title: Using FTIR spectra and pattern recognition for discrimination of tea varieties.
abstract: In order to classify typical Chinese tea varieties, Fourier transform infrared spectroscopy (FTIR) of tea polysaccharides (TPS) was used as an accurate and economical method. Partial least squares (PLS) modeling method along with a self-organizing map (SOM) neural network method was utilized due to the diversity and heterozygosis between teas. FTIR spectra results of tea extracts after spectra preprocessing were used as input data for PLS and SOM multivariate statistical analyses respectively. The predicted correlation coefficient of optimization PLS model was 0.9994, and root mean square error of calibration and cross-validation (RMSECV) was 0.03285. The features of PLS can be visualized in principal component (PC) space, contributing to discover correlation between different classes of spectra samples. After that, a data matrix consisted of the scores on the selected 3PCs computed by principle component analysis (PCA) and the characteristic spectrum data was used as inputs for training of SOM neural network. Compared with the PLS linear technique's recognition rate of 67% only, the correct recognition rate of the PLS-SOM as a non-linear classification algorithm to differentiate types of tea reaches up to 100%. And the models become reliable and provide a reasonable clustering of tea varieties.
authors.affiliation: Institute of Food Engineering, College of Life & Environment Science, Shanghai Normal University, 100 Guilin Road, Shanghai 200234, PR China.
authors.forename: Jian-xiong
authors.initials: JX
authors.lastname: Cai
authors.affiliation: Institute of Food Engineering, College of Life & Environment Science, Shanghai Normal University, 100 Guilin Road, Shanghai 200234, PR China. Electronic address: yfwang@shnu.edu.cn.
authors.forename: Yuan-feng
authors.initials: YF
authors.lastname: Wang
authors.affiliation: Institute of Food Engineering, College of Life & Environment Science, Shanghai Normal University, 100 Guilin Road, Shanghai 200234, PR China.
authors.forename: Xiong-gang
authors.initials: XG
authors.lastname: Xi
authors.affiliation: Shanghai Yuanzu Mengguozi Ltd., 6088 Jiasong Road, Zhaoxiang Town, Shanghai 201703, PR China.
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.affiliation: Institute of Food Engineering, College of Life & Environment Science, Shanghai Normal University, 100 Guilin Road, Shanghai 200234, PR China. Electronic address: foodlab2010@163.com.
authors.forename: Xin-lin
authors.initials: XL
authors.lastname: Wei
mesh_terms: D008962:Models, Theoretical; D010936:Plant Extracts / Q000737:chemistry*; D011134:Polysaccharides / Q000737:chemistry; D017550:Spectroscopy, Fourier Transform Infrared; D013662:Tea / Q000737:chemistry*
chemical_list: D010936:Plant Extracts; D011134:Polysaccharides; D013662:Tea
keywords: Artificial neural network; Fourier transform infrared spectroscopy; Partial least squares; Spectra preprocessing; Tea polysaccharides