多元数据分析方法在解释GC-MS动植物油脂数据中的应用

doi:10.13560/j.cnki.biotech.bull.1985.2024-0525

摘要/Abstract

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。

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

Abstract:

【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 techniques, as well as HCA, PCA, and OPLS-DA analytical methods. This approach effectively addresses the complexities associated with oil degradation and decay, enabling rapid and precise differentiation of oils from different regions or brands.

Key words: multivariate curve resolution, multivariate data analysis, GC-MS, vegetable oils and animal fats, discrimination

刘平阳, 刘占芳, 周红, 张冠男, 孙振文, 李亚军, 周正, 刘耀. 多元数据分析方法在解释GC-MS动植物油脂数据中的应用[J]. 生物技术通报, 2025, 41(1): 120-131.

LIU Ping-yang, LIU Zhan-fang, ZHOU Hong, ZHANG Guan-nan, SUN Zhen-wen, LI Ya-jun, ZHOU Zheng, LIU Yao. Multivariate Data Analysis in the Interpretation of GC-MS Data of Vegetable Oils and Animal Fats[J]. Biotechnology Bulletin, 2025, 41(1): 120-131.

图/表 7

表1 收集到的动植物油脂样本

Table 1 Vegetable oils and animal fats samples collected in this study

种类Category	产地/品牌 Origin/Brand	样本数量 Samples	分组 Group	种类 Category	产地/品牌 Origin/Brand	样本数量 Samples	分组 Group
Soybean oil	Inner Mongolia	8	A	Tung oil	Henan Province	8	G
	Heilongjiang Province	8			Hubei Province	8
	Anhui Province	8			Shanghai City	8
	Shandong Province	8		Castor oil	Hebei Province	8	H
	Sichuan Province	8			India/ Moutain rose herbs	8
	Guangdong Province	8			Egypt/ Ruifantine	8
	Shanxi Province	8			Hebei Province/Yanwei	8
	Yunnan Province	8			Henan Province/Huangjia	8
	Jilin Province	8			Guangdong Province	8
	Hubei Province	8		Butter	Henan Province	8	I
Olive oil	Spain/Oliveira	8	B		Guangdong Province	8
	Italy/Philippe Berry	8			Australia	8
	Spain/Ajinomoto	8			Inner Mongolia	8
	Spain/Obera	8			Shandong Province	8
	Spain/Earl	8			Beijing City	8
	Spain/Omanti	8			Sichuan Province	8
	Spain/Borges	8		Sheep oil	Shanghai City	8	J
Sunflower oil	Beijing City	8	C		Gansu Province	8
	Ukraine/HADAY	8			Xinjiang Uygur Autonomous Region	8
	Europe/COFCO	8			Jiangsu Province	8
	Europe/Arawana	8			Ningxia Hui Autonomous Region	8
	Ukraine/Dianxue	8			Shandong Province	8
	Russia/Red	8			Inner Mongolia	8
	Ukraine/Qiandaoyuan	8			Beijing City	8
Peanut oil	Shandong Province/Luhua	8	D		Shandong Province	8
	Inner Mongolia	8		Chicken oil	Yongjia/Sichuan Province	8	K
	Shandong Province	8		Chicken oil	Jinluo/Shandong Province	8	K
	Yunnan Province	8		Duck oil	Beijing City	8	L
	Guangdong Province	8		Lard	Inner Mongolia	8	M
	Beijing City	8			Xizang	8
Palm oil	Guangdong Province	8	E		Heilongjiang Province	8
	Indonesia	8			Hubei Province	8
	Brazil/ TMEPEREX	8			Guangdong Province	8
	Thailand/Huarui	8			Beijing City	8
	Malaysia/Tianyijia	8			Guizhou Province	8
Linseed oil	Inner Mongolia	8	F		Sichuan Province	8
	Ningxia Hui Autonomous Region/Haoyu	8			Gansu Province	8
	Ningxia Hui Autonomous Region/Yixiayuan	8			Yunnan Province	8
	China/Yuedushoufang	8
	Russia/Fuyide	8
	China/Qiumanxian	8

