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基于GC-IMS及机器学习的不同炒制程度山楂饮片的快速鉴别

Identification of Crataegi Fructus decoction pieces under different stir-frying degrees with GC-IMS and machine learning
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摘要 目的:构建不同炒制程度山楂饮片的快速鉴别方法。方法:采用气相色谱-离子迁移谱(gas chromatography-ion mobility spectrometry,GC-IMS)分析不同炒制程度山楂饮片中的挥发性成分及其含量,采用偏最小二乘判别分析(partial least squares discriminant analysis,PLS-DA)、岭回归和弹性网络3种数据分析方法进一步筛选具有差异的特征性成分;基于特征性成分,采用7种机器学习算法开展不同炒制程度山楂饮片的鉴别与构建区分模型。结果:通过GC-IMS从不同炒制程度的山楂饮片中共检测出47种挥发性成分,包含10个醇类、9个醛类、8个酯类、6个杂环类、5个酮类、4个有机酸类、2个烃类、2个不饱和烃类和1个酚类;结合PLS-DA、岭回归及弹性网络3种数据分析方法共筛选出6个特征性成分;7种机器学习算法的预测结果显示,支持向量机径向核函数(support vector machine radial kernel,SVM-R)和朴素贝叶斯(naive Bayes,NB)具有较好的预测能力,可用于不同炒制程度山楂饮片的快速鉴别与区分。结论:本研究为不同炒制程度山楂饮片的快速鉴别与区分提供了一种简便、快速的方法,同时可为山楂及其炮制品质量评价体系的建立提供参考。 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 47volatile compounds were detected from C.Fructus decoction pieces under different stir-frying degrees by GC-IMS,including 10alcohols,9 aldehydes,8 esters,6 heterocycles,5 ketones,4 organic acids,2 hydrocarbons,2 unsaturated hydrocarbons and 1phenolic.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 stirfrying degrees.Also it offers references for establishing their quality evaluation methods.
作者 刘纹纹 董红敬 张敏敏 刘双 马鑫慧 王珍强 王晓 LIU Wenwen;DONG Hongjing;ZHANG Minmin;LIU Shuang;MA Xinhui;WANG Zhenqiang;WANG Xiao(College of Pharmacy,Shandong University of Traditional Chinese Medicine,Shandong Jinan 250300,China;Qilu University of Technology(Shandong Academy of Sciences)Shandong Analysis&Test Center,Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province,Shandong Jinan 250014,China;Qilu University of Technology(Shandong Academy of Sciences)College of Pharmacy,Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province,Shandong Jinan 250014,China)
出处 《中国医院药学杂志》 CAS 北大核心 2024年第18期2082-2089,共8页 Chinese Journal of Hospital Pharmacy
基金 山东省重点研发计划项目(编号:2021CXGC010508) 山东省泰山学者项目(编号:tstp20221138)。
关键词 山楂 炒制程度 机器学习 挥发性成分 气相色谱-离子迁移谱 crataegi fructus stir-frying degree machine learning volatile compounds GC-IMS
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