  Extra virgin olive oil (EVOO) is the highest quality of olive oil and is
characterized by highly beneficial nutritional properties. The large increase
in both consumption and fraud, for example through adulteration, creates new
challenges and an increasing demand for developing new quality assessment
methodologies that are easier and cheaper to perform. As of today, the
determination of olive oil quality is performed by producers through chemical
analysis and organoleptic evaluation. The chemical analysis requires the
advanced equipment and chemical knowledge of certified laboratories, and has
therefore a limited accessibility. In this work a minimalist, portable and
low-cost sensor is presented, which can perform olive oil quality assessment
using fluorescence spectroscopy. The potential of the proposed technology is
explored by analyzing several olive oils of different quality levels, EVOO,
virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were
analyzed using a large number of machine learning methods, including artificial
neural networks. The analysis performed in this work demonstrates the
possibility of performing classification of olive oil in the three mentioned
classes with an accuracy of 100$\%$. These results confirm that this minimalist
low-cost sensor has the potential of substituting expensive and complex
chemical analysis.
