Dynamic predictive models of five alkaloids in Coptis during the process of stir-frying with wine using near-infrared spectroscopy

Abstract

The goal of this research was to develop dynamic predictive models for five alkaloids in Coptis during the process of stir-frying with wine by near-infrared spectroscopy (NIR). Six batches of samples were collected and then processed on the basis of orthogonal design. With high-performance liquid chromatography (HPLC) analysis as reference, calibration models were generated by a partial least squares (PLS) regression. The root mean square errors of prediction (RMSEP) for the PLS models of jatrorrhizine, epiberberine, coptisine, palmatine, and berberine were 0.0030, 0.0387, 0.0206, 0.0095, and 0.0516, and the correlation coefficients (R) were 99.43%, 98.41%, 99.29%, 99.23%, and 99.87%, respectively. Accordingly, these results demonstrated that the models could be efficiently used to develop robust methods of online analysis and quality control for Coptis.