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LIBS combined with SG-SPXY spectral data pre-processing for cement raw meal composition analysis.

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    This study introduces a new method using Savitzky-Golay (SG) smoothing and sample set partitioning based on joint x-y distance (SPXY) to enhance laser-induced breakdown spectroscopy (LIBS) for cement raw meal analysis. The improved technique significantly boosts accuracy in determining cement component concentrations.

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    Area of Science:

    • Materials Science
    • Analytical Chemistry
    • Spectroscopy

    Background:

    • Accurate and rapid analysis of cement raw meal composition is critical for efficient cement production.
    • Existing methods for quantitative analysis of cement raw meal components using laser-induced breakdown spectroscopy (LIBS) face challenges with accuracy due to spectral noise and data complexity.
    • There is a need for advanced data pre-processing techniques to improve the performance of LIBS in real-time cement analysis.

    Purpose of the Study:

    • To develop and validate a novel spectral data pre-processing method combining Savitzky-Golay (SG) smoothing and sample set partitioning based on joint x-y distance (SPXY).
    • To enhance the accuracy of quantitative analysis of major oxide components (CaO, SiO2, Al2O3, Fe2O3) in cement raw meal using laser-induced breakdown spectroscopy (LIBS).
    • To demonstrate the effectiveness of the proposed method in improving the predictive performance of back-propagation (BP) neural network models.

    Main Methods:

    • Savitzky-Golay (SG) smoothing was applied for spectral denoising and baseline correction.
    • Sample Set Partitioning based on Joint x-y distance (SPXY) was employed for efficient and representative sample set division.
    • A back-propagation (BP) neural network model was utilized for quantitative analysis of cement raw meal components using the pre-processed spectral data.
    • Performance was evaluated by comparing with the Hold-Out method, assessing correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error (MAPE).

    Main Results:

    • The SG smoothing effectively reduced spectral noise and baseline variations.
    • The SPXY method facilitated improved data analysis efficiency.
    • The proposed pre-processing method significantly enhanced the quantitative analysis accuracy for CaO, SiO2, Al2O3, and Fe2O3.
    • Specific improvements included a 26% increase in R for CaO, a 47% reduction in RMSE for CaO, and a 63% reduction in MAPE for CaO compared to the Hold-Out method.

    Conclusions:

    • The combination of SG smoothing and SPXY pre-processing significantly improves the accuracy of LIBS for quantitative analysis of cement raw meal composition.
    • The developed method offers a robust and effective solution for real-time detection and quality control in cement production.
    • This advancement holds significant implications for optimizing the cement manufacturing process through precise and rapid compositional analysis.