부산대학교 도서관

Laser-induced breakdown spectroscopy (LIBS), an emerging elemental analysis technique, provides a fast and low-cost solution for coal characterization without complex sample preparation. However, LIBS spectra contain a large number of uninformative variables, resulting in reduction in the predictive ability and learning speed of a multivariate model. Variable selection based on a single criterion usually leads to a lack of diversity in the selected variables. Coupled with spectral uncertainty in LIBS measurements, this can degrade the reliability and robustness of the multivariate model when analysing spectra obtained at different times and conditions. This work proposes a validated ensemble method for variable selection which uses six base algorithms and combines the returned variable subsets based on the cross-validation results. The proposed method is tested on two sets of LIBS spectra obtained within one month under variable experimental conditions to quantify the properties of coal, including fixed carbon, volatile matter, ash, calorific value and sulphur. The results show that the multivariate model based on the proposed method outperforms those using benchmark variable selection algorithms in six out of the seven tasks by 0.3%–2% in the coefficient of determination for prediction. This study suggests that variable selection based on ensemble learning improves the predictive ability and computational efficiency of the multivariate model in coal property analysis. Moreover, it can be used as a reliable method when the user is not sure which variables to choose in LIBS application. [ABSTRACT FROM AUTHOR]