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https://doi.org/10.1063/1.4984675

Mechanistic understanding of cellular level of water in plant-based food materialMd. Imran H. Khan, C. Kumar, and M. A. Karim

Citation: AIP Conference Proceedings 1851, 020046 (2017); doi: 10.1063/1.4984675View online: http://dx.doi.org/10.1063/1.4984675View Table of Contents: http://aip.scitation.org/toc/apc/1851/1Published by the American Institute of Physics

Mechanistic Understanding of Cellular Level of Water in Plant-Based Food Material

Md. Imran H. Khan1, 2, a), C. Kumar1, M. A. Karim1, b)

1Science and Engineering Faculty, Queensland University of Technology (QUT), 2 George St, Brisbane, QLD 4000, Australia

2Department of Mechanical Engineering, Dhaka University of Engineering & Technology, Gazipur-1700, Bangladesh

a)Corresponding author: mdimranhossen.khan@hdr.qut.edu.au b) azharul.karim@qut.edu.au

Abstract. Understanding of water distribution in plant-based food material is crucial for developing an accurate heat and mass transfer drying model. Generally, in plant-based food tissue, water is distributed in three different spaces namely, intercellular water, intracellular water, and cell wall water. For hygroscopic material, these three types of water transport should be considered for actual understanding of heat and mass transfer during drying. However, there is limited study dedicated to the investigation of the moisture distribution in a different cellular environment in the plant-based food material. Therefore, the aim of the present study was to investigate the proportion of intercellular water, intracellular water, and cell wall water inside the plant-based food material. During this study, experiments were performed for two different plant-based food tissues namely, eggplant and potato tissue using 1H-NMR-T2 relaxometry. Various types ofwater component were calculated by using multicomponent fits of the T2 relaxation curves. The experimental result showed that in potato tissue 80-82% water exist in intracellular space; 10-13% water in intercellular space and only 4-6% water exist in the cell wall space. In eggplant tissue, 90-93% water in intracellular space, 4-6% water exists in intercellular space and the remaining percentage of water is recognized as cell wall water. The investigated results quantify different types of water in plant-based food tissue. The highest proportion of water exists in intracellular spaces. Therefore, it is necessary to include different transport mechanism for intracellular, intercellular and cell wall water during modelling of heat and mass transfer during drying.

INTRODUCTION

Because of the high moisture content (about 80-90 %), foods have a great tendency to grow bacteria that reduce quality and ultimately damage the food. Food security is a major concern in large parts of the world with about one-third of the global food production, or 1.3 billion tons, lost annually due to a lack of proper processing [1]. Drying is a method of food preservation that inhibits the growth of bacteria, yeasts, and mould through the removal of water. Plant-based food materials are heterogeneous in structure, and their porous, amorphous and hygroscopic in nature [2] makes it complex to formulate a real understanding of heat and mass transfer during thermal processing. It is reported that most of the plant tissue contains about 80-90 % of water [3], which is distributed in three different environments namely, intercellular environments, intracellular spaces, and cell wall spaces.

Water residing in the intercellular space is known as capillary water or free water (FW). The water inside the cell is referred to as intracellular water and the cell wall water that occupies the fine space inside the cell wall, as shown in Fig. 1. These two types of water (intracellular water and cell wall water) are known as physically bound water or simply bound water [4]. Based on its mobility, bound water present inside the cells (intracellular water) is referred to as loosely bound water (LBW) while cell wall water is termed strongly bound water (SBW) [5]. The plant-based food materials that are subjected to drying processes can be treated as hygroscopic, porous and amorphous media with multiphase transport of heat and mass [6]. Most of the existing food drying models consider all water inside the

7th BSME International Conference on Thermal EngineeringAIP Conf. Proc. 1851, 020046-1–020046-10; doi: 10.1063/1.4984675

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food material as transportable i.e. bulk water. From Figure 1(b), it can be seen that the current models consider three phases inside food structure: water, gas (vapour and air), and solid matrix [7-14]. During this type of modelling, all the water is considered as intercellular (free water); therefore, it considers that there is no water inside the solid matrix. Due to the hygroscopic nature of plant-based food materials, bound water transport has a great effect on material shrinkage during food drying. Joardder et al. [15] suggested that migration of free water has no effect on material structure whereas; migration of LBW contributes to cellular shrinkage, pore formation and collapse of the cell. Furthermore, overall food tissue is deformed due to the migration of SBW. Whereas, Prothon et al. [16] showed that migration of three types of water causes overall tissue shrinkage, cellular shrinkage as well as structure collapse.

