Modeling and optimization of cooking process parameters to improve the nutritional profile of fried fish by robust hybrid artificial intelligence approach

Abstract

AbstractFish, being a good source of nutrients, is often cooked by different methods before consumption, which affect the beneficial quality detrimentally. In this study, Catla catla, and mustard oil are selected as representative of fish and cooking oil for frying, respectively, because of their agricultural importance and worldwide demand. Extensive experiments are performed varying the effective processing variables of conventional frying viz., temperature (140 °C‐240 °C), time (5 min–20 min) and oil amount (25 ml/kg of fish‐100 ml/kg of fish) to correlate the drastic reduction of the nutritional quality indices, that is, ω‐3/ω‐6 and cis/trans‐fatty acids (FAs) profiles of fish after frying. To establish a nonlinear correlation between these inputs and outputs, an exhaustive search of all available artificial neural network (ANN) algorithms and activation functions is executed for the development of a model. The hybrid robust process approach integrating ANN with differential evolution (DE) and simulated annealing (SA) are employed to optimize the cooking parameters for regaining nutritional impact. After frying ω‐3/ω‐6 and cis/trans‐FAs ratio deteriorated by 76.65% and 92.68%, respectively, than the fresh samples. The ANN‐DE and ANN‐SA formalism efficiently enhanced these nutritional parameters up to 33.18% and 79%, respectively.Practical applicationsThe present study applied artificial neural network (ANN) as an advanced alternative modeling tool to propose a generalized nonlinear correlation between temperature, time, oil amount, and nutritional values, that is, ω‐3/ω‐6 and cis/trans‐fatty acids (FAs) profiles of fried fish. Frying time provided a strong impact on food nutrition compared to other two input variables. Frying process detrimentally affected both the nutritional indices, that is, ω‐3/ω‐6 and cis/trans‐FAs profiles. The meta‐heuristic, stochastic optimization algorithms, namely differential evolution and simulated annealing along with ANN‐based processed model were implemented successfully to tune the cooking parameters, so that food quality indices of fish improved again to maximum value. The artificial intelligence modeling, along with optimizing methodology based parameters tuning approach described here is generic and can be advantageously extended to other experimentation of food process engineering. Besides, the finding of this study will benefit common people also.