In this paper, we demonstrated a detective system to evaluate the quality and classification of different tea samples based
on multi-wavelength LED-induced fluorescence spectroscopy. By utilizing multiple excitation wavelengths, we obtained
much more physical and chemical information from the detected samples than single excitation wavelength. By utilizing
principal component analysis (PCA), we extracted the dominant features of the samples to classify and characterize the
tea samples.
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