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

Abstract

Fish, 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 applications The 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.

Towards Foodservice Robotics: A Taxonomy of Actions of Foodservice Workers and a Critical Review of Supportive Technology

“…As the algorithm learns the staff preferences, the suggestions are tailored [238], so, the staff puts less effort into the initial decision of process parameters and in interacting with the machine. Another example is a research work instead [239], in which an Artificial Neural Network (ANN) was trained to model the nonlinear relation between controllable factors of frying (time, temperature, oil amount) and nutritional quality indices of the fried food. With two optimization methods (Differential Evolution and Simulated Annealing) applied to the ANN, the researchers were able to determine the frying factors that maximize the nutritional quality of fried fish.…”

“…With two optimization methods (Differential Evolution and Simulated Annealing) applied to the ANN, the researchers were able to determine the frying factors that maximize the nutritional quality of fried fish. This method could be integrated in the control of professional fryers or robots monitoring fryers, and it is also applicable to optimize other food processes [239]. In other studies, prototypes were created, like Chef Watson [240] or RecipeScape [241], to support cognitive tasks of food design i.e.…”

Quality Inspection of Food and Agricultural Products using Artificial Intelligence

“…The findings showed that the CNN model gave correct prediction higher than 99% for all classes of browning degree of bread crust. Sadhu et al (2020) determined the correlation between temperature, time, oil amount, and nutritional values of fried fish. The ANN model used had successfully tuned the cooking parameters to regain the nutritional composition of the fried fish.…”

Application of artificial neural network with metaheuristic optimization for improving the nutritive value of fried fish

“…Recently, artificial neural network (ANN) is successfully utilized in the food industry, food processing, food engineering, food properties, or quality control to model and predict many different processes because of its efficiency, high flexibility, good adaptability, possibility of generalization, high noise tolerance, and simplicity (Aghbashlo et al, 2015; Guiné, 2019). Our previous studies established a unified nonlinear relationship between the process parameters of frying and different NQI by ANN and tried to preserve the nutritional value after frying by tuning the cooking condition through various single‐objective optimization algorithms (Sadhu et al, 2022; Sadhu, Banerjee, Lahiri, & Chakrabarty, 2020). But these single‐objective optimization topologies failed to optimize the tuned cooking condition simultaneously for these conflicting NQI.…”