Optimization of esterase production from Monascus purpureus Y146 by artificial neural network-genetic algorithm and its application in flavor enhancement of fermented wine.

Abstract

Esterases play an important role in flavor regulation during food fermentation. In this study, a high esterase-producing strain, Monascus purpureus Y146, was used to optimize esterase production via one-factor-at-a-time experiments (OFAT), response surface methodology (RSM), and an artificial neural network with genetic algorithm (ANN-GA) approach. The esterase was partially purified and characterized, and its application in rice wine fermentation was evaluated using HS-SPME-GC-MS. The results showed that the esterase activity of the Y146 strain reached up to 1248.02 U/mL, a 4.68-fold increase over the unoptimized condition, under conditions of 16% seed inoculation, 10 g/L L-arginine addition at 84 h, pH 7, and 8.69 d of fermentation. The partially purified esterase preparation exhibited optimal activity at 30 °C and pH 7, showed enhanced activity in the presence of 2 mM Fe2+, and had an approximate half-life of 9 h. HS-SPME-GC-MS analysis identified ethyl caprate as the only aroma-active compound (OAV > 1) detected in the control group. Following addition of the partially purified esterase preparation, increased diversity and abundance of volatile compounds were observed, with the number of aroma-active compounds increasing from one to six, including ethyl undecanoate, ethyl hexanoate, ethyl palmitate, ethyl linoleate, 1-dodecanol, and 2,4-Di-tert-butylphenol. These findings highlight the potential application of the M. purpureus Y146-derived partially purified esterase preparation in flavor-oriented fermented food systems.