Self-learning optical sensor
Description
The goal is to build a compact and low-cost optical sensor that can learn to classify different vegetable oils based on fluorescence spectroscopy using machine learning. The innovative approach is to use a neural network which learns by examples to classify substances without using analytical substance-specific mathematical models.
Key Data
Projectlead
Project team
Project partners
TOELT GmbH
Project status
completed, 04/2019 - 04/2020
Funding partner
Innosuisse
Publications
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Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra
2023 Venturini, Francesca; Michelucci, Umberto; Sperti, Michela; Gucciardi, Arnaud; Deriu, Marco A.
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Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks
2022 Sperti, Michela; Michelucci, Umberto; Venturini, Francesca; Gucciardi, Arnaud; Deriu, Marco A.
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Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks
2022 Sperti, Michela; Gucciardi, Arnaud; Michelucci, Umberto; Venturini, Francesca; Deriu, Marco Agostino .
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Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil
2022 Gucciardi, Arnaud; Michelucci, Umberto; Venturini, Francesca; Sperti, Michela; Martos, Vanessa M.; Deriu, Marco A.
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Exploration of Spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniques
2021 Venturini, Francesca; Sperti, Michela; Michelucci, Umberto; Herzig, Ivo; Baumgartner, Michael; Caballero, Josep Palau; Jimenez, Arturo; Deriu, Marco Agostino