Identification of Crataegi Fructus decoction pieces under different stir-frying degrees with GC-IMS and machine learning
OBJECTIVE To develop a method for rapid identification of Crataegi Fructus decoction pieces under different stir-frying degrees.METHODS Gas chromatography-ion mobility spectrometry(GC-IMS)was employed for identifying the contents of volatile compounds in C.Fructus decoction pieces under different stir-frying degrees.Three data analytic methods of partial least squares discriminant analysis(PLS-DA),ridge regression and elastic network were employed for further screening for featured differential compounds.Based upon the featured compounds,machine learning algorithms were utilized for constructing models for identifying and discriminating C.Fructus decoction pieces under different stir-frying degrees.RESULTS A total of 47 volatile compounds were detected from C.Fructus decoction pieces under different stir-frying degrees by GC-IMS,including 10 alcohols,9 aldehydes,8 esters,6 heterocycles,5 ketones,4 organic acids,2 hydrocarbons,2 unsaturated hydrocarbons and 1 phenolic.Six featured compounds were selected by combining the data analytic methods of PLS-DA,ridge regression and elastic network.Finally,among 7 machine learning models,SVM-R and NB demonstrated the best prediction capability.It could be used to quickly identify and discriminate C.Fructus decoction pieces under different stir-frying degrees.CONCLUSION This study provides a simple and quick method of quickly identifying and discriminating C.Fructus decoction pieces under different stir-frying degrees.Also it offers references for establishing their quality evaluation methods.
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