Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra

Francesca Venturini; Umberto Michelucci; Michela Sperti; Arnaud Gucciardi; Marco A. Deriu

doi:10.1117/12.2647809

15 March 2023 Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra

Francesca Venturini, Umberto Michelucci, Michela Sperti, Arnaud Gucciardi, Marco A. Deriu

Proceedings Volume 12438, AI and Optical Data Sciences IV; 124380O (2023) https://doi.org/10.1117/12.2647809
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

The power of artificial neural networks to determine the quality and properties of olive oil was proven by several studies in the last years. Less clear is, however, how the neural network is able to extract useful information from the input data. This work investigates the learning mechanism of one-dimensional convolutional neural networks (1D-CNNs) trained to predict the physicochemical properties of olive oil from single fluorescence spectra. Such a 1D-CNN can successfully predict the parameters relevant to the quality assessment: acidity, peroxide value, and UV absorbance. To go beyond a simple quality assessment algorithm, it is important to identify which spectral features in the measured spectra are correlated with each chemical parameter and therefore with the quality of olive oil. To obtain this information, explainability techniques can be used by studying the latent feature space generated by the intermediate layers of the one-dimensional trained convolutional neural network. This work analyses in detail the common features that are used by the 1D-CNN to predict the two physicochemical parameters: acidity and K₂₃₂.

Conference Presentation

Citation Download Citation

Francesca Venturini, Umberto Michelucci, Michela Sperti, Arnaud Gucciardi, and Marco A. Deriu "Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra", Proc. SPIE 12438, AI and Optical Data Sciences IV, 124380O (15 March 2023); https://doi.org/10.1117/12.2647809

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