Near-infrared determination of polyphenols using linear and nonlinear regression algorithms

In the present study, the possibility of using Fourier transform near-infrared spectroscopy (FT-NIR) to measure the concentration of polyphenols in Yunnan tobacco was investigated. Selected samples representing a wide range of varieties and regions were analyzed by high performance liquid chromatography (HPLC) for the concentrations of polyphenols in tobacco. Results showed that positive correlations existed between NIR spectra and concentration of objective compound upon the established linear and nonlinear regression models. The optimal model was obtained by comparing different modeling processes. It was demonstrated that the PLS regression covering the range of 5450–4250 cm−1 could lead to a good linear relationship between spectra and polyphenols with the R2 of 0.9170. Optimal model generated the RMSEP of 0.254, RSEP of 0.0554, and RPD of 3.47, revealing that the linear model was able to predict the content of polyphenols in tobacco. Support vector regression (SVR) preprocessed by SNV obtained the predictable results with the R2 of 0.8461, RMSEP of 0.374, and RPD of 2.36, which was inferior to PLS modeling.