Therefore, without considering bound water transport, a food drying model cannot provide realistic understanding of the heat and mass transfer mechanism as well as material deformation (shrinkage) during drying.

Figure 1. (a) Actual food matrix (b) Domain that is considered in existing literature [8, 10, 11, 21-23]

Very few researchers have attempted to investigate the proportion of different water environments in the plant-based food material [17-20]. However, the literature has not described the proportion of the other two types of water environment. Therefore, it is necessary to measure the accurate proportion of FW, LBW and SBW in plant-based food material for developing accurate heat and mass transfer model.

There are many methods for measuring the different types of water, including differential scanning calorimetry (DSC), differential thermal analysis (DTA), dilatometry, bioelectric impedance analysis (BIA), nuclear magnetic resonance (NMR), etc. Among all the methods, proton nuclear magnetic resonance (1H-NMR) relaxometry studies have proven to be valuable in the study of plants and plant-based food materials submitted to stress reflecting anatomical details of the entire tissue and the water status in particular [24, 25]. The water exchange rates between the different compartments are controlled by the water proton relaxation behaviour T2 that strongly depends on the water mobility in the microscopic environment of the tissue and the strength of the applied magnetic field. The spin-spin T2 relaxation is the transverse component of the magnetization vector, which exponentially decays towards its equilibrium value after excitation by radio frequency energy. It can be expressed as

)/exp()( 21i

ini TtAtM (1)

Where, M(t) is the function of relaxation time, A is the relative contributions of sets of protons, T2 is the relaxation time of water proton, and i is the number of contributing component.

Many studies used this technique for investigating differences in tissue composition. For instance, examining free and bound water in animal tissue for lung [26, 27], brain [28-32], liver [33, 34], red blood cells [35]. In the case of plant-based food material, T2 relaxation theories have been applied to the investigation of different things such as sugar content in fruit tissue [36], quality of fruits and vegetables [37, 38], and the maturity of fruits and vegetables [39, 40]. The literature for measuring free and bound water using T2 relaxometry in plant-based food materials is very rare. Most of the NMR experiments have used T2 relaxometry to study the development of the water core [41-

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43], internal browning [41, 44, 45] and microstructural heterogeneity [46, 47] in apple tissue. However, these studies have not investigated the proportion of FW, LBW and SBW.

Therefore, the aim of this study is to investigate the proportion or percentage of three types of water namely free water, LBW (intracellular water), and SBW (cell wall water) for two different fruit tissues 1H-NMR method that can be used for developing accurate food processing model.

MATERIALS & METHODS

Sample Preparation

Experiments were performed for two different materials, one is highly porous (Eggplant) and another is very low porous (Potato). The samples were collected from the local market in Brisbane, Australia. Samples were stored in a refrigerator at 4oC prior to NMR measurement. At the start of each experiment, the materials were washed and cut into cylindrical shapes of 30 mm length and 10 mm radius and then weighed on digital electronic balance (model BB3000; Mettler-Toledo AG, Grefensee, Switzerland) with an accuracy of 0.01 g.

