Journal of Food and Bioprocess Engineering

Prediction of the changes in physicochemical properties of key lime juice during IR thermal processing by artificial neural networks

Document Type : Original research

Authors

1 Department of Food Materials and Process Design Engineering, Faculty of Food Science and Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

2 Department of Food Science, Kherad institute of higher education, Bushehr, Iran

Abstract

Thermal processing of the key lime juice leads to the inactivation of pectin methylesterase (PME) and the degradation of ascorbic acid (AA). These changes affect directly the cloud stability and color of the juice. In this study, an artificial neural network (ANN) model was applied for designing and developing an intelligent system for prediction of the thermal processing effects on the physicochemical properties of key lime juice during conventional and infrared (IR) heating. The inputs of this network were time and temperature and the outputs were changes in PME activity, AA content, browning index (BI) and also cloud stability of the juice.The feed-forward neural network with a logarithmic transfer function, Levenberg–Marquardt training algorithm and eight neurons in the hidden layer (topology 2-8-4) was chosen as the best ANN model (R2> 0.95, RMSE=0.47 and SE=0.28). The predicted values using the optimal ANN model vs. experimental values represented a correlation coefficient higher than 0.95 and 0.90 during IR and conventional thermal processing, respectively. This model can therefore be applied in prediction of the effects of thermal processing on the physicochemical properties of the lime juice in pilot plants, processing factories and online monitoring.

Keywords

Aghajanzadeh, S., Kashaninejad, M., & Ziaiifar, A. M. (2016). Effect of infrared heating on degradation kinetics of key lime juice physicochemical properties. Innovative Food Science & Emerging Technologies, 38, 139-148.
Bahmani, A., Jafari, S. M., Shahidi, S. A., & Dehnad, D. (2015). Mass transfer kinetics of eggplant during osmotic dehydration by neural networks. Journal of food processing and preservation, 40, 815-827.
Burdurlu, H. S., Koca, N., & Karadeniz, F. (2006). Degradation of vitamin C in citrus juice concentrates during storage. Journal of Food Engineering, 74(2), 211-216.
Camara, M., Torrecilla, J. S., Caceres, J. O., Sanchez Mata, M. C., & Fernandez-Ruiz, V. (2009). Neural network analysis of spectroscopic data of lycopene and β-carotene content in food samples compared to HPLC-UV-Vis. Journal of Agricultural and Food Chemistry, 58(1), 72-75.
Chen, C., Shaw, P., & Parish, M. (1993). Orange and tangerine juices. Fruit juice processing technology, 110-156.
Chen, C., & Wu, M. (1998). Kinetic models for thermal inactivation of multiple pectinesterases in citrus juices. Journal of Food Science, 63(5), 747-750.
Dolatabadi, Z., Rad, A. H. E., Farzaneh, V., Feizabad, S. H. A., Estiri, S. H., & Bakhshabadi, H. (2016). Modeling of the lycopene extraction from tomato pulps. Food Chemistry, 190, 968-973.
Kashyap, G., & Gautam, M. (2012). Analysis of vitamin c in commercial and naturals substances by iodometric titration found in nimar and malwa regeion. Journal of Scientific Research in Pharmacy, 1(2), 77-78.
Kimball, D. A. (1999). Citrus processing. A complete guide, 2nd edn. Aspen Publishers, Inc., aithersburg, Maryland, 257- 264.
Lee, & Coates. (1999). Thermal pasteurization effects on color of red grapefruit juices. Journal of Food Science, 64(4), 663-666.
Menlik, T., Kirmaci, V., & Usta, H. (2009). Modeling of freeze drying behaviors of strawberries by using artificial neural network. Journal of Thermal Science and Technology, 29(2), 11-21.
Nayak, S., Misra, B., & Behera, H. (2014). Impact of data normalization on stock index forecasting. International Journal of Computer Information Systems and Industrial Management Applications, 6, 257-269.
Polydera, A., Galanou, E., Stoforos, N., & Taoukis, P. (2004). Inactivation kinetics of pectin methylesterase of greek navel orange juice as a function of high hydrostatic pressure and temperature process conditions. Journal of Food Engineering, 62(3), 291-298.
Rai, P., Majumdar, G., DasGupta, S., & De, S. (2005). Prediction of the viscosity of clarified fruit juice using artificial neural network: a combined effect of concentration and temperature. Journal of Food Engineering, 68(4), 527-533.
Rastogi, N. K. (2012). Recent trends and developments in infrared heating in food processing. Critical reviews in food science and nutrition, 52(9), 737-760.
Ruan, R., Almaer, S., & Zhang, J. (1995). Prediction of dough rheological properties using neural networks. Cereal Chemistry, 72(3), 308-311.
Sablani, S. S., Baik, O.-D., & Marcotte, M. (2002). Neural networks for predicting thermal conductivity of bakery products. Journal of Food Engineering, 52(3), 299-304.
Sablani, S. S., & Rahman, M. S. (2003). Using neural networks to predict thermal conductivity of food as a function of moisture content, temperature and apparent porosity. Food Research International, 36(6), 617-623.
Snir, R., Koehler, P., Sims, K., & Wicker, L. (1996). Total and thermostable pectinesterases in citrus juices. Journal of Food Science, 61(2), 379-382.
Tripathy, P., & Kumar, S. (2009). Neural network approach for food temperature prediction during solar drying. International Journal of Thermal Sciences, 48(7), 1452-1459.
Versteeg, C., Rombouts, F., Spaansen, C., & Pilnik, W. (1980). Thermostability and orange juice cloud destabilizing properties of multiple pectinesterases from orange. Journal of Food Science, 45(4), 969-971.
Zheng, H., Fang, S., Lou, H., Chen, Y., Jiang, L., & Lu, H. (2011). Neural network prediction of ascorbic acid degradation in green asparagus during thermal treatments. Expert Systems with Applications, 38(5), 5591-5602.
Ziena, H. (2000). Quality attributes of Bearss Seedless lime (Citrus latifolia Tan) juice during storage. Food Chemistry, 71(2), 167-172.
Journal of Food and Bioprocess Engineering
Volume 3, Issue 2
December 2020
Pages 95-100