Identification of Coal and Gangue Using Visible-Near Infrared (Vis-NIR) Hyperspectral Imaging

Abstract

Accurate and rapid identification of coal and gangue is a crucial issue for efficient resource utilization and environmental protection. In this study, visible and near infrared (Vis-NIR) hyperspectral imaging was used for the classification of coal and gangue. Principal component analysis was adopted to generate score scatter plots for differentiating specific grouping of samples. Competitive adaptive reweighted sampling and random frog (RF) were employed to screen out the effective wavelengths. Three models, partial least squares, a library for support vector machines, and back-propagation (BP) neural network were developed to identify coal and gangue using effective wavelengths and full spectra as inputs. The results showed that all models provided satisfactory identification rates of over 91.6%. The best performances emerged from BP neural network using effective wavelengths extracted by RF with identification accuracy of 100% in validation sets. It can be concluded that Vis-NIR hyperspectral imaging is a noninvasive analytical method with promising application prospects for coal and gangue identification.