NMR Measurement

The prepared samples were then placed in a 25 mm diameter NMR tubes, as shown in Fig. 2, and then sealed with a standard NMR tube cap and incubated at 22°C for 5 min to reach thermal equilibrium. Measurement were made with a Bruker DRX wide-bore spectrometer (Bruker Bio-spin, Karlsruhe, Germany) operating at 300 MHz for hydrogen with a micro-imaging (micro 120) gradient set was used for measuring the spatial T2 relaxation times. Data were collected using Paravision 4 softwaer (Bruker). T2 relaxation times were measured with a Multi-Slice-Multi-Echo (MSME) sequence using the following acquisition parameters: 64 averages, 1000 echoes with 10 ms echo time and 5.0 sec repetition time. The slice thickness and the matrix size were 3 mm and 64, respectively. For each sample, two ROIs (regions-of-interest) were defined manually from the MSME images. The mean value of these ROIs was computed for all the images. A third ROI was assessed outside the sample for determining the signal-to-noise ratio. Noisy T2-signals were eliminated from the original signal. The data were transferred to a personal computer for storage and further analysis. The details experimental procedure can be found to the authors previous publication [48].

Figure 2. The schematic of the methodology NMR T2 relaxometry

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Mathematical Analysis

The water mobility in different plant-based food tissue was investigated by assessing the water fractions quantitatively using multicomponent analysis of the T2 relaxation decay curves. A nonlinear least-squares method was applied for data analysis [49]. Generally, the free induction decay of the proton relaxation process follows an exponential decay curve. A multi-exponential equation can describe this function. Each tissue compartment corresponding to a different environment will have a distinct relaxation time constant (T2). We assumed that these compartments were not inter-reliant at the time of the measurements. The multi-exponential nature of the T2 decay curve relates to the different water compartments in the tissue, the protons of water molecules do not undergo rapid exchange. However, when water proton relaxation follows a pattern of mono-exponential decay, there is a fast exchange of protons between tissue water and macromolecules, showing that the water compartments are symbiotic [50, 51].

In this study, we applied Tri-exponential fitting of T2 relaxation time curve, as shown in Fig. 3 for estimation of the free water (FW), LBW and SBW in the plant-based food material. Three components of the T2 relaxation curve were derived from the following expression:

32

22

12 /

3

/

2

/

1 expexpexp TtTtTt AAAY (2)

Where, Y is the function of T2 relaxation time constant, A1, A2 and A3 represent the relative contributions of the three proton environments, 1

2T , 22T and, 3

2T are the relaxation time constants of the different components. The multicomponent T2 relaxation curve was fitted with user defined MATLAB code (MathWorks, Natick, MA). The experiments were replicated three times for each sample, and the average of the relative contribution was taken for analysing the results.

Statistical Analysis

Data are expressed as mean ±SD of the mean. For statistical analysis, least-squares linear regression analysis was used where the P value was.

RESULTS & DISCUSSION

In this study, the bound water of two different plant-based food materials was investigated using H1-NMR relaxometry. The T2 relaxation decay curve as shown in Fig. 3 was fitted by the tri-exponential decay equation (Eqn. 2). After fitting the tri-exponential decay curve with the different T2 relaxation intensity data, three different proton components (slow, medium and fast) of water were described in Table 1, with their corresponding standard deviation and goodness of fit.

Table 1. The different T2 component of selected vegetables

Material Slow component Medium component Fast component Goodness of fit (R2)

T2 (ms) % T2 (ms) % T2 (ms) %Potato 141.0 11.2 81.3 3.8 35.9 9.6 12.9 2.5 7.3 2.6 5.8 2.1 0.9984

Eggplant 102.4 12.7 91.3 4.3 30.5 6.9 5.5 2.2 6.3 2.1 3.2 1.6 0.9988

Three types of signal component namely, slow, medium and fast were categorized as LBW, FW, and SBW respectively. This categorization is mainly based on the pore size and water mobility within the sample [52]. The water mobility is defined as the ability of water molecules to rotate freely, as well as moving spatially with the result that T2 is inversely proportional to rotational motion (which will be a reflection of inherent mobility). It is reported that the water is in a restrictive environment shows fast relaxation times [53-54]. In plant-based food material, the

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cell wall is mostly composed of solid matter with very low water content. This structure acts to restrict translational motion. Therefore, the fast T2 component is most likely to relate to cell wall water (SBW). Furthermore, it has been stated that the majority of water is present in intracellular spaces that act as a water reservoir with a predominantly water-water interface, where the water proton relax slowly, resulting in a longer T2 relaxation. Among the three T2 components fitted, the longest component had the greater water fraction. Based on the water content of the sample, it can be related to the intracellular water (LBW). The remaining component (medium) then relates to the free water (FW).

