Comparative performance analysis of enzyme inactivation of soy milk by using RSM and ANN

Abstract

The presence of antinutritional factors as trypsin inhibitors (TIA) and lipoxygenase (LOX) in soy milk produces indigestion and off‐flavor due to oxidation of linoleic acid to hyperoxide. The objective of the study was to determine the prediction capacity of response surface methodology (RSM) and artificial neural network (ANN) for enzyme inactivation of soy milk. The microwave and thermo‐sonication method were used to prepare and treat the sample. Statistical parameters like NRMSE and %MAE were used to compare and evaluate the final result. NRMSE value had shown five times better results in the case of ANN (0.015) compared to RSM (0.082). Similarly, the % MAE value was also fivefold better in the case of ANN. In the case of RSM, Chi‐square values for TIA and LOX were 317.32 and 146.73, respectively. Whereas for ANN, the value was 4.68 and 2.69, respectively. So, it can be concluded that the prediction capacity of ANN is better than RSM. Practical Applications This modeling has a tremendous potential contributing to prediction of enzymes inactivation without real‐time experiments with great accuracy. Moreover, a number of industries are producing soy milk nowadays, and it could be helpful in elimination of beany flavor with desirable inactivation of lipoxygenase and enhancing the digestibility by removing trypsin inhibitors with appropriate environmental conditions. In addition, it can also pave the way for a number of food processing technology in which enzymatic inactivation is prominent and the prediction of percentage inactivation is crucial. This technique could be applied to predict the yield of any process after maintaining a certain treatment and process condition. Process engineering of any industry and biochemical processes are very sensitive to small changes in parameters and their effects could be predicted by using the modern approach of artificial neural network with more preciseness without running the actual experiments.

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Rapid monitoring of milk fat using Image processing coupled with ANN and PSO methods

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