Discrimination of tea varieties from different countries is demanding task and to address this requirement, we demonstrate the feasibility of using x-ray fluorescence (XRF) spectrometry combined with chemometric, to classify geographical origin of different based on their elemental contents. Both direct non-pre-treatment XRF spectra and elemental concentration were used to achieve discrimination of tea. The classification was carried out on the basis of chemical information contained in tea samples. In total, 13 elements (Mg, Al, P, S, Rb, Sr, K, Ca, Mn, Fe, Cu, Zn, As, Pb) were used as chemometric descriptors for classification purpose of tea types based to their geographical origin. Different pattern recognition techniques such as singular value decomposition (SVD), hierarchical clustering analysis (HCA) and artificial neural network (ANN) were applied to differentiate tea varieties. The most dominant element descriptors features were calcium Ca, potassium K, manganese Mn and iron Fe.
Key words: Portable X-ray fluorescence, tea, singular value decomposition; hierarchical clustering analysis, artificial neural network.
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