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Development of a Novel Image Analysis Technique to Detect the Moisture Diffusion of Soybeans [Glycine max (L.)] During Rehydration Using a Mass Transfer Simulation Model

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Abstract

The change in the moisture content of soybeans during a rehydration process at 25 °C was investigated. Peleg’s equation was suitable for describing the soaking characteristics of the soybean with a R2 value of 0.98. The soaking time to achieve the target moisture content of soybeans (33.33%) was estimated to be 14.59 min by the Peleg model. The mass transfer coefficient (k) for the mass transfer simulation was determined with two calculation steps using the Omoto model and the simulation models. The most suitable k value for the simulation was determined to be 6.0 × 10−7 m2/s, which is higher than the apparent k value obtained from the Omoto model. The diffusion simulation for the internal diffusion of the soybean after soaking was conducted for 180 min and the cross-section of soybeans was analyzed using an image processing technique during the diffusion process. The image analysis detected a moving layer during the diffusion process and the moisture content at the layer was determined to be 32.08% (± 0.34) based on the results of the simulation model.

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References

  • Abu-Ghannam, N., & McKenna, B. (1997). Hydration kinetics of red kidney beans. Journal of Food Science, 62(3), 520–523.

    Article  CAS  Google Scholar 

  • AOAC (1990). Official methods of analysis. Association of Official Analytical Chemists, Washington, DC, U.S.A.

  • Bayram, M., Kaya, A., & Öner, M. D. (2004). Changes in properties of soaking water during production of soy-bulgur. Journal of Food Engineering, 61(2), 221–230.

    Article  Google Scholar 

  • Chen, L., Song, D. S., Tian, Y., Lan, D., Yu, A., & Zhang, H. (2008). Application of on-line microwave sample-preparation techniques. TrAC Trends in Analytical Chemistry, 27(2), 151–159.

    Article  CAS  Google Scholar 

  • Coutinho, M. R., Conceicao, W. A. S., Paraiso, P. R., Andrade, C. M. G., Omoto, E. S., Jorge, R. M. M., Filho, R. M., & Jorge, L. M. M. (2010). Application of the Hsu model to soybean grain hydration. Food Science and Technology, 30(1), 19–29.

    Article  Google Scholar 

  • Deshpande, S. D., Bal, S., & Ojha, T. P. (1994). A study on diffusion of water by the soybean grain during cold water soaking. Journal of Food Engineering, 23(1), 121–127.

    Article  Google Scholar 

  • Du, C. J., & Sun, D. W. (2004). Shape extraction and classification of pizza base using computer vision. Journal of Food Engineering, 64(4), 489–496.

    Article  Google Scholar 

  • Hussain, M. M., & Dincer, I. (2003). Two-dimensional heat and moisture transfer analysis of a cylindrical moist object subjected to drying: A finite-difference approach. International Journal of Heat and Mass Transfer, 46(21), 4033–4039.

    Article  CAS  Google Scholar 

  • Jideani, V. A., & Mpotokwana, S. M. (2009). Modeling of water absorption of Botswana Bambara varieties using Peleg’s equation. Journal of Food Engineering, 92(2), 182–188.

    Article  Google Scholar 

  • Kaushik, G., Satya, S., & Naik, S. N. (2010). Effect of domestic processing techniques on the nutritional quality of the soybean. Mediterranean Journal of Nutrition and Metabolism, 3(1), 39–46.

    Article  Google Scholar 

  • Khazaei, N. B., Tavakoli, T., Ghassemian, H., Khoshtaghaza, M. H., & Banakar, A. (2013). Applied machine vision and artificial neural network for modeling and controlling of the grape drying process. Computers and Electronics in Agriculture, 98, 205–213.

    Article  Google Scholar 

  • Li, N., Wu, L., Nian, L., Song, Y., Lei, L., Yang, X., Wang, K., Wang, Z., Zhang, L., Zhang, H., Yu, A., & Zhang, Z. (2015). Dynamic microwave assisted extraction coupled with dispersive micro-solid-phase extraction of herbicides in soybeans. Talanta, 1421(1), 43–50.

    Article  CAS  Google Scholar 

  • Lima, F. S., Kurozawa, L. E., & Ida, E. L. (2014). The effects of soybean soaking on grain properties and isoflavones loss. LWT-Food Science and Technology, 59(2), 1274–1282.