表1 收集到的动植物油脂样本

Table 1 Vegetable oils and animal fats samples collected in this study

种类Category	产地/品牌 Origin/Brand	样本数量 Samples	分组 Group	种类 Category	产地/品牌 Origin/Brand	样本数量 Samples	分组 Group
Soybean oil	Inner Mongolia	8	A	Tung oil	Henan Province	8	G
	Heilongjiang Province	8			Hubei Province	8
	Anhui Province	8			Shanghai City	8
	Shandong Province	8		Castor oil	Hebei Province	8	H
	Sichuan Province	8			India/ Moutain rose herbs	8
	Guangdong Province	8			Egypt/ Ruifantine	8
	Shanxi Province	8			Hebei Province/Yanwei	8
	Yunnan Province	8			Henan Province/Huangjia	8
	Jilin Province	8			Guangdong Province	8
	Hubei Province	8		Butter	Henan Province	8	I
Olive oil	Spain/Oliveira	8	B		Guangdong Province	8
	Italy/Philippe Berry	8			Australia	8
	Spain/Ajinomoto	8			Inner Mongolia	8
	Spain/Obera	8			Shandong Province	8
	Spain/Earl	8			Beijing City	8
	Spain/Omanti	8			Sichuan Province	8
	Spain/Borges	8		Sheep oil	Shanghai City	8	J
Sunflower oil	Beijing City	8	C		Gansu Province	8
	Ukraine/HADAY	8			Xinjiang Uygur Autonomous Region	8
	Europe/COFCO	8			Jiangsu Province	8
	Europe/Arawana	8			Ningxia Hui Autonomous Region	8
	Ukraine/Dianxue	8			Shandong Province	8
	Russia/Red	8			Inner Mongolia	8
	Ukraine/Qiandaoyuan	8			Beijing City	8
Peanut oil	Shandong Province/Luhua	8	D		Shandong Province	8
	Inner Mongolia	8		Chicken oil	Yongjia/Sichuan Province	8	K
	Shandong Province	8		Chicken oil	Jinluo/Shandong Province	8	K
	Yunnan Province	8		Duck oil	Beijing City	8	L
	Guangdong Province	8		Lard	Inner Mongolia	8	M
	Beijing City	8			Xizang	8
Palm oil	Guangdong Province	8	E		Heilongjiang Province	8
	Indonesia	8			Hubei Province	8
	Brazil/ TMEPEREX	8			Guangdong Province	8
	Thailand/Huarui	8			Beijing City	8
	Malaysia/Tianyijia	8			Guizhou Province	8
Linseed oil	Inner Mongolia	8	F		Sichuan Province	8
	Ningxia Hui Autonomous Region/Haoyu	8			Gansu Province	8
	Ningxia Hui Autonomous Region/Yixiayuan	8			Yunnan Province	8
	China/Yuedushoufang	8
	Russia/Fuyide	8
	China/Qiumanxian	8

图1 动植物油脂的色谱与质谱特征分析及其地域差异 A：老化前13种动植物油脂的3D总离子流色谱图；B：棕榈酸甲酯的质谱图；C：硬脂酸甲酯的质谱图；D：油酸甲酯的质谱图；E：亚油酸甲酯的质谱图； F：老化前江苏、新疆和山东地区羊油脂色谱图比较

Fig. 1 Analysis of the chromatographic and mass spectrometric characteristics of vegetable oils and animal fats and their regional differences A: TIC of 13 different types of vegetable oils and animal fats before aging. B: Mass spectrometry of methyl palmitate. C: Mass spectrometry of methyl stearate. D: Mass spectrometry of methyl oleate. E: Mass spectrometry of methyl linoleate. F: Comparison of chromatograms of sheep oil from Jiangsu Province, Xinjiang Uygur Autonomous Region, and Shandong Province before aging

图2 不同老化条件下橄榄油脂脂肪酸组成及其变化的GC-MS分析 A：在60℃恒温箱中，0、6、12、18、24、30和36 d的橄榄油脂脂肪酸含量变化；B：在60℃恒温箱中，0、6、12、18、24、30和36 d的橄榄油脂饱和脂肪酸（SFA）和不饱和脂肪酸（UFA）变化趋势；C：新鲜橄榄油脂的GC-MS色谱图；D-F：在实验台上空气（25℃）暴露6、18和36 d的老化橄榄油脂GC-MS色谱图；G-I：在恒温箱（60℃）中6、18、36 d的老化橄榄油脂GC-MS色谱图。载体包括A4纸、瓦楞纸、海绵、土壤和地板革，数据取它们的平均值（n=3）

Fig. 2 GC-MS analysis of fatty acid composition and its changes of olive oil under different aging conditions A: Changes of fatty acid composition of olive oil at 0, 6, 12, 18, 24, 30, and 36 d in the incubator of 60℃. B: The trend of changes of saturated fatty acids and unsaturated fatty acids of olive oil at 0, 6, 12, 18, 24, 30, and 36 d in the incubator of 60℃. C: GC-MS chromatogram of fresh olive oil. D-F: GC-MS chromatogram of aged olive oil placed on the experimental bench after 6, 18, and 36 d of indoor air exposure. G-I: GC-MS chromatogram of aged olive oil placed in the incubator of 60℃ after 6, 18, and 36 d. The carrier includes A4 paper, corrugated paper, sponge, soil, and floor leather, and the data are taken as their average values（n=3）

