Dynamic prediction models for alkaloid content using NIR technology for the study and online analysis of parching in Areca Seed

The goal of this research was to develop dynamic prediction models for contents of arecoline, arecaidine and guvacine by NIR for the study and online analysis of the parching process in Areca Seed (AS). Twenty types of AS were selected from 60 types obtained from various places and were then parched. With partial least squares (PLS), calibration models were generated based on Multiplicative Scatter Calibration (MSC) for guvacine and arecoline and First Derivative + MSC for arecaidine. The root mean square errors of cross-validation (RMSECV) for arecoline, arecaidine and guvacine were 0.141, 0.0822 and 0.181 mg/g, respectively; the root mean square errors of prediction (RMSEP) were 0.224, 0.0897 and 0.187 mg/g, respectively; the correlation coefficients (R) were 0.9813, 0.9658 and 0.9831, respectively. Furthermore, the time–temperature-content-drug efficacy law was analyzed, and some new technology and methods were used in online analysis and quality control.