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Rapid and simultaneous analysis of five free anthraquinone contents in rhubarb during the stir-frying with rice wine process by near infrared reflectance spectroscopy
| Content Provider | Scilit |
|---|---|
| Author | Ye, Liming Chen, Xingyi Xue, Jintao Wang, Weiying Ma, Bihua Huang, Guo Chen, Yu |
| Copyright Year | 2019 |
| Abstract | Background: The active ingredients of Traditional Chinese Medicines (TCMs) vary greatly with the degree of stir-frying; so, rapid analysis of the active content is very important for the processing of TCMs. Objective: In this study, near infrared reflectance (NIR) spectroscopy was used to develop a new method for the rapid online analysis of five free anthraquinones (aloe-emodin, rhein, emodin, chrysophanol, and physcion) during the stir-frying process for rhubarb. Materials and Methods: With partial least-squares (PLSs) and artificial neural networks (ANN) regression, calibration models were generated based on five free anthraquinone contents, as measured by high-performance liquid chromatography. Results: The results indicated that the 2 types of models were robust, accurate, and repeatable for five free anthraquinones. Moreover, PLS as the linear model was more suitable for developing the NIR models of the five free anthraquinones than ANN. The performance of the optimal models was achieved as follows: the coefficient of determination for prediction (R^{2}$_{pre}$) for aloe-emodin, rhein, emodin, chrysophanol, and physcion was 0.9161, 0.9699, 0.9655, 0.9611, and 0.9724, respectively; the root mean square error of prediction was 0.0251, 0.0445, 0.3333, 0.0862, and 0.0211, respectively. Conclusion: The established NIR models could apply to determine the content of five free anthraquinones in rhubarb. This work demonstrated that NIR may be an effective online analysis method to reflect the quality of TCM industrial manufacturing processes. |
| Related Links | http://www.phcog.com/article.asp?issn=0973-1296;year=2019;volume=15;issue=63;spage=479;epage=486;aulast=Chen;type=2 |
| File Format | XHTML |
| ISSN | 09731296 |
| e-ISSN | 09764062 |
| DOI | 10.4103/pm.pm_562_18 |
| Journal | Pharmacognosy Magazine |
| Issue Number | 63 |
| Volume Number | 15 |
| Language | English |
| Publisher | Medknow |
| Publisher Date | 2019-01-01 |
| Access Restriction | Open |
| Subject Keyword | Integrative and Complementary Medicine Artificial Neural Network Anthraquinones Near Infrared Reflectance Spectroscopy Partial Least Squares Rhubarb Pharmacognosy Magazine, Volume 15, Issue 63 |
| Content Type | Text |
| Resource Type | Article |
| Subject | Drug Discovery Pharmaceutical Science |
