荧光指纹图谱结合交替三线性分解算法和模式识别方法用于不同品类茶叶的分类
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- 英文论文题名：Fluorescence fingerprint coupled with alternating trilinear decomposition and pattern recognition algorithms for classification of tea varieties
- 论文作者：胡勇 ; 吴海龙 ; 尹小丽 ; 谷惠文 ; 肖蓉 ; 方焕 ; 俞汝勤
- 英文论文作者：Yong Hu ; Hai-Long Wu ; Xiao-Li Yin ; Hui-Wen Gu ; Rong Xiao ; Ru-Qin Yu ; State Key Laboratory of Chemo/Biosensing and Chemometrics ; College of Chemistry and Chemical Engineering ; Hunan University
- 年：2016
- 作者机构：湖南大学化学生物传感与计量学国家重点实验室;
- 论文关键词：荧光指纹图谱 ; 茶叶 ; 模式识别 ; 交替三线性分解算法
- 会议召开时间：2016-07-01
- 会议录名称：中国化学会第30届学术年会摘要集-第二十五分会：化学信息学与化学计量学
- 语种：中文
- 分类号：TS272.7;O657.3
- 学会代码：ZGHY
- 会议名称：中国化学会第30届学术年会-第二十五分会 ; 化学信息学与化学计量学
- 会议地点：中国辽宁大连
- 主办单位：中国化学会
- 学会名称：中国化学会
- 页数：1
- 文件大小：203k
- 原文格式：D
- 会议级别：全国
摘要
茶是当今消费最多的饮料之一。根据按焙火程度来分类,茶叶可以分成六大类:绿茶、红茶、乌龙茶、黑茶、黄茶、白茶。茶叶中含有大量的具有天然抗氧化性的酚类物质可以增进人的健康,其中的茶氨酸有降血压的作用,同时还可以为人体提供重要的生物必需元素。随着生活水平的提高,人们对茶叶的质量要求越来越高,如何合理地选择茶叶已经越来越受到人们的关注。研究简单、快速和正确率高的茶叶分类及其质量控制方法已成为科研工作者的一个重要任务。当今,茶叶的识别方法主要采用高效液相色谱法、气相色谱法、等离子光谱法、近红外光谱法、核磁共振法和高光谱成像法等。本文采用茶叶荧光指纹图谱结合交替三线性分解算法(ATLD)和模式识别方法对不同类别的茶叶进行分类。46个茶叶样本的荧光指纹图谱通过三维荧光扫描200-390 nm的激发波长和270-700 nm的发射波长获得。激发发射矩阵荧光数据构成的三维数阵用5组分的ATLD成功建模,随后把46个茶叶样本随机分成训练集和预测集并应用偏最小二乘法判别分析(PLS-DA)对5组分ATLD获得的相对浓度信息构建分类模型,实现了茶叶样本的准确分类和识别。结果表明,荧光指纹图谱结合化学计量学工具能够很好地提取茶叶的化学信息,并且能够准确地分类和识别不同的茶叶品类。
Tea,one of the most consumed beverages all over the world,is of great importance in the economies of a number of countries.Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data,such as metal profile,amino acids,catechins and volatile compounds.Some of these analytical methods become tedious and expensive to be applied in routine works.In this work,fluorescence fingerprint coupled with alternating trilinear decomposition(ATLD) and pattern recognition algorithms were used for characterization and classification of tea varieties.Excitation-emission matrix fluorescence(EEMs) spectra were obtained from 46 tea samples of different types by recording emission wavelength from 270 to 700 nm with excitation wavelength in the range of 200-390 nm.A five component ATLD model best explained the variability of fluorescence characteristics of 46 tea samples.And then a PLS-DA classification model,constructed from ATLD model scores,classified different tea varieties with high sensitivity and specificity.This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.
Tea,one of the most consumed beverages all over the world,is of great importance in the economies of a number of countries.Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data,such as metal profile,amino acids,catechins and volatile compounds.Some of these analytical methods become tedious and expensive to be applied in routine works.In this work,fluorescence fingerprint coupled with alternating trilinear decomposition(ATLD) and pattern recognition algorithms were used for characterization and classification of tea varieties.Excitation-emission matrix fluorescence(EEMs) spectra were obtained from 46 tea samples of different types by recording emission wavelength from 270 to 700 nm with excitation wavelength in the range of 200-390 nm.A five component ATLD model best explained the variability of fluorescence characteristics of 46 tea samples.And then a PLS-DA classification model,constructed from ATLD model scores,classified different tea varieties with high sensitivity and specificity.This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.
引文
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[2]Palacios-Morillo,A.;Alcazar,A.Spectrochim.Acta.Pt.A:Mol.Biomol.Spectrosc.2013,103:1386-1425.
[3]Coelho,C;Aron,A.;Roullier-Gall,C;Gonsior,M.;Schmitt-Kopplin,P.Anal.Chem.2015,87(16):8132-7.