tomography an attractive characterisation technique in materials 617 science. Nuclear instruments and methods in physics research section B: Beam 618 interactions with materials and atoms, 200, 273-286. 619

Schoeman, L., Williams, P., du Plessis, A., & Manley, M. (2016). X-ray micro-computed 620 tomography (μCT) for non-destructive characterisation of food microstructure. Trends 621 in Food Science & Technology, 47, 10-24. 622

Si, Y., & Sankaran, S. (2016). Computed tomography imaging-based bitter pit evaluation in 623 apples. Biosystems Engineering, 151, 9-16. 624 doi:https://doi.org/10.1016/j.biosystemseng.2016.08.008 625

Swasdisevi, T., Devahastin, S., Sa-Adchom, P., & Soponronnarit, S. (2009). Mathematical 626 modeling of combined far-infrared and vacuum drying banana slice. Journal of Food 627 Engineering, 92(1), 100-106. 628

Torre, I. G., Losada, J. C., & Tarquis, A. M. (2016). Multiscaling properties of soil images. 629 Biosystems Engineering. doi:http://dx.doi.org/10.1016/j.biosystemseng.2016.11.006 630

24

Tsotsas, E., & Mujumdar, A. S. (Eds.). (2011). Modern Drying Technology, Volume 3: 631 Product Quality and Formulation (Vol. 1). John Wiley & Sons. 632

van Dael, M., Verboven, P., Dhaene, J., Van Hoorebeke, L., Sijbers, J., & Nicolai, B. (2017). 633 Multisensor X-ray inspection of internal defects in horticultural products. Postharvest 634 Biology and Technology, 128, 33-43. 635

Verboven, P., Kerckhofs, G., Mebatsion, H. K., Ho, Q. T., Temst, K., Wevers, M., . . . 636 Nicolaï, B. M. (2008). Three-dimensional gas exchange pathways in pome fruit 637 characterized by synchrotron X-ray computed tomography. Plant Physiology, 147(2), 638 518-527. 639

Verboven, P., Nemeth, A., Abera, M. K., Bongaers, E., Daelemans, D., Estrade, P., . . . 640 Vanstreels, E. (2013). Optical coherence tomography visualizes microstructure of 641 apple peel. Postharvest Biology and Technology, 78, 123-132. 642

Voda, A., Homan, N., Witek, M., Duijster, A., van Dalen, G., van der Sman, R., . . . van 643 Duynhoven, J. (2012). The impact of freeze-drying on microstructure and rehydration 644 properties of carrot. Food Research International, 49(2), 687-693. 645

Wildenschild, D., Vaz, C., Rivers, M., Rikard, D., & Christensen, B. (2002). Using X-ray 646 computed tomography in hydrology: systems, resolutions, and limitations. Journal of 647 Hydrology, 267(3), 285-297. 648

Wood, S. A., Zerhouni, E. A., Hoford, J. D., Hoffman, E. A., & Mitzner, W. (1995). 649 Measurement of three-dimensional lung tree structures by using computed 650 tomography. Journal of Applied Physiology, 79(5), 1687-1697. 651

Yang, J., Di, Q., Jiang, Q., & Zhao, J. (2010). Application of pore size analyzers in study of 652 Chinese angelica slices drying. Drying Technology, 28(2), 214-221. 653

Zhu, L.-J., Dogan, H., Gajula, H., Gu, M.-H., Liu, Q.-Q., & Shi, Y.-C. (2012). Study of 654 kernel structure of high-amylose and wild-type rice by X-ray microtomography and 655 SEM. Journal of cereal science, 55(1), 1-5. 656

657

658

25

Abstract1. Introduction2. Materials and methods2.1 Sample preparation and the drying experiment2.2 X-ray micro-tomography2.4 Image processing2.4.1 Measurement of cellular level moisture content2.4.2 Measurement of equivalent cell diameter2.4.3 Porosity measurement2.4.4 Measurement of cell rupture2.5 Statistical analysis

3. Results and discussions3.1 Moisture distribution in fresh sample3.2 Changes in moisture distribution during drying3.3 Morphological changes3.4 Analysis of the cell rupture3.5 Porosity

4. ConclusionAcknowledgementReferences

Karim, Azharul This ﬁle was downloaded … · 2021. 1. 16. · 1 Non-destructive investigation of...

Documents

Transcript of Karim, Azharul This ﬁle was downloaded … · 2021. 1. 16. · 1 Non-destructive investigation of...