多元数据分析方法在解释GC-MS动植物油脂数据中的应用  

Multivariate Data Analysis in the Interpretation of GC-MS Data of Vegetable Oils and Animal Fats

作　　者：刘平阳 刘占芳 周红 张冠男 孙振文 李亚军 周正 刘耀[1,2] LIU Ping-yang;LIU Zhan-fang;ZHOU Hong;ZHANG Guan-nan;SUN Zhen-wen;LI Ya-jun;ZHOU Zheng;LIU Yao(School of Criminal Investigation,People's Public Security University of China,Beijing 100038;Institute of Forensic Science,Ministry of Public Security,Beijing 100038)

机构地区：[1]中国人民公安大学侦查学院,北京100038 [2]公安部鉴定中心,北京100038

出　　处：《生物技术通报》2025年第1期120-131,共12页Biotechnology Bulletin

基　　金：中央级基本科研业务费项目(2024JB014)。

摘　　要：【目的】开发一种基于气相色谱-质谱联用技术(gas chromatography-mass spectrometry, GC-MS)结合多元分辨与多元数据分析的综合方法,以实现对法庭科学中常见老化动植物油脂的快速、准确鉴别,特别是针对腐败降解后脂肪酸组成复杂、难以通过传统谱图比对区分的样品。【方法】首先,采用直观推导式演进投影法(heuristic evolving latent projection, HELP)对GC-MS采集的复杂重叠峰进行解析,分离并提取出动植物油脂中各化学组分的纯色谱图和纯质谱图。随后,运用层次聚类分析(hierarchical cluster analysis, HCA)和主成分分析(principal component analysis, PCA)两种无监督学习方法,对附着于5种不同载体上、经60℃老化36 d后的13种动植物油脂的GC-MS数据进行降维和聚类分析,以探索其种属间的差异。进一步地,采用正交偏最小二乘判别分析(orthogonal partial least squares-discriminant analysis, OPLS-DA)这一有监督学习方法,对油脂样品的地域来源及品牌进行快速鉴别。【结果】HCA和PCA分析结果显示,该方法能够有效区分出老化后动植物油脂的种属类别,但在进一步区分不同地区或品牌的油脂时存在局限性。而OPLS-DA模型则展现出更高的分类精度,成功实现了对不同地区或品牌老化动植物油脂的快速准确鉴别。【结论】通过GC-MS结合HELP多元分辨技术及HCA、PCA、OPLS-DA分析方法,为法庭科学中老化动植物油脂的鉴别提供了一种高效、准确的技术方案。该方法有效解决了油脂腐败降解复杂性问题,并实现了对不同地区或品牌油脂的快速准确鉴别。【Objective】This study is aimed to develop a comprehensive approach based on gas chromatography-mass spectrometry(GC-MS)combined with multivariate resolution and multivariate data analysis to achieve rapid and accurate identification of commonly encountered aged vegetable oils and animal fats in forensic science,particularly for samples with complex fatty acid compositions post-degradation that are difficult to distinguish through traditional spectral comparison.【Method】Firstly,the heuristic evolving latent projection(HELP)method was employed to resolve complex overlapping peaks acquired by GC-MS,enabling the separation and extraction of pure chromatograms and mass spectra of individual chemical components in vegetable oils and animal fats.Subsequently,two unsupervised learning methods,hierarchical cluster analysis(HCA)and principal component analysis(PCA),were applied to reduce the dimensionality and perform cluster analysis of GC-MS data from 13 different types of aged vegetable oils and animal fats(aged at 60℃for 36 d)attached to five different carriers,with the aim of exploring differences among species.Furthermore,orthogonal partial least squares-discriminant analysis(OPLS-DA),a supervised learning method,was utilized for rapid identification of the geographical origin and brand of the fat and oil samples.【Result】The analyzed results via HCA and PCA indicated that this approach effectively differentiated the species categories of aged vegetable oils and animal fats.However,limitations were observed in further distinguishing fats and oils from different regions or brands.In contrast,the OPLS-DA model demonstrated higher classification accuracy,successfully achieving rapid and accurate identification of aged vegetable oils and animal fats from various regions or brands.【Conclusion】This study provides an efficient and accurate technical solution for the identification of aged vegetable oils and animal fats in forensic science through the integration of GC-MS with HELP multivariate resolution te

关 键 词：多元分辨技术 多元数据分析 气相色谱-质谱联用 动植物油脂 鉴别 

分 类 号：D918.9[政治法律—法学] TS225[轻工技术与工程—粮食、油脂及植物蛋白工程] O657.63[轻工技术与工程—食品科学与工程]