Modeling Excitation-Emission Fluorescence Matrices with Multidimensional Pattern Recognition Algorithms for Classification of Raisin

With the improvement of living standards, raisins are accepted by an increasing number of people for its abundant nutrients and delicious. The quality of different kinds of raisins is very distinct because of its wide variety, diverse geographical origin, and various manufacturing technology. It is very important to establish scientific and accurate identification of variety of raisins, geographical origin and quality analysis method. These methods can not only ensure good quality of raisins and protect the consumer's interest, but also helpful for the maintenance of the market competition order. Raisin can be measured with three-dimensional fluorescence spectrometry methods, for it contains muti-fluorescent components. In this research, fluorescence components in raisins samples were extracted with microwave method with methanol as solvent. Excitation emission spectra were obtained for 150 raisins samples of different varieties by recording emission from 300 to 700 nm with excitation in the range of 360 720 nm. The fluorescence matrix data were then analyzed by multidimensional pattern recognition methods, such as the multidimensional principal components analysis (M-PCA) multi-dimensional discrimination analysis of least squares (N-PLS-DA) and partial least square based on parallel factor algorithm discrimination analysis (PARAFAC-PLS-DA), to classify the variety of raisin. The result of M-PCA revealed the clustering tendency for the different kinds of raisins, and N-PLS-DA and PARAFAC-PLS-DA could give satisfactory classification results. In comparison, The PLS-DA classification model, constructed from PARAFAC model scores, detected the variety of raisins samples with 100% sensitivity and specificity. The study demonstrated that the excitation emission fluorescence spectrometry combining with multidimensional pattern recognition is a valuable and reliable technique for raisins classification. The results also showed that this method is promising to discriminate the quality and trace the geographical origin of raisins.