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Interpreting convolutional neural network classifiers applied to laser-induced breakdown optical emission spectra. | LitMetric

Interpreting convolutional neural network classifiers applied to laser-induced breakdown optical emission spectra.

Talanta

Central European Institute of Technology, Brno University of Technology, Purkyňova 656/123, CZ-61200, Brno, Czech Republic; Brno University of Technology, Faculty of Mechanical Engineering, Institute of Physical Engineering, Technická 2, CZ-61669, Brno, Czech Republic. Electronic address:

Published: January 2024

AI Article Synopsis

  • Laser-induced breakdown spectroscopy (LIBS) is a powerful analytical tool used in various high-stakes industries, often enhanced by machine learning algorithms, especially artificial neural networks (ANNs).
  • This study explores the decision-making process of convolutional neural networks (CNNs) in LIBS by employing post-hoc interpretation techniques to better understand how these models make predictions.
  • The findings reveal that a simple CNN effectively identifies important spectroscopic features related to major oxides, while also providing a basic baseline correction during the analysis.

Article Abstract

Laser-induced breakdown spectroscopy (LIBS) is a well-established industrial tool with emerging relevance in high-stakes applications. To achieve its required analytical performance, LIBS is often coupled with advanced pattern-recognition algorithms, including machine learning models. Namely, artificial neural networks (ANNs) have recently become a frequently applied part of LIBS practitioners' toolkit. Nevertheless, ANNs are generally applied in spectroscopy as black-box models, without a real insight into their predictions. Here, we apply various post-hoc interpretation techniques with the aim of understanding the decision-making of convolutional neural networks. Namely, we find synthetic spectra that yield perfect expected classification predictions and denote these spectra class-specific prototype spectra. We investigate the simplest possible convolutional neural network (consisting of a single convolutional and fully connected layers) trained to classify the extended calibration dataset collected for the ChemCam laser-induced breakdown spectroscopy instrument of the Curiosity Mars rover. The trained convolutional neural network predominantly learned meaningful spectroscopic features which correspond to the elements comprising the major oxides found in the calibration targets. In addition, the discrete convolution operation with the learnt filters results in a crude baseline correction.

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Source
http://dx.doi.org/10.1016/j.talanta.2023.124946DOI Listing

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