基于气相离子迁移谱技术的不同产地乌龙茶挥发性物质分析

Analysis of Volatile Substance of Oolong Tea from Different Origins Based on Gas Chromatography-Ion Mobility Technique

摘要:

为实现不同产地乌龙茶中挥发性风味物质的快速检测，采用气相色谱离子迁移谱（gas chromatography-ion mobility spectrometry，GC-IMS）联用技术结合化学计量学方法对我国4个不同产地的29个乌龙茶样品的挥发性物质进行了研究。不同产地乌龙茶样本的IMS图谱显示，挥发性物质含量存在显著差异。呈柑橘香的柠檬烯在广东乌龙茶中含量较高；闽南地区乌龙茶中具有青草香特征成分的庚醛、己醛、3-辛酮含量明显高于其他产地的乌龙茶；台湾地区乌龙茶中具有果香气特征成分的2-己酮、丙酸、γ-丁内酯、正己醛等含量较高；闽北地区乌龙茶中具有烘烤香呈香特性的反-2，4-庚二烯醛、对乙基苯酚、5-甲基糠醛含量较高。采用差谱法从谱图中筛选出43种挥发性化合物，以峰值强度作为变量，通过主成分分析（principal component analysis，PCA）进行降维，能对不同产地乌龙茶进行区分，其中PC1的贡献率为54.2%，PC2的贡献率为19.4%，累计贡献率为73.6%；使用偏最小二乘判别分析（partial least squares discriminant analysis，PLS-DA），其中PC1的贡献率为54.2%，PC2的贡献率为19.3%，累计贡献率为73.5%，各产地乌龙茶区分效果较好；使用线性判别分析（linear discriminant analysis，LDA），LDA判别率为100%，可以有效判别不同产地乌龙茶。表明GC-IMS技术可以有效应用于乌龙茶挥发性物质的检测及产地的快速区分。

Abstract:

In order to realize the rapid detection of volatile flavor substances in oolong tea samples from different producing areas, the gas chromatography-ion mobility (GC-IMS) technique combined with the stoichiometry was employed to study the volatile compounds in 29 samples of oolong tea from 4 different origins. The IMS maps of oolong tea samples from different origins showed significant differences in volatile substances. The citrus limonene content was the highest in the oolong tea from Guangdong Province, and the contents of heptanal, hexanal and 3-octanone with characteristic components of herbaceous aroma in the oolong tea from southern Fujian were obviously higher than those of oolong tea from other producing areas. The oolong tea from Taiwan Province had more fruit aroma characteristic components, such as 2-hexanone, propionic acid, γ-butyllactone, n-hexanal and so on. The oolong tea from northern Fujian had more baked fragrance like trans-2,4-heptyl diene aldehyde, p-ethyl phenol and 5-methyl furfural. The peak strength of 43 kinds of volatile compounds screened by the differential spectrum method were set as the variables, and dimension reduction was done by the principal component analysis (PCA), which could distinguish oolong tea from different producing areas. The contribution of PC1 and PC2 were 54.2% and 19.4%, respectively, and the cumulative contribution rate was 73.6%. The results of partial least squares discriminant analysis (PLS-DA) showed that the contribution rate of PC1 and PC2 were 54.2% and 19.3%, respectively, and the cumulative contribution rate was 73.5%, which had good distinguishing effect on the oolong tea from different producing areas. The linear discriminant analysis (LDA) could effectively distinguish oolong tea from different producing areas with the discriminant rate of 100%. The experiment showed that GC-IMS technology could be effectively applied to detect the volatile substances in oolong tea and rapidly identify their origin.