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利用傅里叶变换红外光谱和化学计量数据分析预测沱茶的年份和种类。

Predicting the age and type of tuocha tea by fourier transform infrared spectroscopy and chemometric data analysis.

机构信息

College of Chemistry and Chemical Engineering, Anyang Normal University , Anyang 455002, People's Republic of China.

出版信息

J Agric Food Chem. 2011 Oct 12;59(19):10461-9. doi: 10.1021/jf2026499. Epub 2011 Sep 19.

DOI:10.1021/jf2026499
PMID:21899255
Abstract

Fourier transform infrared (FTIR) spectroscopy combined with chemometric multivariate methods was proposed to discriminate the type (unfermented and fermented) and predict the age of tuocha tea. Transmittance FTIR spectra ranging from 400 to 4000 cm(-1) of 80 fermented and 98 unfermented tea samples from Yunnan province of China were measured. Sample preparation involved finely grinding tea samples and formation of thin KBr disks (under 120 kg/cm(2) for 5 min). For data analysis, partial least-squares (PLS) discriminant analysis (PLSDA) was applied to discriminate unfermented and fermented teas. The sensitivity and specificity of PLSDA with first-derivative spectra were 93 and 96%, respectively. Multivariate calibration models were developed to predict the age of fermented and unfermented teas. Different options of data preprocessing and calibration models were investigated. Whereas linear PLS based on standard normal variate (SNV) spectra was adequate for modeling the age of unfermented tea samples (RMSEP = 1.47 months), a nonlinear back-propagation-artificial neutral network was required for calibrating the age of fermented tea (RMSEP = 1.67 months with second-derivative spectra). For type discrimination and calibration of tea age, SNV and derivative preprocessing played an important role in reducing the spectral variations caused by scattering effects and baseline shifts.

摘要

傅里叶变换红外(FTIR)光谱结合化学计量多元方法被提出用于区分沱茶的类型(未发酵和发酵)并预测其陈化年份。本研究测定了来自中国云南省的 80 个发酵和 98 个未发酵茶样本的透射 FTIR 光谱,范围在 400 到 4000 cm(-1)。样品制备涉及将茶叶样品精细研磨并形成薄的 KBr 圆盘(在 120 kg/cm(2)下压制 5 min)。为了数据分析,应用偏最小二乘判别分析(PLSDA)来区分未发酵和发酵茶。一阶导数光谱的 PLSDA 的灵敏度和特异性分别为 93%和 96%。建立了多元校准模型来预测发酵和未发酵茶的陈化年份。研究了不同的数据预处理和校准模型选项。线性 PLS 基于标准正态变量(SNV)光谱对于建模未发酵茶样本的陈化年份是足够的(RMSEP = 1.47 个月),而对于发酵茶的陈化年份则需要非线性反向传播人工神经网络(RMSEP = 1.67 个月,二阶导数光谱)。对于类型判别和茶龄校准,SNV 和导数预处理在减少散射效应和基线漂移引起的光谱变化方面发挥了重要作用。

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