Exploiting Data Uncertainty for Improving the Performance of a Quantitative Analysis Model for Laser-Induced Breakdown Spectroscopy.

The accuracy and precision of laser-induced breakdown spectroscopy (LIBS) quantitative analysis are significantly limited by the spectral noise. Normalization and ensemble averaging of multiple spectra were often used to preprocess spectra. However, these methods cannot completely remove the spectral noise. Data uncertainty due to the irremovable spectral noise will affect LIBS quantitative analysis. Therefore, this paper proposes a method using data uncertainty to improve the performance of LIBS quantitative analysis. The proposed method uses several spectra to characterize each sample to preserve some data uncertainty in the calibration data matrix. Thus, the data uncertainty is used to optimize the calibration model for improving the toleration to the spectral signal variation. As a result, the optimized calibration model had better accuracy and robustness than the calibration model trained by conventional method. The best root mean square error of prediction (RMSEP) of the ash content of coal was 1.152% for the optimized calibration model, while that for the conventional calibration model was 1.718%. The optimized calibration model also showed a lower relative standard deviation (RSD) value of repeated predictions. Moreover, the calibration model for predicting the ash content in biomass was also optimized by the proposed method. The optimized calibration model outperformed the conventional calibration model again, which demonstrated the extensive applicability of the proposed method.

Optimizing the quantitative analysis of solid biomass fuel properties using laser induced breakdown spectroscopy (LIBS) coupled with a kernel partial least squares (KPLS) model.

The rapid analysis of fuel properties is important for the utilization of solid biomass due to its great variation in feedstock. Laser-induced breakdown spectroscopy (LIBS) technology combined with quantitative analysis models can be used for this analysis. Most existing prediction models used in LIBS for fuel property analysis are linear methods, such as the partial least squares (PLS) model, which fail to reflect the non-linear relationships between the LIBS spectrum and the fuel property index being predicted. In the present work, LIBS data combined with a kernel partial least squares (KPLS) method are used to analyze the gross calorific value, and the volatile matter, ash and fixed carbon content of the solid biomass fuel. Quantitative analysis performance of the KPLS model was compared to that of the widely used PLS method, with the results showing some improvements. The KPLS model was further improved using three data normalization methods (i.e., C internal standardization, total intensity standardization and standard normal variate). The best quantitative analysis results of the volatile matter and ash content were obtained when the KPLS model was combined with C internal standardization, with root mean square errors of prediction (RMSEP) of 1.365% and 0.290%, and average standard deviations (ASD) of 0.277% and 0.080%, respectively. The best quantitative analysis results of the gross calorific value and fixed carbon content were obtained when using KPLS without normalization. The RMSEP and ASD of the gross calorific value and fixed carbon content were 0.198 MJ kg-1 and 0.746%, and 0.070 MJ kg-1 and 0.111% respectively.

Optimizing analysis of coal property using laser-induced breakdown and near-infrared reflectance spectroscopies.

Coal properties have different correlations with elements or molecules. It is difficult to optimize the analysis of multiple coal properties simultaneously by a single analytical technique. This paper reports a method for optimizing analysis of coal properties by using laser-induced breakdown spectroscopy (LIBS) and near-infrared reflectance spectroscopy (NIRS). Briefly, LIBS, NIRS, as well as spectral information fusion of LIBS and NIRS (LIBS&NIRS) were used to establish the quantitative analysis models of coal properties with partial least squares (PLS) method. The performance of models based on different spectral information was compared with each other according to the determination coefficient (R2), root mean square error of prediction (RMSEP), average absolute error (AAE), and average relative error (ARE). As a result, the models of calorific value and volatile matter based on LIBS&NIRS have the best performance with minimum root mean square error for prediction (RMSEP) of 0.192 MJ/kg and 0.672%. However, for the model of ash content, the minimum RMSEP of 0.774% was achieved by using LIBS. Meanwhile, optimal performance of modeling moisture content was obtained from NIRS with the minimum RMSEP of 0.308%. After obtaining the best prediction results of volatile matter content, ash content, and moisture content, the fixed carbon content can be calculated by the definition formula. These results demonstrated that the reported method can optimize the rapid analysis of multiple coal properties simultaneously.