Screening and optimization of γ-aminobutyric acid production from Monascus sanguineus under solid-state fermentation

The aim of this study was to screen and optimize gamma-aminobutyric acid (GABA) production from Monascus sanguineus. Different agro-waste residues were screened for GABA production. The GABA yield was confirmed by thin-layer chromatography and mass spectrometry. GABA was quantified using the simple ninhydrin protocol. Plackett-Burman and response surface methodology (RSM) as statistical tools were applied for screening and optimization of GABA yield. The accuracy of the RSM model was demonstrated by generating a non-statistical model using artificial neural network methodology. Coconut oil cake was the best substrate for GABA yield of all the tested substrates. Monosodium glutamate (MSG), pH and incubation period were found to favour GABA production. Maximum yield predicted from the RSM model was 15.53 mg/gds with an MSG concentration of 0.05 g at pH 7.5 and an incubation period of 20 days. This study considered an unexplored Monascus sp., M. sanguineus, which has primarily been used for pigment production. The capability of producing GABA from M. sanguineus using coconut oil cake as a substrate is an economical method with potential industrial use. The convincing results from this work could be considered as a benchmark for exploiting the Monascus strain to obtain GABA-enriched functional food for human consumption.