高等学校化学学报2026，Vol.47Issue(7)：56-62,7.DOI:10.7503/cjcu20260047
机器学习驱动的中药银杏叶SERS多维度鉴定
Machine-learning-driven Multidimensional Identification of Traditional Chinese Medicine Ginkgo Folium Using SERS
摘要
Abstract
This study establishes a multidimensional discrimination strategy for ginkgo folium based on surface-enhanced Raman spectroscopy(SERS)combined with machine learning.A systematic investigation was conducted across four dimensions(geographical origin,drying process,storage quality,and pesticide residues),enabling intelligent classification and quality assessment of ginkgo folium.The results showed that the SERS spectra of ginkgo folium extracts exhibited characteristic peaks of flavonoids(quercetin,kaempferol,isorhamnetin)and ginkgolides.Based on SERS combined with partial least squares discriminant analysis(PLS-DA),100%classification accuracy was achieved for ginkgo folium samples from five origins and processed by four drying methods,indicating that both origin differences and drying processes significantly affect their chemical composition.Furthermore,SERS combined with partial least squares regression(PLS)accurately predicted storage time(training set,R²=0.991;cross-validation set,R²=0.841)and enabled highly sensitive quantitative detection of pesticide residues,including glyphosate and thiram.In summary,SERS combined with machine learning provides a rapid,highly sensitive,and reliable analyti-cal strategy for origin tracing,process monitoring,storage evaluation,and pesticide residue detection of ginkgo folium and other traditional Chinese medicinal materials.This approach opens a novel technical pathway for quality control and safety supervision in traditional Chinese medicines field.关键词
银杏叶/表面增强拉曼光谱/机器学习/多维度鉴定/中药质量鉴定Key words
Ginkgo folium/Surface-enhanced Raman spectroscopy/Machine learning/Multidimensional identifica-tion/Quality assessment of traditional Chinese medicine分类
化学化工引用本文复制引用
马其林,吴叶,汤丹旸,杨茂生,徐国富,杨帆,石嘉,姚建铨..机器学习驱动的中药银杏叶SERS多维度鉴定[J].高等学校化学学报,2026,47(7):56-62,7.基金项目
安徽省高等学校科学研究项目(批准号:2024AH051986)和天津市光电检测技术与系统重点实验室开放课题(批准号:2025LODTS112,2025LODTS106)资助. Supported by the Scientific Research Project of Universities of Anhui Province,China(No.2024AH051986)and the Open Project of Tianjin Key Laboratory of Optoelectronic Detection Technology and System,China(No.2025LODTS112,2025LODTS106). （批准号:2024AH051986）
