On-line monitoring of extraction of coldrine by near-infrared spectrum.
- Author:
Guo-Quan CHEN
1
;
Hong-Ye PAN
1
;
Xue-Song LIU
1
;
Yue-Fei ZHANG
2
;
Ping SHAO
3
;
Long-Hu WANG
4
Author Information
- Publication Type:Journal Article
- Keywords: coldrine granules; extraction process; near-infrared spectroscopy; on-line monitoring
- From: China Journal of Chinese Materia Medica 2016;41(8):1383-1387
- CountryChina
- Language:Chinese
- Abstract: Extraction of the four Chinese herbals is the beginning step of the production process of coldrine granules and influences on drug quality significantly. In this paper, the on-line near infrared spectrum was collected during the extraction process of coldrine and then pre-processed by the first derivative. Partial least square regression (PLSR) model was developed for the quantity indicators of linarin, chlorogenic acid and solid content, according to results of both HPLC and weight-loss as reference methods. The correlation coefficient, root mean square error of cross-validation (RMSECV), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were used to optimize model parameters and confirm their performance. Correlation coefficients of three quality control indicator models reached more than 0.95.Values of RMSEC of linarin, chloroenic acid and solid content were 0.010 4, 0.009 34 and 0.055 5, respectively. And the values of RMSEP were 0.009 47, 0.142 and 0.008 42, respectively. The models, built on-line analyze data, revealed that the correlation coefficients of predicted values and measured values were greater than 0.97 and values of RSEP of linarin, chloroenic acid and solid content were 8.14%, 8.17% and 9.86%, respectively. The results showed that the NIR method could achieve the on-line detection and real-time monitoring of multi-indexes during the extraction process of coldrine. The technology could be used for drug quality control in the process of practical production, reducing the batch differences and ensuring pharmaceutical quality stability. In addition, it could provide real-time production data for subsequent product quality backtracking.
