Abstract
The potential of FT-IR spectra was examined to classify tea samples based on the geographical origins. Principal component analysis (PCA), principal component analysis-linear discriminant analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were investigated in order to achieve discrimination of tea samples. Several spectral pre-processing methods, such as mean centering (MC), auto-scaling, multiplicative scatter correction (MSC), standard normal variate (SNV) and their combinations, were employed to improve the quality of the spectra. The results showed that the tea samples from five geographical regions can be identified based on using FT-IR spectral fingerprints. The results demonstrated that FT-IR spectral fingerprinting combined with pattern recognition methods can be employed as an effective and feasible method for classification of Iranian tea based on their geographical origins.
Highlights
Tea is considered as one of the most popular non-alcoholic and low cost beverages which is consumed by a great number of people in the world
Principal component analysis (PCA) (Fernandez et al, 2000; He et al, 2007), linear discriminant analysis (LDA) (Chen et al, 2009b; Fernandez et al, 2000), cluster analysis (CA) (Wu et al, 2018), soft independent modeling of class analogy (SIMCA) (Fujiwara et al, 2006) and artificial neural networks (ANNs) (Chen et al, 2009b) are pattern recognition methods, which have been used in most common activities of classification and authentication of tea samples
The potential of FT-infrared spectroscopy (IR) spectroscopy coupled to super vised and unsupervised pattern recognition methods was demonstrated for the classification and traceability of Iranian teas based on their geographical origin
Summary
Tea is considered as one of the most popular non-alcoholic and low cost beverages which is consumed by a great number of people in the world. Principal component analysis (PCA) (Fernandez et al, 2000; He et al, 2007), linear discriminant analysis (LDA) (Chen et al, 2009b; Fernandez et al, 2000), cluster analysis (CA) (Wu et al, 2018), soft independent modeling of class analogy (SIMCA) (Fujiwara et al, 2006) and artificial neural networks (ANNs) (Chen et al, 2009b) are pattern recognition methods, which have been used in most common activities of classification and authentication of tea samples. There fore, in this work, the potential of Fourier transforms infrared (FT-IR) spectroscopy in combination with chemometrics was examined in order to investigate the possibility of differentiation and characterization of Iranian tea according to their geographical origins For this purpose, the FT-IR spectra of tea samples, produced in five Iranian regions were considered. The predictive ability of the constructed models was assessed by the statistical parameters for calibration and prediction set
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