Abstract
A new assay of identifying wines was developed based on fingerprints of three-dimensional fluorescence spectra, and 30 samples from different manufacturers were analyzed. The techniques of principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to differentiate and evaluate the character parameters of wines’ three-dimensional fluorescence spectra. At the same time, the back-propagation network (BPN) was applied to predict the attribution of unknown samples. The results of PCA and HCA showed that there was definite different information among the wine samples from different manufacturers. It was promising that the method could be applied to distinguish wine samples produced by different manufacturers. The proposed method could provide the criterion for the quality control of wines.
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