Figure 3. T2 Relaxation decay curve for Eggplant and Potato

-2000000

-1000000

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

8000000

9000000

10000000

11000000

12000000

0 50 100 150 200 250 300 350 400 450 500 550 600 650

Inte

nsity

Echo time (ms)

EggplantPotato

Figure 4. The percentage of different types of water in plant-based food material

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Figure 4 shows a comparison of the percentage of different types of water between two different food materials. Depending on the type of food and its cell structure about 82-92% LBW, 3-6% SBW and 6-13 % FW was observed. Due to diversified food properties, cell structure orientation and the solute content, the proportion of SBW, LBW and FW differs for various plant-based food materials.

It can also be seen from the illustration that between the two different materials, eggplant contains the highest amount of LBW (about 92%). This result shows a good agreement with study of Halder and Datta [20]; they found about 96% water of eggplant tissue exists in the intracellular space.

On the other hand, the SBW of potato is higher than that of eggplant. This may cause solely due to its high proportion of solid material content, as shown in Fig. 5. The SBW of both of potato and eggplant is proportional to the amount of solid.

Figure 5: The relationship between SBW and percentage of solid for highly and low porous material

Figure 6: Effect of porosity on free water (results are mean SD)

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Figures 6 show the effect of initial porosity on FW. It can be seen that the percentage of free water shows an inverse relationship with the percentage of porosity. In hygroscopic porous material, air and water mutually stay within intercellular spaces. Highly porous material means it contains a higher amount of air inside intercellular space. From Figure 6, it can be observed that potato contains the highest amount of free water because of its least amount of porosity. On the other hand, eggplant with the greatest porosity contains the least amount of free water.

CONCLUSION

In this study, the different proportion of free water, loosely bound water and strongly bound water in two different plant-based food materials were observed using 1H NMR T2 relaxometry. The experimental results show that based on multiple component T2 relaxometry data, the proportion of FW, LBW, and SBW present in plant-based food materials is about 6-13 %, 82-92%, and 3-6% respectively. Between the two different materials, eggplant showed the highest fraction of LBW. Depending on the solid portion and porosity of those two materials tissue, SBW and FW both were found highest in potato. This study also confirmed that there is a significant effect of porosity on the free water measured. The findings of this study enhance the understanding of plant-based food tissue that may contribute to a better understanding of potential changes occurring during food processing and may contribute to developing accurate heat and mass transfer models and prediction of deformation.

ACKNOWLEDGEMENT

The authors are sincerely grateful to the Queensland University of Technology, Australia for funding a QUTPRA scholarship and HDR tuition fee sponsorship, which has enabled the conduct of this research. The first author is also sincerely grateful to the Dhaka University of Engineering & Technology, Gazipur, Bangladesh for providing the deputation leave

REFERENCES

1. J. Gustavsson, Cederberg, C., Sonesson, ULF, Global food losses and food waste-Extent, causes andprevention, Food and Agriculture organization of United nations, Rome, (2011).

2. M. I. H. Khan, M. U. H. Joardder, C. Kumar, M. A. Karim, Multiphase porous media modelling: A novelapproach of predicting food processing performance. Critical Reviews in Food Science and Nutrition, (2016).doi.org/10.1080/10408398.2016.1197881

3. M. I. H. Khan, C. Kumar, M. U. H Joardder, M. A. Karim, Determination of appropriate effective diffusivityduring drying. Drying Technology 35, 335-346 (2017).

4. M. Karel, D. B. Lund, Physical principles of food preservation: revised and expanded (Vol. 129). CRC Press.New York, (2003)

5. M. Caurie, Bound water: its definition, estimation, and characteristics. International Journal of Food Science &Technology, 46, 930-934 (2011).