    Article  CAS  Google Scholar 

  • Marques, B. C., Jorge, L. M. M., & Jorge, R. M. M. (2016). Chemical properties and water absorption kinetics of transgenic corn grain (2B587 Hx) and its conventional isoline (2B587). Journal of Cereal Science, 71, 93–98.

    Article  CAS  Google Scholar 

  • Mirade, P. S. (2008). Computational fluid dynamics (CFD) modelling applied to the ripening of fermented food products: Basics and advances. Trends in Food Science & Technology, 19(9), 472–481.

    Article  CAS  Google Scholar 

  • Montanuci, F. D., Perussello, C. A., Jorge, L. M. M., & Jorge, R. M. M. (2014). Experimental analysis and finite element simulation of the hydration process of barley grains. Journal of Food Engineering, 131, 44–49.

    Article  CAS  Google Scholar 

  • Niamnuy, C., Nachaisin, M., Poomsa-ad, N., & Devahastin, S. (2012). Kinetic modelling of drying and conversion/degradation of isoflavones during infrared drying of soybean. Food Chemistry, 133(3), 946–952.

    Article  CAS  Google Scholar 

  • Norton, T., & Sun, D. (2006). Computational fluid dynamics (CFD)—An effective and efficient design and analysis tool for the food industry: A review. Trends in Food Science & Technology, 17(11), 600–620.

    Article  CAS  Google Scholar 

  • Omoto, E. S., Andrade, C. M. G., Jorge, R. M. M., Coutinho, M. R., Paraíso, P. R., & Jorge, L. M. M. (2009). Mathematical modeling and analysis of pea grains hydration. Food Science and Technology, 29(1), 12–18.

    Article  Google Scholar 

  • Pan, Z., & Tangratanavalee, W. (2003). Characteristics of soybeans as affected by soaking conditions. LWT-Food Science and Technology, 36(1), 143–151.

    Article  CAS  Google Scholar 

  • Park, H. W., Han, W. Y., & Yoon, W. B. (2015). Effect of grain size and drying temperature on drying characteristics of soybean (Glycine max) using hot air drying. Journal of the Korean Society of Food Science and Nutrition, 44(11), 1700–1707.

    Article  CAS  Google Scholar 

  • Peleg, M. (1988). An empirical model for description of moisture sorption curves. Journal of Food Science, 41, 57–72.

    Google Scholar 

  • Piergiovanni, A. R. (2011). Kinetic of water adsorption in common bean: Considerations on the suitability of Peleg’s model for describing bean hydration. Journal of Food Processing and Preservation, 35(4), 447–452.

    Article  Google Scholar 

  • Pramiu, P. V., Rizzi, R. L., Prado, N. V., Coelho, S. R. M., & Bassinello, P. Z. (2015). Numerical modeling of chickpea (Cicer arietinum) hydration: The effects of temperature and low pressure. Journal of Food Engineering, 165, 112–123.

    Article  Google Scholar 

  • Rafiee, S., Keyhani, A., & Mohammadi, A. (2008). Soybean seeds mass transfer simulation during drying using finite element method. World Applied Sciences Journal, 4(2), 284–288.

    Google Scholar 

  • Rafiq, A., Chowdhary, J., Hazarika, M. K., & Makroo, H. A. (2015). Temperature dependence on hydration kinetic model parameters during rehydration of parboiled rice. Journal of Food Science and Technology, 52(9), 6090–6094.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Seo, J., & Lee, S. (2004). Production of fibrinolytic enzyme from soybean grits fermented by Bacillus firmus NA-1. Journal of Medicinal Food, 7(4), 442–449.

    Article  CAS  PubMed  Google Scholar 

  • Seyhan-Gürtaş, F., Mehmet, A. K., & Evranuz, Ö. E. (2001). Water diffusion coefficients of selected legumes grown in Turkey as affected by temperature and variety. Turkish Journal of Agriculture and Forestry, 25(5), 297–304.

    Google Scholar 

  • Shafaei, S. M., Masoumi, A. A., & Roshan, H. (2016). Analysis of water absorption of bean and chickpea during soaking using Peleg model. Journal of the Saudi Society of Agricultural Sciences, 15(2), 135–144.

    Article  Google Scholar 

  • Shei, H., & Lin, C. (2012). An optical automatic measurement method for the moisture content of rough rice using image processing techniques. Computers and Electronics in Agriculture, 85, 134–139.

    Article  Google Scholar 

  • Sopade, P. A., & Obekpa, J. A. (1990). Modeling water absorption in soybean, cowpea and peanuuts at three temperature using Peleg’s equation. Journal of Food Science, 55(4), 1084–1087.

