Spectral knowledge-based regression for laser-induced breakdown spectroscopy quantitative analysis

نویسندگان

چکیده

Laser-induced breakdown spectroscopy (LIBS) is a promising atomic emission spectroscopic technique for multi-elemental analysis and has the advantages of real-time multi-element measurement, minimal sample preparation remote detection. However, quality LIBS data can be low due to matrix effects signal uncertainty which hinders wide application LIBS. Recent studies attempt improve performance quantitative using linear nonlinear multivariate models. Linear models easily present how key variables contribute prediction but suffer from degradation if high degree nonlinearity. Nonlinear tend have good performance, they lack simple intuitive explanations contribution are prone overfitting. Moreover, used in analysis, such as support vector regression (SVR) kernel extreme learning machine (K-ELM), not designed incorporate domain knowledge spectral data. In this work, new algorithm proposed, namely knowledge-based (SKR), integrates analysis. The model knowledge-driven built on correlated with analyte composition. data-driven transforms input into matrix. proposed SKR tested 4 datasets outperforms 5 baseline methods 12 18 quantification tasks. it intuitively explains towards same computational complexity ridge These results demonstrate that inherits accuracy modelling variable interpretability Therefore, serve method improving reliability

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ژورنال

عنوان ژورنال: Expert Systems With Applications

سال: 2022

ISSN: ['1873-6793', '0957-4174']

DOI: https://doi.org/10.1016/j.eswa.2022.117756