基于气相色谱-质谱技术与多元统计分析对不同栗香特征绿茶判别分析

采用气相色谱-质谱联用技术结合多元统计分析主成分分析(principal component analysis,PCA)、偏最小二乘法-判别分析(partial least squares-discriminant analysis,PLS-DA)和系统聚类分析(hierarchical clusteranalysis,HCA)对18个不同栗香特征的绿茶开展研究。结果表明,PCA、PLS-DA和HCA均可直观地对3种不同栗香特征的绿茶进行有效区分;PLS-DA中,18个栗香茶样基于其香气特征实现良好分离,其中R2Y=0.843、Q2=0.694,说明该模型对3种栗香特征绿茶具有良好的稳定性和较好的预测能力;HCA中,3种栗香绿茶在聚类距离12处被清晰地分成3类,其中板栗香型和嫩栗香型距离更接近,聚类效果和感官辨识基本一致。此外,基于变量投影重要性大于1,筛选出了38种区分不同栗香特征的重要挥发性组分。

Discrimination of Different Characteristics of Chestnut-like Green Tea Based on Gas Chromatography-Mass Spectrometry and Multivariate Statistical Data Analysis

The volatile constitutions of 18 green tea samples with three different types of characteristic chestnut-like aroma were characterized based on gas chromatography-mass spectrometry (GC-MS) combined with multivariate statistical data analysis including principal component analysis (PCA), hierarchical cluster analysis (HCA) and partial least squares-discriminant analysis (PLS-DA). The results showed that PCA, PLS-DA and HCA could achieve good differentiation of three chestnut flavored green teas. In the PLS-DA analysis, 18 chestnut-like green tea samples were well separated according to their aroma characteristics, and the well-explained variance (R2Y =0.843) and cross-validated predictive capability (Q2 = 0.694) indicated the model’s good feasibility. In the HCA analysis, three kinds of chestnut fragrant green tea could be clearly divided into three categories at a distance of 12, of which the chestnut-like and tender chestnut-like tea samples were closer, matching the results of sensory evaluation. In addition, 38 volatile components were identified based on variable importance in projection (VIP) score > 1, which were responsible for the discrimination of green teas with three different flavor characteristics.