    Article  Google Scholar 

  • Sopade, P. A., Ajisegiri, E. S., & Badau, M. H. (1992). The use of Peleg’s equation to model water absorption in some cereal grains during soaking. Journal of Food Engineering, 15(4), 269–283.

    Article  Google Scholar 

  • Suarez, C., Viollaz, P., & Chirife, J. (1980). Kinetics of soybean drying. Transaction of the International Conference on Structural Mechanics in Reactor Technology, 2, 251–255.

  • Szczypiński, P. M., & Zapotoczny, P. (2012). Computer vision algorithm for barley kernel identification, orientation estimation and surface structure assessment. Computers and Electronics in Agriculture, 87, 32–38.

    Article  Google Scholar 

  • Tahir, A. R., Neethirajan, S., Jayas, D. S., Shahin, M. A., Symons, S. J., & White, N. D. G. (2007). Evaluation of the effect of moisture content on cereal grains by digital image analysis. Food Research International, 40(9), 1140–1145.

    Article  CAS  Google Scholar 

  • Turhan, M., Sayar, S., & Gunasekaran, S. (2002). Application of Peleg model to study water absorption in chickpea during soaking. Journal of Food Engineering, 53(2), 153–159.

    Article  Google Scholar 

  • Vincent, R. K., & Hunt, G. R. (1968). Infrared reflectance from mat surface. Applied Optics, 7(1), 53–59.

    Article  CAS  PubMed  Google Scholar 

  • Vishwanathan, K. H., Singh, V., & Subramanian, R. (2011). Wet grinding characteristics of soybean for soymilk extraction. Journal of Food Engineering, 106(1), 28–34.

    Article  Google Scholar 

  • Wang, S. H., & Toledo, M. C. F. (1987). Inactivation of soybean lipoxygenase by microwave heating: Effect of moisture content and exposure time. Journal of Food Science, 52(5), 1344–1347.

    Article  CAS  Google Scholar 

  • Wang, C., Duan, H., & Teng, J. (2014). Assessment of microwave cooking on the bioaccessibility of cadmium from various food matrices using an in vitro digestion model. Biological Trance Element Research, 160(2), 276–284.

    Article  CAS  Google Scholar 

  • Yoshida, H., & Takagi, S. (1996). Vitamin E and oxidative stability of soya bean oil prepared with beans at various moisture contents roasted in a microwave oven. Journal of the Science of Food and Agriculture, 72(1), 111–119.

    Article  CAS  Google Scholar 

  • Yoshida, H., Takagi, S., Kajimoto, G., & Yamaguchi, M. (1997). Microwave roasting and phospholipids in soybeans (Glycine max. L.) at different moisture contents. Journal of the American Oil Chemists’ Society, 74(2), 117.

  • Yu, K., Zhao, Y., Li, X., Shao, Y., Zhu, F., & He, Y. (2014). Identification of crack features in fresh jujube using Vis/NIR hyperspectral imaging combined with image processing. Computers and Electronics in Agriculture, 103, 1–10.

    Article  Google Scholar 

  • Zilic, S., Mogol, B., Akillioglu, G., Serpen, A., Delic, N., & Gokmen, V. (2013). Effects of extrusion, infrared and microwave processing on Maillard reaction products and phenolic compounds in soybean. Journal of the Science of Food and Agriculture, 94(1), 45–51.

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

This work was carried out with the support of “Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ012544)” Rural Development Administration, Republic of Korea. This study has been worked with the support of a research grant of Kangwon National Univ. in 2016.

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Authors and Affiliations

  1. Department of Food Science and Biotechnology, College of Agricultural and Life Science, Kangwon National University, Chuncheon, Gangwon, 23431, South Korea

    Hyeon Woo Park & Won Byong Yoon

  2. Department of Food Science and Biotechnology, College of Agricultural and Life Science, Kangwon National University, Chuncheon, Gangwon, 200-701, South Korea

    Won Byong Yoon

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  1. Hyeon Woo Park
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Correspondence to Won Byong Yoon.

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Park, H.W., Yoon, W.B. Development of a Novel Image Analysis Technique to Detect the Moisture Diffusion of Soybeans [Glycine max (L.)] During Rehydration Using a Mass Transfer Simulation Model. Food Bioprocess Technol 11, 1887–1894 (2018). https://doi.org/10.1007/s11947-018-2150-1

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