摘要
本文首先利用电子鼻(E-nose)结合偏最小二乘判别分析(PLSDA)成功判别了8种不同种类和等级的名优绿茶的香气品质.为进一步解释香气质量差异,借助局部极小值背景漂移校正、多尺度高斯平滑以及色谱保留时间校正法对名优绿茶的气相色谱-质谱联用(GC-MS)指纹图谱进行预处理,再利用移动窗口偏最小二乘回归(MWPLSR)将预处理后的指纹图谱与香气品质得分构建谱效关系模型,筛选出21种潜在特征香气物质,最后利用变量加权最小二乘支持向量机(PSO-VWLS-SVM)将特征香气物质的含量与不同绿茶香气质量得分相关联,根据各特征香气物质的贡献率,成功揭示了名优绿茶香气物质与香气品质之间的协同量-组效关系.本文提出的方法为绿茶特征香气品质标志物的筛查和其量效-组效关系研究提供了一种新的策略方法.
This article identifies the aroma quality of eight different types and grades of famous green teas by E-nose combined with partial least squares discriminant analysis (PLSDA).Subsequently,in order to further explain the reasons for the differences in aroma quality,the background drift correction,multi-scale Gaussian smoothing and chromatographic retention time correction methods were employed to preprocess the chromatographic fingerprint of the famous green tea collected from GC-MS technology.Then,the pre-processed fingerprint and the famous green tea aroma score were analyzed to construct spectrum-activity relationship model by moving window partial least squares regression (MWPLSR).As a result,21 kinds of latent characteristic aroma substances were screened out.Finally,the variable weighted least squares support vector (PSO-VWLS-SVM) was used to correlate the content of 21 kinds of latent characteristic aroma substances with the score for the aroma quality of different green tea.According to the contribution rate of each characteristic aroma substance to the score,a synergistic quantitative composition-activity relationship between aroma substances and the aroma quality of the famous green tea was successfully revealed.This method provided a new strategy for screening and quantitative analysis of characteristic aroma substances,as well their synergistic relationship in aroma quality of green tea.
作者
付海燕
时琼
李鹤东
范尧
胡鸥
佘远斌
Haiyan Fu;Qiong Shi;Hedong Li;Yao Fan;Ou Hu;Yuanbin She(School of Pharmacy,South-Central University for Nationalities,Wuhan 430074;College of Chemical Engineering,Zhejiang University of Technology,Hangzhou 310032)
出处
《中国科学：化学》
CAS
CSCD
北大核心
2019年第4期625-636,共12页
SCIENTIA SINICA Chimica
基金
国家自然科学基金(编号:21776321
21576297
21776259)资助项目
关键词
名优绿茶
香气成分
GC-MS
变量加权最小二乘支持向量
协同量效-组效关系
famous green tea
aroma components
GC-MS
variable weighted least squares support vector
synergistic quantitative composition-activity relationship