6. J. Srikiatden, J. S. Roberts, Moisture Transfer in Solid Food Materials: A Review of Mechanisms, Models, andMeasurements. International Journal of Food Properties, 10, 739-777 (2007).

7. C. Kumar, Modelling intermittent microwave convective (IMCD) drying of food materials. PhD Thesis,Queensland University of Technology, Australia, (2015).

8. A. K. Datta, Porous media approaches to studying simultaneous heat and mass transfer in food processes. I:Problem formulations. Journal of Food Engineering, 80, 80-95 (2007).

020046-7

9. A. Halder, A. K. Datta, Surface heat and mass transfer coefficients for multiphase porous media transportmodels with rapid evaporation. Food and Bioproducts Processing, 90, (2012) 475-490.

10. A.H. Feyissa, K.V. Gernaey, J. Adler-Nissen, 3D modelling of coupled mass and heat transfer of a convection-oven roasting process. Meat Science, 93 (2013) 810-820.

11. T. Gulati, A.K. Datta, Mechanistic understanding of case-hardening and texture development during drying offood materials. Journal of Food Engineering 166, 119-138 (2015).

12. S. Chemkhi, W. Jomaa & F. Zagrouba, Application of a Coupled Thermo-Hydro-Mechanical Model toSimulate the Drying of Nonsaturated Porous Media. Drying technology 27, 842-850 (2009).

13. R. Yamsaengsung, R. G. Moreira, Modeling the transport phenomena and structural changes during deep fatfrying: Part I: model development. Journal of Food Engineering, 53, 1-10, (2002).

14. R.G.M. van der Sman, Modeling cooking of chicken meat in industrial tunnel ovens with the Flory–Rehnertheory. Meat Science, 95 (2013) 940-957.

15. M. U. H. Joardder, A. Karim, R. J. Brown, C. Kumar, Porosity : Establishing the Relationship between DryingParameters and Dried Food Quality, Springer, 2015.

16. F. Prothon, L. Ahrne, I. Sjoholm, Mechanisms and prevention of plant tissue collapse during dehydration: acritical review. Crit Rev Food Sci Nutr, 43 447-479, (2003).

17. D.W.S. José Miguel Aguilera, Simultanious heat and mass transfer: Dehydration, Aspen Publishers, Inc, 2000.18. K. O. Honikel & R. Hamm, Measurement of Water-Holding Capacity and Juiciness, Quality Attributes in

Meat, Poultry and Fish Products. Blackie Academic and Professional Publication, London, 1994.19. N. PS., Cells and Diffusion, 4th edition ed., CA: Elsevier, Inc., San Diego, 2009.20. A. Halder, A. K. Datta, R. M. Spanswick, Water transport in cellular tissues during thermal processing. AIChE

Journal, 57, 2574-2588 (2011).21. S. Mercier, B. Marcos, C. Moresoli, M. Mondor, S. Villeneuve, Modeling of internal moisture transport during

durum wheat pasta drying. Journal of Food Engineering 124, 19-27 (2014).22. C. Kumar, M. U. H. Joardder, T. W. Farrell and M. A. Karim, Multiphase porous media model for Intermittent

microwave convective drying (IMCD) of food. International Journal of Thermal Sciences 104, 304–314(2016).

23. A. D. Halder, A. K. Datta, An Improved, Easily Implementable, Porous Media Based Model for Deep-FatFrying: Part I: Model Development and Input Parameters. Food and Bioproducts Processing 85, 209-219(2007).

24. L. Van Der Weerd, M. M. A. E. Claessens, C. Efdé, H. Van As, Nuclear magnetic resonance imaging ofmembrane permeability changes in plants during osmotic stress. Plant, Cell & Environment, 25, 1539-1549(2002).

25. P. N. Gambhir, Y. J. Choi, D. C. Slaughter, J. F. Thompson, M. J. McCarthy, Proton spin–spin relaxation timeof peel and flesh of navel orange varieties exposed to freezing temperature. Journal of the Science of Food andAgriculture 85, 2482-2486 (2005).

