To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near-infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS-DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross-validation of 0.0261. PLS-DA model of mean centre and first-order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post-harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices.
Gamma-aminobutyric acid (GABA), a main inhibitory neurotransmitter in brain, is a nonproteinogenic amino acid. The production of GABA from rice bran by lactic acid bacteria (Lactobacillus brevis) is an approach for maximising the potential of agricultural byproduct and reducing production cost. The use of media additives enhances the efficiency of GABA production, but their effects on cells and biomass in fermentation must be assessed to determine the appropriate concentration and efficiency. The influences of CaCl 2 (0-1.5% w/v), Tween 80 (0-0.9% w/v), MnSO 4 (0-0.6% w/v) and Pyridoxine hydrochloride (0-90 ppm) at different concentrations on GABA production by defatted rice bran extract fermentation using Lb. brevis VTCC-B397 were evaluated. The changes in cell density, GABA content, reducing sugar content and pH value were observed. Consequently, the impact of media additive compounds on growth, development and GABA synthesis of Lb. brevis was demonstrated. Results showed that CaCl 2 , MnSO 4 and Tween 80 positively affected GABA production from defatted rice bran extract by fermentation method.
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