Systematic comparison of unsupervised feature extraction methods for enhancing discrimination accuracy of brown rice flour adulteration in LIBS

Abstract

Laser-induced breakdown spectroscopy (LIBS) is widely applied in various critical fields due to its technical advantages. However, with the explosive growth in spectral data volume, the challenges of high-dimension and redundant information of spectra are becoming increasingly prominent. Therefore, dimensionality reduction of spectra is crucial for LIBS. In this study, to enhance the discrimination accuracy of brown rice flour and its adulterants, a systematic comparative analysis of eight unsupervised feature extraction methods was conducted, including principal component analysis (PCA), non-negative matrix factorization (NMF), independent component analysis (ICA), multidimensional scaling (MDS), kernel PCA (KPCA), locally linear embedding (LLE), Laplacian eigenmaps (LE), and isometric mapping (ISOMAP). The results indicate that linear feature extraction methods (PCA, NMF, ICA, and MDS) show a more obvious improvement in the analytical performance of the classification models, compared to nonlinear feature extraction methods (KPCA, LLE, LE, and ISOMAP). Among these methods, the commonly used PCA has the best improvement effect. The accuracy, macro precision, macro recall, and macro F1-score of the classification models improve from 92.73%, 94.13%, 92.92%, and 0.93 to 95.52%, 95.67%, 95.63%, and 0.96. Therefore, this study can provide experience and guidance for selecting appropriate unsupervised feature extraction methods in subsequent research, contributing to the further application and development of LIBS.