Application of artificial neural network with metaheuristic optimization for improving the nutritive value of fried fish
Tithli Sadhu
Department of Chemistry, National Institute of Technology Durgapur, Durgapur, India
Department of Biochemistry, School of Agriculture, SR University, Hanumakonda, Telangana, India
Search for more papers by this authorSandip K. Lahiri
Department of Chemical Engineering, National Institute of Technology Durgapur, Durgapur, India
Search for more papers by this authorAshish Bhattacharjee
Department of Biotechnology, National Institute of Technology Durgapur, Durgapur, India
Search for more papers by this authorCorresponding Author
Jitamanyu Chakrabarty
Department of Chemistry, National Institute of Technology Durgapur, Durgapur, India
Correspondence
Jitamanyu Chakrabarty, Department of Chemistry, National Institute of Technology Durgapur, Mahatma Gandhi Avenue Durgapur, 713209, West Bengal, India.
Email: [email protected]
Search for more papers by this authorTithli Sadhu
Department of Chemistry, National Institute of Technology Durgapur, Durgapur, India
Department of Biochemistry, School of Agriculture, SR University, Hanumakonda, Telangana, India
Search for more papers by this authorSandip K. Lahiri
Department of Chemical Engineering, National Institute of Technology Durgapur, Durgapur, India
Search for more papers by this authorAshish Bhattacharjee
Department of Biotechnology, National Institute of Technology Durgapur, Durgapur, India
Search for more papers by this authorCorresponding Author
Jitamanyu Chakrabarty
Department of Chemistry, National Institute of Technology Durgapur, Durgapur, India
Correspondence
Jitamanyu Chakrabarty, Department of Chemistry, National Institute of Technology Durgapur, Mahatma Gandhi Avenue Durgapur, 713209, West Bengal, India.
Email: [email protected]
Search for more papers by this authorAbstract
Nutritional quality indices of fish deteriorate drastically during frying. In this study, using Catla catla fish and mustard oil (culinary media), extensive experiments are carried out varying the temperature, time, and oil amount to attain the best nutritional quality indices of fried fish with a tuned combination of cooking parameters. An artificial neural network (ANN) is developed to select the best model to find a nonlinear correlation between the frying conditions and nutritional quality indices. ANN-based metaheuristic optimization methodologies, namely genetic algorithm (GA), differential evolution, firefly optimization, and gray wolf optimization (GWO), are applied to optimize the best cooking conditions. Among these, GWO is most promising for optimizing favorable inputs, practical optimal solutions, and reasonable execution time. As outputs are conflicting, multi-objective genetic algorithm (MOGA) is implemented for their simultaneous optimization with optimum values of process variables for health benefit, reducing frying time, and minimizing the wastage of culinary media. The MOGA successfully improves the ω-3/ω-6 fatty acids, polyunsaturated fatty acids/saturated fatty acids, cis/trans fatty acids ratio, and index of atherogenicity values up to 40.43%, 65.35%, 137%, and 83.84%, respectively, satisfying the multi-objective criteria.
Practical applications
The conventional frying process of fish is successfully optimized by the developed hybrid “model-optimization” topology that improves the nutrient value of fried fish significantly. The developed neural model automatically searches all available algorithms and activation functions exhaustively to select the best model. All single-objective and multi-objective genetic algorithms integrated with an artificial neural network can attain the optimum successfully for all outputs by a unique tuned cooking condition. Operators can choose the preferred solution among many Pareto optimal combinations as per their needs. Furthermore, this developed, generic topology provides a tool for process modification and optimization of other food process engineering methods.
CONFLICT OF INTEREST
The authors declare that they have no conflicts of interest.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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