烟草科技2025，Vol.58Issue(8)：1-18,18.DOI:10.16135/j.issn1002-0861.2025.0051
基于UPLC-Q-Orbitrap/HRMS结合监督学习非靶向分析国内外烟草提取物难挥发成分差异
Non-targeted analysis of non-volatile components in domestic and foreign tobacco extracts by supervised learning algorithms based on UPLC-Q-Orbitrap/HRMS
摘要
Abstract
To objectively evaluate the non-volatile components of domestic and foreign tobacco extracts,UPLC-Q-Orbitrap/HRMS technology was used to conduct a non-targeted analysis of these components in typical tobacco extract samples.Two supervised learning algorithms,orthogonal partial least squares discriminant analysis(OPLS-DA)and support vector machine(SVM),were used to construct models exploring the differences in the non-volatile components of domestic and foreign tobacco extracts.The predictive abilities of these models for leaf origin traceability were then compared.The results showed that:1)A total of 167 non-volatile components were identified and classified into 14 compound categories from the tobacco extracts.The numbers of flavonoids,organic acids,and terpenoids were highest,while the number of esters was relatively low.2)Utilizing the constructed OPLS-DA model,26 characteristic components were screened(VIP＞1 and P＜0.05).Of these,the contents of 11 components,including DL-proline and rutin,were significantly higher in domestic samples than in foreign samples,while the contents of 15 components,such as D-(+)-tryptophan and 5-hydroxymethylfurfural,were significantly higher in foreign samples than in domestic samples.The model was used to identify and trace the domestic and foreign tobacco extracts.The prediction accuracy for single extract samples and flavor base modules was 100.0％and 67.5％,respectively.3)An SVM prediction model was established,and the identification accuracy for the origin traceability of single extract samples reached 100.0％,while the prediction accuracy for flavor base modules reached 75.0％,which was slightly higher than that of the OPLS-DA model.This study provides new analytical methods for identifying the composition of tobacco extracts,and can be used as technical references for identifying and tracing the origin of domestic and foreign tobacco extracts,as well as for their flavoring.关键词
超高效液相色谱-四极杆轨道肼高分辨质谱/烟草提取物/难挥发性成分/监督学习算法/溯源鉴别Key words
Ultra-high performance liquid chromatography-quadrupole/orbitrap high resolution mass spectrometry/Tobacco extract/Non-volatile component/Supervised learning algorithm/Traceability identification分类
轻工纺织引用本文复制引用
张华,朱莹,刘静,叶远青,汪祺,李彦鹏,张媛,朱怀远,廖惠云..基于UPLC-Q-Orbitrap/HRMS结合监督学习非靶向分析国内外烟草提取物难挥发成分差异[J].烟草科技,2025,58(8):1-18,18.基金项目
中国烟草总公司科技项目"废次烟叶的渗透汽化提取分离耦合技术开发及规模化应用"(110202202024) （110202202024）
江苏中烟工业有限责任公司科技创新项目"高分辨质谱分析常用天然植物提取物及其在仿香中的应用"(202410). （202410）