图3 数据不平衡处理原理示意图 A：SMOTE生成样本示意图；B：Tomek links原理示意图；C：SMOTE-Tomek links的原理示意图

Fig. 3 Schematic diagram of data imbalance handling principles A: Schematic diagram of SMOTE generated samples. B: Schematic diagram of Tomek links. C: Schematic diagram of SMOTE-Tomek links

图4 动植物油脂的层次聚类图 a：13种动植物油脂的层次聚类图；b：5种动物油脂的层次聚类图；c：8种植物油脂的层次聚类图。载体包括A4纸、瓦楞纸、海绵、土壤和地板革，数据取它们的平均值（n=3）；A：大豆油脂；B：橄榄油脂；C：葵花籽油脂；D：花生油脂；E：棕榈油脂；F：亚麻籽油脂；G：桐油脂；H：蓖麻油脂；I：牛油脂；J：羊油脂；K：鸡油脂；L：鸭油脂；M：猪油脂

Fig. 4 HCA of vegetable oils and animal fats a: HCA of 13 different types of vegetable oils and animal fats. b: HCA of 5 different types of animal fats. c: HCA of 8 different types of vegetable oils. The carrier includes A4 paper, corrugated paper, sponge, soil, and floor leather, and the data is taken as the average value（n=3）. A: Soybean oil. B: Olive oil. C: Sunflower oil. D: Peanut oil. E: Palm oil. F: Linseed oil. G: Tung oil. H: Castor oil. I: Butter. J: Sheep oil. K: Chicken oil. L: Duck oil. M: Lard

图5 动植物油脂的HELP解析及其主成分分析 A：老化（60℃ 36 d）桐油脂的GC-MS总离子流色谱图及局部放大图；B：老化桐油脂24.967-25.127 min的总离子流色谱图；C：老化桐油脂24.967-25.127 min的二维数据图；D：老化桐油脂24.967-25.127 min的秩图[虚线下方的线表示噪声水平，虚线上方的线表示特征值情况（化合物数量）]；E：经过HELP方法解析后纯组分色谱图；F：在5种载体（海绵、A4纸、地板革、土壤和瓦楞纸）上老化13种动植物油脂的PCA得分图，R2X（cum）= 0.859，Q2（cum）= 0.815；G：其所对应的PCA载荷图。载体包括A4纸、瓦楞纸、海绵、土壤和地板革，数据取它们的平均值（n=3）

Fig. 5 HELP analysis of vegetable oils and animal fats and their principal component analysis A: GC-MS total ion flow chromatogram and local magnification of aged（60℃ 36 d）tung oil. B: Total ion flow chromatogram of aged tung oil between 24.967-25.127 min. C: Two-dimensional data graph of aged tung oil between 24.967-25.127 min. D: Rank plot of aged tung oil between 24.967-25.127 min[Lines below the dotted line indicate noise level, and lines above dotted line indicate the local ranks（Number of compounds）]. E: The chromatogram of pure compound was analyzed by the HELP method. F: PCA score plot of 13 vegetable oils and animal fats aged on 5 carriers（sponge, A4 printer paper, floor leather, soil, and corrugated paper）, with R2X（cum）=0.859 and Q2（cum）=0.815. G: The corresponding PCA load diagram. The carrier includes A4 paper, corrugated paper, sponge, soil, and floor leather, and the data are taken as their average values（n=3）

图6 多省份老化（60℃ 36 d）羊油脂的PCA与OPLS-DA A：8省（甘肃、内蒙古、宁夏、江苏、新疆、北京、上海和山东）羊油脂（60℃ 36 d）的PCA模型；B：8省（甘肃、内蒙古、宁夏、江苏、新疆、北京、上海、山东）羊油脂（60℃ 36 d）的OPLS-DA模型；C：OPLS-DA模型置换检验结果图；D：OPLS-DA的模型参数图。载体包括A4纸、瓦楞纸、海绵、土壤和地板革，数据取它们的平均值（n=3）

Fig. 6 PCA and OPLS-DA of aged sheep oil（60℃ 36 d）in 8 provinces A: PCA model of aged sheep oil（60℃ 36 d）in 8 provinces（Gansu, Inner Mongolia, Ningxia, Jiangsu, Xinjiang, Beijing, Shanghai, and Shandong）. B: OPLS-DA model of aged sheep oil（60℃ 36 d）in 8 provinces（Gansu, Inner Mongolia, Ningxia, Jiangsu, Xinjiang, Beijing, Shanghai, and Shandong）. C: Permutation test result of the OPLS-DA model. D: Model parameter of OPLS-DA. The carrier includes A4 paper, corrugated paper, sponge, soil, and floor leather, and the data are taken as their average values（n=3）

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