Application of near infrared spectroscopy combined with multivariate statistical process control for in-line monitoring of extraction of Lonicerae Japonicae Flos / 中草药

ABSTRACT

Objective To develop a multivariate statistical process control (MSPC) model for in-line monitoring of the extraction process of Lonicerae Japonicae Flos (LJF). Methods The spectral data collected by near infrared spectrascopy coupled with MSPC technique were applied to establish the statistical model. Three kinds of multivariate control charts (PC scores, Hottelling T2 and DModX) were used to monitor the abnormal variations caused by the change of starting material quality attributes and abnormal operation conditions. Moreover, the extraction process trajectory was developed by applying principal component analysis on the process spectra, reflecting the changing trend with extraction time. Results The MSPC model with good repeatability and robustness could show the quality variation of extraction process of LJF and accurately predict the normal batches. The joint use of three control charts could effectively identify the abnormal conditions. Compared with the traditional monitoring methods, this approach was fast and nondestructive, and can implemente the in-line and real-time monitoring. Conclusion The near infrared spectroscopy combined with MSPC can be successfully applied to the extraction processes of LJF, which is of great significance for the improvement of the quality control of Chinese material medica (CMM).