A novel quality evaluation method for magnolia bark using electronic nose and colorimeter data with multiple statistical algorithms

Magnolia bark (Magnolia Officinalis REHD. & WILS. and Magnolia officinalis REHD. & WILS. VAR. biloba REHD. & WILS, Hou Po in Chinese), is widely applied in clinical prescriptions and Chinese patent medicines. Origin place is a crucial factor affecting the quality of Hou Po, and chemical composition is an important index for evaluating its quality, which is closely related to its clinical efficacy. This study aims to develop a novel method for rapidly, accurately and comprehensively identifying the origin places of Hou Po and predicting the contents of its important chemical components. High performance liquid chromatography was used to analyze the contents of magnolol and honokiol and ultra-performance liquid chromatography the contents of magnocurarine and magnoflorine. The cold soak method was used to determine the contents of water-soluble extracts. The E-nose and colorimeter were used to determine the odor and color characteristics, respectively, of the collected Hou Po samples. Using several statistical algorithms, different discriminant models based on the E-nose and colorimeter data were established to distinguish the origin place of Hou-Po and predict the chemical components of honokiol, magnolol, magnocurarine, magnoflorine and water-soluble extracts. The results showed that the Random Forest classifier combined with the ten-fold cross-validation method provided the highest classification accuracy for origin place, accounting for 99.53% among these models. The correlation coefficients between predicted and experimental values of the five chemical components were all higher than 0.96. This study has indicated that the electronic nose and colorimeter are promising methods for evaluating the quality of Chinese herbal medicines both qualitatively and quantitatively.