USE OF ARTIFICIAL INTELLIGENCE IN FOOD TECHNOLOGIES ON THE EXAMPLE OF OPTIMIZING PARAMETERS OF THE ENZYMATIC PROCESS FOR COCOA BUTTER EQUIVALENT PRODUCTION
DOI:
https://doi.org/10.15421/jchemtech.v34i1.351426Keywords:
artificial intelligence, interesterification, lipase, cocoa butter equivalent, particle swarm optimizationAbstract
In this study, an approach to intelligent control of biocatalytic processes in food technologies was substantiated and validated using the optimization of enzymatic interesterification for the production of a cocoa butter equivalent in a continuous packed-bed reactor with immobilized lipase. High-oleic sunflower oil and a mixture of ethyl esters of palmitic and stearic acids were used as substrates. The controlled factors were the molar ratio of ethyl esters to oil (4–9 mol/mol), temperature (50–75 °C), and hydrodynamic residence time (15–60 min). The methodological framework was based on integrating artificial intelligence models with an evolutionary optimization method, namely particle swarm optimization. Experimental data were obtained using an orthogonal maximin Latin hypercube design and used to compare nine regression models via nested cross-validation. Support vector regression with a radial basis function kernel provided the highest accuracy and the lowest inter-fold variability. The selected model was used as the fitness function in particle swarm optimization, enabling determination of the optimal enzymatic interesterification conditions: substrate molar ratio of 6 mol/mol, temperature of 65 °C, and residence time of 38 min. A confirmatory experiment verified the adequacy of the model in the vicinity of the optimum: the cocoa butter equivalent yield was 77.0 ± 1.1 %, with a relative deviation of δ = 0.52 %. The results demonstrate the effectiveness of using artificial intelligence to identify optimal parameters of biocatalytic processes in food industry technologies.
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