Comparative machine learning strategies for improving antioxidant properties and aroma quality in fermented mung bean milkby Lactobacillus plantarum PC4 - Tang, Ping; Mohsin, Aliah Zannierah; Juhari, Nurul Hanisah; Meor Hussin, Anis Shobirin (Elsevier B.V., 2025)
This study compares least squares support vector machine (LSSVM) and artificial neural network (ANN) models, integrated with the NSGA-II algorithm, to optimize the fermentation of mung bean milk by Lactobacillus plantarum PC4. Given its superior predictive accuracy and generalization, LSSVM was selected as the final model for multi-objective optimization and experimental validation. LSSVM consistently outperformed ANN in predictive accuracy and generalization, particularly under data-scarce conditions, yielding R2 values exceeding 0.97 across all responses. Optimal fermentation conditions predicted by LSSVM (6.0 h, 37.0 °C, 1.99 % inoculum) were experimentally validated, showing minimal error (<5 %) across most parameters. GC–MS analysis confirmed that the LSSVM-NSGA-II optimized fermentation conditions effectively suppressed off-flavor aldehydes (e.g., hexanal, nonanal) while promoting the formation of favorable volatiles, including 1-hexanol, acetoin, esters, and aromatic compounds. These targeted improvements in antioxidant and aroma profiles underscore the efficacy of this data-driven approach in enhancing both the functional and sensory attributes of plant-based fermented beverages.