Optimization of accelerated solvent extraction of zeaxanthin from orange paprika using response surface methodology and an artificial neural network coupled with a genetic algorithm

• Published in: Food science and biotechnology 2024, 33(11), , pp.2521-2531
• Main Authors: Kim, Jaecheol, Lee, Ga Eun, Kim, Suna
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Chemistry; Chemistry and Materials Science; Correlation coefficient; Correlation coefficients; Ethanol; Food resources; Food Science; Functional materials; Genetic algorithms; Hydrophobicity; Neural networks; Nutrition; Optimization; Paprikas; Regression models; Research Article; Response surface methodology; Solvent extraction; Solvents; Zeaxanthin; 식품과학
• ISSN: 1226-7708; 2092-6456; 2092-6456
• DOI: 10.1007/s10068-023-01514-8

This study aimed to optimize the accelerated solvent extraction (ASE) condition of zeaxanthin from orange paprika using a response surface methodology (RSM) or an artificial neural network (ANN) with a genetic algorithm (GA). Input variables were ethanol concentration, extraction time, and extraction temperature, while output variable was zeaxanthin. The mean squared error and regression correlation coefficient of the developed ANN model were 0.3038 and 0.9983, respectively. Predicted optimal extraction conditions from ANN-GA for maximum zeaxanthin were 100% ethanol, 3.4 min, and 99.2 °C. The relative errors under the optimal extraction conditions were RSM for 10.46% and ANN-GA for 2.18%. We showed that the recovery of hydrophobic zeaxanthin could be performed using ethanol, an eco-friendly solvent, via ASE, and the extraction efficiency could be improved by ANN-GA modeling than RSM. Therefore, combining ASE and ANN-GA might be desirable for the efficient and eco-friendly extraction of hydrophobic functional materials from food resources.