Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil Gucciardi, Arnaud Martos Núñez, María Vanesa Fluorescence spectroscopy Fluorescence sensor Olive oil Machine learning Artificial neural networks Convolutional neural networks Quality control Olive oil is an important commodity in the world, and its demand has grown substantially in recent years. As of today, the determination of olive oil quality is based on both chemical analysis and organoleptic evaluation from specialized laboratories and panels of experts, thus resulting in a complex and time-consuming process. This work presents a new compact and low-cost sensor based on fluorescence spectroscopy and artificial neural networks that can perform olive oil quality assessment. The presented sensor has the advantage of being a portable, easy-to-use, and low-cost device, which works with undiluted samples, and without any pre-processing of data, thus simplifying the analysis to the maximum degree possible. Different artificial neural networks were analyzed and their performance compared. To deal with the heterogeneity in the samples, as producer or harvest year, a novel neural network architecture is presented, called here conditional convolutional neural network (Cond- CNN). The presented technology is demonstrated by analyzing olive oils of different quality levels and from different producers: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). The sensor classifies the oils in the three mentioned classes with an accuracy of 82%. These results indicate that the Cond-CNN applied to the data obtained with the low-cost luminescence sensor, can deal with a set of oils coming from multiple producers, and, therefore, showing quite heterogeneous chemical characteristics. 2022-09-23T07:30:49Z 2022-09-23T07:30:49Z 2022-05-17 conference output Gucciardi Arnaud... [et al.], "Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil," Proc. SPIE 12139, Optical Sensing and Detection VII, 121391J (17 May 2022); doi: [10.1117/12.2621588] https://hdl.handle.net/10481/76887 10.1117/12.2621588 eng info:eu-repo/grantAgreement/EC/H2020/101007702 info:eu-repo/grantAgreement/EC/H2020/956394 http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional SPIE