Volume 20, Issue 134 (2023)                   FSCT 2023, 20(134): 87-97 | Back to browse issues page


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Dalvi-Isfahan M. Hydration Modeling of Rice (Oryza Sativa) by Empirical and Diffusion Models. FSCT 2023; 20 (134) :87-97
URL: http://fsct.modares.ac.ir/article-7-67034-en.html
Assistant professor, Department of Food Science and Technology, Faculty of Agriculture, Jahrom University, Jahrom, Iran. TEL:07154344445. Email: mohsen.dalvi@gmail.com- dalvi@jahromu.ac.ir , mohsen.dalvi@gmail.com
Abstract:   (728 Views)
In this study, water absorption characteristics of white rice during soaking at 25-65 oC was investigated. In the next step, the efficiency of fundamental and empirical models to predict the moisture content of grain during soaking were evaluated and compared. The fundamental models were developed by using analytical and numerical solutions of Fick’s second law of diffusion based on regular shapes (cube and cylinder) and the real geometry of the white rice, respectively. Five empirical models (Henderson and Pabis model, exponential model, Page model, modified Page model and two-term exponential model) for explaining the soaking behavior of rice were also studied. The results of the studied models indicate that the numerical model were substantially more accurate than analytical model in describing the water absorption curves. The higher accuracy of numerical model can be attributed to the fact that this model selected appropriate shape to represent rice grains in the mathematical model. The average value of the effective water diffusivity at 25-65 oC was estimated to be in the order of 8.83×10-11 m2/s, by minimizing the error between experimental and numerically predicted results. Among the empirical models, the two-term exponential model was better than others in predicting changes in sample moisture during soaking. Overall, although both modeling approaches were able to predict the changes in moisture content of the sample during soaking, the numerical model was found to be more appropriate because it provided a more comprehensive understanding of the underlying physics of the process and the model parameters were directly related to measurable physical quantities. 
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Article Type: Original Research | Subject: food industry engineering
Received: 2023/01/24 | Accepted: 2023/02/26 | Published: 2023/04/7

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