A data preprocessing method based on matrix matching for coal analysis by laser-induced breakdown spectroscopy

Abstract

Matrix effects limit the quantitative performance of laser-induced breakdown spectroscopy (LIBS) for coal analysis. In this work, we proposed a data preprocessing method to reduce matrix effects, namely adaptive subset matching (ASM). ASM constructs a series of calibration models based on the similarity of sample matrix properties. Then an unknown sample is assigned to an appropriate model by calculating its matrix property. The proposed method was evaluated on 90 coal samples to determine the carbon content. Results demonstrate that ASM improves the quantitative performance of both multiple linear regression (MLR) and partial least square regression (PLSR). The root mean square error of prediction (RMSEP) is decreased from 6.19% to 3.23% for MLR and from 2.83% to 1.59% for PLSR, respectively. The corresponding mean pulse-to-pulse relative standard deviation (RSD) of prediction is decreased from 13.8% to 8.43% for MLR and from 4.59% to 2.48% for PLSR, respectively. Moreover, the results of calorific value, nitrogen and hydrogen quantification are improved. These results demonstrate that ASM can effectively reduce matrix effects for coal analysis using LIBS.