基于微型化低成本荧光传感器和机器学习技术的西班牙橄榄油品质研究
Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques
摘要：
特级初榨橄榄油（EVOO）是最高品质的橄榄油，具有高度有益的营养特性。消费和欺诈（例如通过掺假）的大量增加带来了新的挑战，并日益需要开发更容易和更便宜的新质量评估方法。到目前为止，橄榄油质量的测定是由生产商通过化学分析和感官评价进行的。化学分析需要经过认证的实验室的先进设备和化学知识，因此获得的机会有限。在这项工作中，一个极简，便携式和低成本的传感器，它可以使用荧光光谱进行橄榄油质量评估。通过分析几种不同质量水平的橄榄油，EVOO，初榨橄榄油（VOO）和Lampante橄榄油（LOO），探索了所提出的技术的潜力。光谱数据使用大量的机器学习方法进行分析，包括人工神经网络。在这项工作中进行的分析表明，在100 $\%$的准确度进行上述三类橄榄油的分类的可能性。这些结果证实，这种极简的低成本传感器具有替代昂贵和复杂的化学分析的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
