Classification and authentication of tea according to their geographical origin based on FT-IR fingerprinting using pattern recognition methods
Mahnaz Esteki, Neda Memarbashi, Jesús Simal‐Gándara
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.