Abstract:In this study, Fourier-transform near infrared (FT-NIR) spectroscopy, attenuated total reflectance infrared (ATR-IR) spectroscopy and their combination for measurements of total antioxidant capacity (TAC) and total phenolic content (TPC) of Chinese rice wine (CRW) were compared. Synergy interval partial least-squares (SiPLS) algorithm was used to select wavelengths to improve PLS models and support vector machine (SVM) and principal component analysis (PCA) were applied to pre-process the merged data from two individual instruments. It was observed that models based on the efficient spectrum intervals selected by siPLS were much better than those based on the full spectra. Models from ATR-IR performed slightly better than those from FT-NIR. Moreover, SVM models based on the combination of two spectroscopies were superior to those from either FT-NIR or ATR-IR spectra, while PLS models based on the same information performed worse than those based on a single spectrum, which may be explained by some non-linearity in the data. Therefore, the integration of FT-NIR and ATR-IR was possible and could improve the prediction accuracy of TAC and TPC in Chinese rice wine.
