Artificial Neural Network Models for Predicting and Optimizing the Effect of Air-frying Time and Temperature on Physical, Textural, Sensory, and Nutritional Quality Parameters of Fish Ball

Abstract

ABSTRACT The study was conducted to see the effect of air-frying time and temperature on physical, textural, sensory and nutritional quality parameters of air-fried fish balls and compared with the quality parameters of deep-fried fish balls. Multilayer feed-forwarded artificial neural network (ANN) models were fitted to the experimental data to predict the response variables of air-fried fish balls as a function of frying temperature and time. The model was validated using the holdback method. Based on the R2 and RMSE values, ANN model with three hidden layers was found to be best fitted model to explain the variability in the proximate composition, texture and sensory data. The desired air-frying condition obtained was temperature at 200 oC and time at 8 minutes by multi-response desirability score. The air-fried fish balls at desired condition had 81 % reduction in fat content and 17 % increase in protein content compared to deep fried sample.