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.

Central European Institute of Technology Brno University of Technology Purkyňova 656 123 CZ 61200 Brno Czech Republic

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

Faculty of Informatics Masaryk University Botanická 68A CZ 60200 Brno Czech Republic

Institute of Computer Science Masaryk University Šumavská 416 15 CZ 60200 Brno Czech Republic

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