Optimization and comparison of ℽ-aminobutyric acid (GABA) production by LAB in soymilk using RSM and ANN models

Abstract Background ℽ-Aminobutyric acid (GABA) is a non-proteinaceous amino acid. In the mammalian nervous system, GABA functions as an inhibitory neurotransmitter. The present study focused on screening and optimization of ℽ-aminobutyric acid (GABA) yield by lactic acid bacteria by using soymilk as basal media. Lactobacillus fermentum ( Lb. fermentum ) was isolated from sourdough. The qualitative confirmation of GABA production by Lb. fermentum was observed by detecting colored spots on thin layer chromatography plate (TLC) and comparing it with standard GABA spot. The GABA from bacteria is confirmed by its molecular mass using mass spectrophotometry analysis (MS analysis). Single variable experiments were conducted for various physical and nutritional parameters, and determined the GABA content produced from Lb. fermentum , viable bacterial count, and pH of the fermented soymilk medium. Experimental data were authenticated by using response surface method (RSM) and artificial neural network (ANN) model. Results The results demonstrated that through single variable experiments, the yield of GABA and the viable bacterial cells increased in soymilk containing one percent of glucose, monosodium glutamate (MSG), and inoculum volume incubated at 37 °C, 48 h at pH 5. According to RSM results, the interaction of the highest concentration of MSG (1.5%) and mid glucose concentration (1.156%) yielded maximum GABA (5.54 g/L). The experimental data were in good agreement with two optimization models. The RSM models showed less error percentage than that of the ANN model. Conclusion This study indicates that soymilk is the best basal substrate for GABA production and growth of Lb. fermentum compared to synthetic media. Lb. fermentum can be explored further by food and pharmaceutical industries for the development of functional foods and therapeutic purposes.