Department of Physics, University of Patras, 26504 Rio, Patras, Greece; Institute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology-Hellas (FORTH), Patras 26504, Greece.
Department of Physics, University of Patras, 26504 Rio, Patras, Greece; Institute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology-Hellas (FORTH), Patras 26504, Greece.
Food Chem. 2020 Jan 1;302:125329. doi: 10.1016/j.foodchem.2019.125329. Epub 2019 Aug 5.
Olive oil is an essential diet component in all Mediterranean countries having a considerable impact on the local economies, which are producing almost 90% of the world production. Therefore, the quality assessment of olive oil in terms of its acidity and its authentication in terms of PDO (Protected Designation of Origin) and PGI (Protected Geographical Indications) characterizations are nowadays necessary and of great importance for the market of olive oil and the related economic activities. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) is used assisted by machine learning algorithms for retrieving of the information contained in the LIBS spectra to provide a simple, reliable, and ultrafast methodology for olive oils classification in terms of the degree of acidity and geographical origin. The combination of LIBS technique with machine learning statistical analysis approaches constitute a very powerful tool for the fast, in-situ and remote quality control of olive oil.
橄榄油是所有地中海国家饮食的重要组成部分，对当地经济有着巨大的影响，这些国家生产了全球近 90%的橄榄油。因此，评估橄榄油的酸度，鉴别其 PDO（受保护的原产地名称）和 PGI（受保护的地理标志）特性，对于橄榄油市场和相关经济活动来说是非常必要且非常重要的。在本工作中，激光诱导击穿光谱（LIBS）结合机器学习算法，用于提取 LIBS 光谱中的信息，从而提供了一种简单、可靠、超快的方法，用于根据酸度和地理来源对橄榄油进行分类。LIBS 技术与机器学习统计分析方法的结合，为橄榄油的快速、原位和远程质量控制提供了一种非常强大的工具。
