分析测试学报2026，Vol.45Issue(5)：951-959,9.DOI:10.12452/j.fxcsxb.25120902
基于气相色谱-离子迁移谱及机器学习的不同产地香附饮片的快速鉴别
Rapid Identification of Cyperi Rhizoma Decoction Pieces from Different Geographical Origins Based on GC-IMS Combined with Machine Learning
摘要
Abstract
Gas chromatography-ion mobility spectrometry(GC-IMS)was employed to analyze the volatile components of Cyperi Rhizoma from different origins.Partial least squares-discriminant anal‑ysis(PLS-DA)was applied to screen differential characteristic components.Based on these charac‑teristic components,9 machine learning algorithms such as SVM-L were constructed to develop rap‑id discrimination models for Cyperi Rhizoma from different origins.Fifty-nine volatile compounds were identified in Cyperi Rhizoma decoction pieces from five provinces,including alcohols,esters,aldehydes,ketones,and unsaturated hydrocarbons.Ten characteristic components were screened by PLS-DA analysis,such as 1,4-dimethylbenzene,3-methyl-1-pentanol,and ethyl acetoace‑tate.Furthermore,nine machine learning models all demonstrated excellent predictive performance(Accuracy=1),indicating good potential for practical application.This study provides a simple and rapid method for discrimination and identification of Cyperi Rhizoma from different geographical ori‑gins,while also offering a reference for establishing a quality evaluation system for Cyperi Rhizoma.关键词
气相色谱-离子迁移谱/香附/挥发性成分/产地/机器学习Key words
gas chromatography-ion mobility spectrometry(GC-IMS)/Cyperi Rhizoma/volatile components/geographical origins/machine learning分类
化学化工引用本文复制引用
卢腾飞,刘青,董红敬,李佳,李明坤,王晓,张艳艳..基于气相色谱-离子迁移谱及机器学习的不同产地香附饮片的快速鉴别[J].分析测试学报,2026,45(5):951-959,9.基金项目
齐鲁工业大学(山东省科学院)科教产融合试点工程重大创新类项目(2025ZDZX07) （山东省科学院）
国家现代农业产业技术体系项目(CARS-21) （CARS-21）
