摘要
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。
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.
作者
尹洪旭
杨艳芹
姚月凤
张铭铭
王家勤
江用文
袁海波
YIN Hongxu;YANG Yanqin;YAO Yuefeng;ZHANG Mingming;WANG Jiaqin;JIANG Yongwen;YUAN Haibo(Key Laboratory of Tea Processing Engineer of Zhejiang Province,Tea Research Institute,Chinese Academy of Agricultural Sciences,Hangzhou 310008,China;Graduate School of Chinese Academy of Agriculture Sciences,Beijing 100081,China)
出处
《食品科学》
EI
CAS
CSCD
北大核心
2019年第4期192-198,共7页
Food Science
基金
国家自然科学基金面上项目(31471651)
浙江省自然科学基金项目(LQ18C160006)
中国农业科学院创新工程项目(CAAS-ASTIP-2014-TRICAAS)
关键词
气相色谱-质谱
主成分分析
层次聚类分析
偏最小二乘判别分析
gas chromatography-mass spectrometry(GC-MS)
principal component analysis(PCA)
hierarchical cluster analysis(HCA)
partial least squares-discriminant analysis(PLS-DA)
