Assessment of the status property(boiling time) is a challenge for the quality control of extraction process in pharmaceutical enterprises. In this study, the pilot extraction process of Phellodendron chinense was used as the research carrier to develop an online near-infrared(NIR) quality control method based on the status property(boiling time). First, the NIR spectra of P. chinense were collected during the two pilot-scale extraction processes, and the status property(boiling time) was assessed by observing the state of bubbles in the extraction tank using a transparent window during the extraction process, which was then used as a reference standard. Based on the moving block standard deviation(MBSD) algorithm, the assessment model using online NIR spectra for boiling time during extraction process was established. In addition, the model was optimized as follows: standard normal variable(SNV) for spectral pretreatment, modeling band of 800-2 200 nm, and window size of 4. The results showed that, with 0.002 0 as the MBSD model threshold, the boiling time can be accurately assessed using online NIR spectra during extraction process. Furthermore, the principal component analysis-moving block standard deviation(PCA-MBSD) model was developed by our group to reduce the influence of online NIR spectral noise and background signal on the model, and the number of principal components was optimized into 2 in the PCA-MBSD model. The results showed that, with 0.000 075 as the PCA-MBSD model threshold, the boiling time can be accurately assessed using online NIR spectra during extraction process, with improved reliability. This study can provide a assessment method for boiling time during extraction process using online NIR spectra, which can replace the empirical judgment in manual observation, and realize the digitalization of the extraction process for big brand traditional Chinese medicine.
Keywords: big brand traditional Chinese medicine; boiling time; classic formulas; critical quality attribute; extraction process; online near infrared.