Spectral intensity drift correction of Spark Mapping Analysis for large-size metal materials

Spectral intensity drift is a frequent issue in analytical processes, especially in long time excitation scanning for large size metal materials, which can significantly adversely impact the accuracy and stability of analysis results. Spectral intensity drift correction is the process of preprocessing spectral data using mathematical algorithms in order to facilitate the subsequent qualitative and quantitative analysis of spectra, especially in combination with stoichiometric methods. Up to now, spectral intensity drift correction within prolonged excitation has not been reported yet.

We propose an intensity drift correction method for element content of large-size samples using the Spark Mapping Analysis for Large Samples (SMALS) technique. By considering the row-by-row and column-by-column mapping modes of the SMALS, this includes curve fitting baseline correction for in-row and in-column correction, as well as total average value correction for inter-row and inter-column correction. The final measurement values are derived by coupling rows with columns. The careful implementation of correction steps can enhance baseline correction performance, effectively reducing measurement errors a drift errors. Application of this method to characterize the cross and longitudinal sections of an oversized steel billet indicates high agreement with composition distribution obtained by micro-beam X-ray fluorescence (μ-XRF). The corrected longitudinal and cross-sectional data also exhibit strong alignment. Comparison of statistical analysis results pre- and post-correction demonstrates significant improvements in the clarity of elements segregation pattern.

This intensity drift correction method not only enhances the spectral quality but also improves the accuracy and robustness of quantitative and qualitative spectral analysis. This study contributes to establishing a robust foundation for component characterization of large-size metal materials using the SMALS technique. The novel spectral intensity correction method shows theoretical significance and practical value for large-scale, long-duration excitation scanning analysis.