A Novel Two-Step Spectral Recovery Framework for Coal Quality Assessment by Near-Infrared Spectroscopy

Near-infrared spectroscopy (NIRS) is an effective and efficient technique for evaluating coal quality. The original spectra might be contaminated by scattering interference and random noise. We propose a novel artifact removal framework to recover the buried information and to overcome limitations of currently available preprocessing techniques, such as the multiplicative scatter correction (MSC), as well as a smoothing process. The two-step framework is mainly constructed by MSC and Savitzky–Golay convolution (S-SGC) . Moreover, a particle swarm optimization (PSO) algorithm is used to search the optimal parameters within the framework. In addition, the spectra are collected from coal samples with different particle sizes (i.e., 0.2 and 3 mm), which may carry different characteristics and interfering information. We have analyzed seven kinds of coal properties, such as moisture (%), ash (%), volatile matter (%), and heating value (MJ/kg) via partial least square regression (PLSR) models in order to verify the effectiveness of the proposed method. The results show that the proposed two-step method provides superior performances for zooming in the spectral characteristic peaks and filtering the random noise simultaneously, which mainly benefits from the appropriate combination of MSC and S-SGC.