Optimization Technology of Longshan Millet γ-amino Butyric Acid Enrichment Process by Neural Network Algorithm

Abstract

In order to promote the quality and efficiency improvement of Longshan millet industry and enhance the added value of products, this study took Longshan millet as the main raw material to learn the effects of soaking time, germination temperature and germination time on Longshan millet γ-aminobutyric acid content. Based on the single factor test, the response surface optimization test was carried out. Through back propagation (BP) neural network and genetic algorithm (GA), the optimization test results are simulated, analyzed and optimized. The results showed: The optimal process for γ-aminobutyric acid enrichment of Longshan millet was soaking time of 11.5 h, germination temperature of 38.5 ℃, and germination time of 49.5 h. Through this process, the content of Longshan millet γ-aminobutyric acid was 444.03 mg/kg, 5.68 times higher than that of untreated samples.

Keywords

• longshan millet
• γ-aminobutyric acid
• neural network
• genetic algorithm technology
• process optimization