Zeng Jing-Qi, Zhang Jing, Zhang Fang-Yu, Zhang Han, Zhu Ming-Li, Lu Ying, Guan Yong-Xia, Wu Zhi-Sheng
College of Pharmacy, Fujian University of Traditional Chinese Medicine Fuzhou 350122, China.
School of Chinese Materia Medica, Beijing University of Chinese Medicine Beijing 102488, China Engineering Research Center of Chinese Medicine Production and New Drug Development, Ministry of Education Beijing 102488, China.
Zhongguo Zhong Yao Za Zhi. 2021 Apr;46(7):1644-1650. doi: 10.19540/j.cnki.cjcmm.20210205.304.
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.
评估状态属性（沸腾时间）对制药企业提取过程的质量控制而言是一项挑战。在本研究中，以黄柏中试提取过程为研究载体，开发了一种基于状态属性（沸腾时间）的在线近红外（NIR）质量控制方法。首先，在两次中试规模提取过程中收集黄柏的近红外光谱，在提取过程中通过透明窗口观察提取罐中气泡的状态来评估状态属性（沸腾时间），并将其用作参考标准。基于移动块标准差（MBSD）算法，建立了利用在线近红外光谱评估提取过程中沸腾时间的评估模型。此外，对模型进行了如下优化：光谱预处理采用标准正态变量（SNV），建模波段为800 - 2200 nm，窗口大小为4。结果表明，以0.002 0作为MBSD模型阈值，提取过程中可利用在线近红外光谱准确评估沸腾时间。此外，本团队开发了主成分分析 - 移动块标准差（PCA - MBSD）模型以减少在线近红外光谱噪声和背景信号对模型的影响，在PCA - MBSD模型中将主成分数量优化为2。结果表明，以0.000 075作为PCA - MBSD模型阈值，提取过程中可利用在线近红外光谱准确评估沸腾时间，可靠性得到提高。本研究可为利用在线近红外光谱评估提取过程中的沸腾时间提供一种评估方法，可取代人工观察中的经验判断，实现大品牌中药提取过程的数字化。
