Journal
FOOD CHEMISTRY
Volume 339, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2020.127862
Keywords
Enzymatic extraction; Artificial neural network; Genetic algorithm; Modeling; Optimization
Funding
- Science and Engineering Research Board (SERB), Department of Science and Technology, New Delhi, India [EMR/2016/004925]
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In this study, the pectinase-assisted extraction of cashew apple juice was modeled and optimized using a multi-layer artificial neural network coupled with genetic algorithm. The experimental results showed a good agreement between the predicted values and the actual values.
In this study, pectinase-assisted extraction of cashew apple juice was modeled and optimized using a multi-layer artificial neural network (ANN) coupled with genetic algorithm (GA). The effect of incubation time, incubation temperature, and enzyme concentration on different responses such as yield, turbidity, ascorbic acid content, polyphenol content, total soluble solids, and pH was also determined. The developed ANN has minimum mean squared error values of 0.83, 40.92, 29.01, and 8.95 and maximum R values of 0.9999, 0.9972, 0.9995, and 0.9996 for training, testing, validation, and all data sets, respectively, which shows good agreement between the actual and predicted values. The optimum extraction parameters obtained using the developed ANN-GA were as follows: an incubation time of 64 min, incubation temperature of 32 degrees C, and enzyme concentration of 0.078%. The measured value of responses at the optimized process conditions were in accordance with the predicted values obtained using the developed ANN model.
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