Prediction of Cinnamomum cassia grade based on binary Logistic regression analysis / 中草药

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.