Prediction of Cinnamomum cassia grade based on binary Logistic regression analysis
10.7501/j.issn.0253-2670.2019.19.023
- VernacularTitle: 基于二分类Logistic回归分析的桂枝等级预测研究
- Author:
Da-Hai JIANG
1
Author Information
1. Shaanxi Province Key Laboratory of New Drugs and Chinese Medicine Foundation Research, Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine
- Publication Type:Journal Article
- Keywords:
Biological activities;
Cinnamaldehyde;
Cinnamic acid;
Cinnamomum cassia Presl;
Cinnamyl alcohol;
Coumarin;
Logistic regression;
Quality marker (Q-marker);
UPLC
- From:
Chinese Traditional and Herbal Drugs
2019;50(19):4697-4704
- CountryChina
- Language:Chinese
-
Abstract:
Objective: In this study, a two-classification model based on the idea of “ingredient-efficacy” was established for the quality classification of Cinnamomum cassia with considerations to quality control components and biological activities. Methods: A method to determine quality control components was proposed by UPLC. The in vitro anti-oxidant activity of C. cassia was reflected by DPPH and hydroxyl radical scavenging experiment. The quality control index and anti-oxidant index were correlated by a Logistic algorithm. Finally, a binary logistic regression model for classification of C. cassia was established. Results: UPLC fingerprints of 20 samples of C. cassia were established, and their anti-oxidant activities were determined. Four quality control components (coumarin, cinnamyl alcohol, cinnamic acid, and cinnamaldehyde) were screened out by principal component analysis, and their methodological validation was carried out. According to the regression equation, 20 batches of C. cassia were divided into four grades: excellent, good, medium, and poor. Conclusion: The binary logistic regression model can describe the mapping relationship between the grade of C. cassia. It can better express the classification standard for the prepared C. cassia. This study provides a new idea for quality evaluation of C. cassia.
