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

Background: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.Methods: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.Results: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.Conclusion: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.