26. G. Sedin, P. Bogner, E. Berenyi, I. Repa, Z. Nyul, E. Sulyok, Lung Water and Proton Magnetic ResonanceRelaxation in Preterm and Term Rabbit Pups: Their Relation to Tissue Hyaluronan, Pediatric Research 48,554-559 (2000).

27. A. G. Cutillo, A. H. Morris, D. C. Ailion, C. H. Durney, K. Ganesan, Determination of Lung Water Contentand Distribution by Nuclear Magnetic Resonance, in: E. Rügheimer (Ed.) New Aspects on Respiratory Failure,Springer Berlin Heidelberg, Berlin, Heidelberg, 1992, pp. 138-146.

28. E. Sulyok, Z. Nyúl, P. Bogner, E. Berényi, I. Repa, Z. Vajda, T. Dóczi, G. Sedin, Brain Water and ProtonMagnetic Resonance Relaxation in Preterm and Term Rabbit Pups: Their Relation to Tissue Hyaluronan.Neonatology 79, 67-72 (2001).

29. E. Berenyi, I. Repa, P. Bogner, T. Doczi, E. Sulyok, Water content and proton magnetic resonance relaxationtimes of the brain in newborn rabbits. Pediatric Research 43, 421-425 (1998).

30. Z. Vajda, E. Berenyi, P. Bogner, I. Repa, T. Doczi, E. Sulyok, Brain adaptation to water loading in rabbits asassessed by NMR relaxometry. Pediatric Research, 46, 450-454 (1999).

31. S. Inao, H. Kuchiwaki, N. Hirai, S. Takada, N. Kageyama, M. Furuse, T. Gonda, Dynamics of Tissue WaterContent, Free and Bound Components, Associated with Ischemic Brain Edema, in: Y. Inaba, I. Klatzo, M.Spatz (Eds.) Brain Edema: Proceedings of the Sixth International Symposium, November 7–10, 1984 inTokyo, Springer Berlin Heidelberg, Berlin, Heidelberg, 1985, pp. 360-366.

020046-8

32. M. Furuse, T. Gonda, H. Kuchiwaki, N. Hirai, S. Inao, N. Kageyama, Thermal Analysis on the State of Freeand Bound Water in Normal and Edematous Brains, in: K.G. Go, A. Baethmann (Eds.) Recent Progress in theStudy and Therapy of Brain Edema, Springer US, Boston, MA, 1984, pp. 293-298.

33. E. Moser, P. Holzmueller, M. Krssak, Improved estimation of tissue hydration and bound water fraction in ratliver tissue. Magnetic Resonance Materials in Physics, Biology and Medicine 4, 55-59 (1996).

34. E. Moser, P. Holzmueller, G. Gomiscek, Liver tissue characterization by in vitro NMR: tissue handling andbiological variation. Magnetic resonance in medicine 24, 213-220 (1992).

35. J. A. Besson, D. N. Wheatley, E. R. Skinner, M. A. Foster, 1H-NMR relaxation times and water content of redblood cells from chronic alcoholic patients during withdrawal. Magn Reson Imaging 7, 289-291 (1989).

36. T. Delgado-Goni, S. Campo, J. Martin-Sitjar, M. E. Cabanas, B. San Segundo, C. Arus, Assessment of a 1Hhigh-resolution magic angle spinning NMR spectroscopy procedure for free sugars quantification in intactplant tissue. Planta, 238, 397-413 (2013).

37. F. Van de Velde, M. H. Grace, D. Esposito, M. É. Pirovani, M. A. Lila, Quantitative comparison ofphytochemical profile, antioxidant, and anti-inflammatory properties of blackberry fruits adapted to Argentin.Journal of Food Composition and Analysis 47, 82-91 (2016).

38. P. Chen, M. J. McCarthy & R. Kauten, NMR for Internal Quality Evaluation of Fruits and Vegetables.Transactions of the ASAE 32, 1747-1753 (1989).

