Thirty-three bituminous coal samples were utilized to test the application of laser-induced breakdown spectroscopy technique for coal elemental concentration measurement in the air. The heterogeneity of the samples and the pyrolysis or combustion of coal during the laser-sample interaction processes were analyzed to be the main reason for large fluctuation of detected spectra and low calibration quality. Compared with the generally applied normalization with the whole spectral area, normalization with segmental spectral area was found to largely improve the measurement precision and accuracy. The concentrations of major element C in coal were determined by a novel partial least squares (PLS) model based on dominant factor. Dominant C concentration information was taken from the carbon characteristic line intensity since it contains the most-related information, even if not accurately. This dominant factor model was further improved by inducting non-linear relation by partially modeling the inter-element interference effect. The residuals were further corrected by PLS with the full spectrum information. With the physical-principle-based dominant factor to calculate the main quantitative information and to partially explicitly include the non-linear relation, the proposed PLS model avoids the overuse of unrelated noise to some extent and becomes more robust over a wider C concentration range. Results show that RMSEP in the proposed PLS model decreased to 4.47% from 5.52% for the conventional PLS with full spectrum input, while R(2) remained as high as 0.999, and RMSEC&P was reduced from 3.60% to 2.92%, showing the overall improvement of the proposed PLS model.
This paper presents a new approach of applying partial least squares method combined with a physical principle based dominant factor. The characteristic line intensity of the specific element was taken to build up the dominant factor to reflect the major elemental concentration and partial least squares (PLS) approach was then applied to further improve the model accuracy. The deviation evolution of characteristic line intensity from the ideal condition was depicted and according to the deviation understanding, efforts were taken to model the non-linear self-absorption and inter-element interference effects to improve the accuracy of dominant factor model. With a dominant factor to carry the main quantitative information, the novel multivariate model combines advantages of both the conventional univariate and PLS models and partially avoids the overuse of the unrelated noise in the spectrum for PLS application. The dominant factor makes the combination model more robust over a wide concentration range and PLS application improves the model accuracy for samples with matrices within the calibration sample set. Results show that RMSEP of the final dominant factor based PLS model decreased to 2.33% from 5.25% when using the conventional PLS approach with full spectral information. Furthermore, with the development in understanding the physics of the laser-induced plasma, there is potential to easily improve the accuracy of the dominant factor model as well as the proposed novel multivariate model.
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