Determination of Molybdenum in Geological Ores by Laser-Induced Breakdown Spectroscopy (LIBS) with Support Vector Machine Regression (SVMR) and Data Preprocessing

Abstract

A support vector machine regression (SVMR) model that integrates data preprocessing was devised for the determination of Mo in molybdenum ores. To eliminate the negative effects of spectral fluctuations and improve the computational efficiency, the model processes original measurements through the following steps: denoising via wavelet transform smoothing (WTS), debaselining via adaptive iteratively reweighted penalized least squares (airPLS), main characteristic peak extraction, elimination of abnormal spectral data via the box plot method, and normalization via min-max scaling. In this study, 255 characteristic peaks were selected from 17,916 spectral datasets, and the total number of datasets after removing the outliers was 491. The calibration curve approach was used to establish a univariate model. The limit of detection of Mo was 0.0080 wt%. The R² value of the calibration curve was 0.6675. Linear regression (LR) and SVMR were used to establish a multivariate analysis model. Compared to that of the calibration curve approach, the determination coefficients (R_P²) of LR and SVMR increased from 0.8034 to 0.9859 and 0.9941, respectively. The range of relative errors (RE_P) decreased from 0.21%–67.66% to 0.48%–18.46% and 2.65%–7.44%, respectively; the mean absolute error (MAE_P) was decreased from 0.0173 wt% to 0.0048 and 0.0039 wt%, respectively; and the root mean square error (RMSE_P) decreased from 0.0243 wt% to 0.0065 and 0.0042 wt%, respectively. These results indicate that the integration of SVMR with data preprocessing is suitable for the determination of Mo in molybdenum ores.

Keywords:

• Geochemical analysis
• laser-induced breakdown spectroscopy (LIBS)
• molybdenum ore
• support vector machine regression (SVMR)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Conflicts of interest

The authors declare there are no conflicts of interest.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number U1930125, 11975121 and 42374226]; Fundamental Research Funds for the Central Universities [grant number NS2022056]; Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and a Jiangxi Provincial Natural Science Foundation [grant number 20232BAB201043 and 20232BCJ23006].