ABSTRACT
Objective: A logistic regression model for grade evaluation of Paeoniae Radix Rubra medicinal slices was constructed based on the quality control idea of traditional Chinese medicines that "ingredients reflect activity and activity expresses effect". Methods: Q-marker content of paeoniflorin was tested by ultra-high performance liquid chromatography (UPLC). Anticoagulation valence, inhibition rate of hydroxyl radical and DPPH clearance rate were used as evaluation indexes of biological activity. Correlations between paeoniflorin content in different batches and bioactivity indexes were analyzed by the logistic algorithm. Finally, a "principal component analysis-Logistic regression" model for grade evaluation of Paeoniae Radix Rubra was constructed. Results: According to grade evaluation results, the grade probability of different batches of Paeoniae Radix Rubra was higher than 95%. Among 16 batches of Paeoniae Radix Rubra, 15 batches were evaluated excellent, good and moderate (five for each), and one batch was evaluated poor. Conclusion: A new grade evaluation method for Paeoniae Radix Rubra medicinal slices is constructed preliminarily. It is applicable to quality evaluation of Paeoniae Radix Rubra medicinal slices.
ABSTRACT
Objective: A Logistic model for quality evaluation of Persicae Semen slices was constructed and its feasibility was verified in this study based on the thoughts on quality control of Chinese materia medica “Components reflect activity and activity points to efficacy”. Methods: Content of amygdalin, thrombin time (TT), ABTS clearance rate, DPPH inhibition rate, and hydroxyl radical scavenging ability in vitro were analyzed by a binary Logistic algorithm. Besides, a grade prediction model for Persicae Semen was established and verified. Results: A total of 18 batches of Persicae Semen were divided into four grades: excellent (represented by Neimeng Persicae Semen), good (represented by Gansu Persicae Semen), medium (represented by Liang Persicae Semen), and poor (represented by Shaanxi Persicae Semen) according to the probability formula of the Logisitc model. All batches of Persicae Semen slices were classified accurately, manifested by the high value of fitting probability (P > 98%). Conclusion: The classification standard based on the Logistic algorithm involving quality control component-in vitro titer is applicable to Persicae Semen slices on the market. Classification results are accurate and reliable.