Abstract：
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.
摘要：
激光诱导击穿光谱（LIBS）定量分析的准确性和精度受到光谱噪声的极大限制。通常使用多个光谱的归一化和集合平均来预处理光谱。然而,这些方法不能完全去除频谱噪声。不可去除的频谱噪声会影响LIBS定量分析数据的不确定性。因此,提出了一种利用数据不确定性提高LIBS定量分析性能的方法。所提出的方法使用多个光谱来表征每个样品，以保留校准数据矩阵中的一些数据不确定性。因此，数据不确定性用于优化校准模型，以提高对光谱信号变化的耐受性。因此,优化后的校准模型比常规方法训练的校准模型具有更好的准确性和鲁棒性。优化的校正模型的煤灰分含量的最佳预测均方根误差(RMSEP)为1.152%,而常规校准模型为1.718%。优化的校准模型还显示了重复预测的较低的相对标准偏差(RSD)值。此外，该方法还优化了预测生物质中灰分含量的校准模型。优化后的校准模型再次优于常规校准模型,证明了该方法的广泛适用性。