39. R. R. Ruan, P. L. Chen, S. Almaer, Nondestructive Analysis of Sweet Corn Maturity Using NMR.HortScience, 34, 319-321 (1999).

40. P. Chen, M. J. McCarthy, R. Kauten, Y. Sarig, S. Han, Maturity Evaluation of Avocados by NMR Methods.Journal of Agricultural Engineering Research 55, 177-187 (1993).

41. B. K. Cho, W. Chayaprasert, R. L. Stroshine, Effects of internal browning and watercore on low field (5.4MHz) proton magnetic resonance measurements of T2 values of whole apples. Postharvest Biology andTechnology 47, 81-89 (2008).

42. A. Melado-Herreros, M.A. Muñoz-García, A. Blanco, J. Val, M. E. Fernández-Valle, P. Barreiro, Assessmentof watercore development in apples with MRI: Effect of fruit location in the canopy. Postharvest Biology andTechnology 86, 125-133 (2013).

43. C. J. Clark, J. S. MacFall, R. L. Bieleski, Loss of watercore from `Fuji' apple observed by magnetic resonanceimaging. Scientia Horticulture 73, 213-227(1998).

44. C. J. Clark, D. M. Burmeister, Magnetic resonance imaging of browning development in 'Braeburn' appleduring controlled-atmosphere storage under high CO2. Hort. Science 34, 915-919 (1999).

45. J. J. Gonzalez, R. C. Valle, S. Bobroff, W. V. Biasi, E. J. Mitcham, M. J. McCarthy, Detection and monitoringof internal browning development in ‘Fuji’ apples using MRI. Postharvest Biology and Technology 22, 179-188 (2001).

46. G. Winisdorffer, M. Musse, S. Quellec, M. F. Devaux, M. Lahaye, F. Mariette, MRI investigation ofsubcellular water compartmentalization and gas distribution in apples. Magnetic Resonance Imaging 33, 671-680 (2015).

47. T. Defraeye, V. Lehmann, D. Gross, C. Holat, E. Herremans, P. Verboven, B.E. Verlinden, B.M. Nicolai,Application of MRI for tissue characterisation of ‘Braeburn’ apple. Postharvest Biology and Technology 75,96-105 (2013).

48. M. I. H. Khan, R. M. Wellard, S. A. Nagy, M. U. H. Joardder, M. A. Karim, Investigation of bound and freewater in plant-based food material using NMR T2 relaxometry, Innovative Food Science & EmergingTechnologies 38, 252-261 (2016).

49. R. V. Mulkern, A. R. Bleier, I. K. Adzamli, R. G. Spencer, T. Sandor, F.A. Jolesz, Two-site exchangerevisited: a new method for extracting exchange parameters in biological systems. Biophysical Journal 55, 221-232 (1989).

50. W. C. Cole, A. D. LeBlanc, S. G. Jhingran, The origin of biexponential T2 relaxation in muscle water.Magnetic resonance in medicine 29, 19-24 (1993).

51. S. Shioya, M. Haida, M. Fukuzaki, Y. Ono, M. Tsuda, Y. Ohta, H. Yamabayashi, A 1-year time course studyof the relaxation times and histology for irradiated rat lungs. Magnetic resonance in medicine 14, 358-368(1990).

52. A. Saeedi, Experimental Study of Multiphase Flow in Porous Media during CO2 Geo-Sequestration Processes,Springer Science & Business Media, 2012.

53. C. A. M. Wheeler-Kingshott, G. J. Barker, S. C. A. Steens, M. A. van Buchem, D: The Diffusion of Water, in:Quantitative MRI of the Brain, John Wiley & Sons, Ltd, 2004, pp. 203-256.

020046-9

54. M. I. H. Khan, R. M. Wellard, N. D. Pham & M. A. Karim, Investigation of cellular level of water in plant-based food material. The 20th International Drying Symposium (IDS 2016), At Gifu, Japan, (2016).

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