Species discrimination of β-phenylethylamine, NaCl and NaOH based on Ultraviolet spectroscopy and principal component analysis combined with improved clustering by fast search and find of density peaks algorithm

Abstract

This paper aimed at putting forward an approach integrating the improved clustering by fast search and find of density peaks (I-CFSFDP) algorithm with Ultraviolet (UV) spectroscopy for identifying the species of NaCl, NaOH, β-phenylethylamine(PEA) and their mixtures. For solving the issue that the clustering precision of the CFSFDP algorithm relies on the density forecast of the dataset and the manually selection of the truncated distance d_c. The idea of kernel density forecast was adopted to the I-CFSFDP algorithm. The I-CFSFDP algorithm can observe the clusters of arbitrary shapes and use an adaptive method to evaluate the truncated distance d_c, thereby generating more accurate clusters and identifying the core points in the clusters effectively. The dimensions of the UV spectra was reduced with principal component analysis (PCA), and the results of PCA were invoked as the input of the I-CFSFDP algorithm. Meanwhile, the effect of PCA-I-CFSFDP was evaluated by recall, accuracy, F-Score and precision. Besides, the DBSCAN and PCA-CFSFDP algorithms were used to compare with the PCA-I-CFSFDP algorithm. All of the classification outcomes displayed that the PCA-I-CFSFDP algorithm has better performance than the DBSCAN and PCA-CFSFDP algorithms. Therefore, the PCA-I-CFSFDP algorithm integrated with UV spectroscopy is a simple, quick and credible identification approach for detecting PEA, NaCl, NaOH and the mixtures.