Rapid Measurement of Antioxidant Activity and γ-Aminobutyric Acid Content of Chinese Rice Wine by Fourier-Transform Near Infrared Spectroscopy

In this study, Fourier-transform near infrared (FT-NIR) spectroscopy in combination with chemometrics was utilized to determine the antioxidant capacity and γaminobutyric acid (GABA) content of Chinese rice wine (CRW). Interval partial least-squares (iPLS) and extreme learning machine (ELM) were used to improve the performances of partial least-squares (PLS) models. In total, four different calibration models, namely PLS, iPLS, ELM, and ELM models based on the subintervals selected by iPLS (iELM), were developed in this study. It was observed that the performances of models based on the efficient spectra intervals selected by iPLS were much better than those based on the full spectrum. In addition, nonlinear models were superior to linear models. After systemically comparison and discussion, it was found that for all of the four parameters determined, iELM model achieved the best result with excellent prediction precision. The coefficient of determination for the prediction set (R (pre)), and the residual predictive deviation for the prediction set were 0.932 and 4.07 for 1,1-diphenyl-2-picrylhydrazyl assay, 0.970 and 6.21 for 2,2-azino-bis-(3-ethylbenzothiazoline6-sulfonic acid) diammonium salt assay, 0.974 and 6.29 for total reducing antioxidant power assay and 0.952 and 4.75 for GABA, respectively. The overall results demonstrated that FT-NIR combined with efficient variable selection algorithm and nonlinear regression tool could be used as a rapid alternative method for the prediction of the antioxidant capacity and GABA content of Chinese rice wine.