Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques

TL;DR

This study introduces a portable, low-cost fluorescence sensor combined with machine learning to accurately classify olive oil quality levels, offering a simpler alternative to traditional chemical analysis methods.

Contribution

The paper presents a novel, minimalist fluorescence sensor and machine learning approach for olive oil quality assessment, reducing reliance on expensive laboratory techniques.

Findings

Achieved 100% accuracy in classifying EVOO, VOO, and LOO.

Demonstrated the sensor's potential as a cost-effective alternative to chemical analysis.

Validated the effectiveness of machine learning models with spectral data.

Abstract

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.