Discrimination and chemical composition quantitative model of Raw Moutan Cortex and Moutan Cortex Carbon based on electronic nose and machine learning.

Raw Moutan Cortex (RMC) is a traditional medicinal material commonly used in China. Moutan Cortex Carbon (MCC) is a processed product of RMC by stir-frying. As raw and processed products of the same Chinese herb pieces, they have different effects. RMC has the effects of clearing heat and cooling blood, promoting blood circulation and removing blood stasis, but MCC has the contrary effect of cooling blood and hemostasis. Therefore, it is necessary to distinguish them effectively. The traditional quality evaluation method of RMC and MCC still adopts character identification, and mainly relies on the working experience and sensory judgment of employees with experience. This will lead to strong subjectivity and poor repeatability. And the final evaluation result may cause inevitable errors and the processed products with different processing degrees in actual production, which affects the clinical efficacy. In this study, the electronic nose technology was introduced to objectively digitize the odor of RMC and MCC. And the discrimination model of RMC and MCC was constructed in order to establish a rapid, objective and stable quality evaluation method of RMC and MCC. According to the correlation analysis, the experiment found the content of gallic acid, 5-hydroxymethylfurfural (5-HMF), paeoniflorin and paeonol determined by high performance liquid chromatography (HPLC) had a certain correlation with their odor characteristics. Thus, partial least squares regression (PLSR) and support vector machine regression (SVR) were compared and established the chemical composition quantitative model. Results showed that the quantitative data of RMC and MCC odor could be used to predict the contents of the chemical components. It can be used for quality control of RCM and MCC.