Nanou Eleni, Bekogianni Marios, Stamatoukos Theodoros, Couris Stelios
Department of Physics, University of Patras, 26504 Patras, Greece.
Institute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology-Hellas (FORTH), 26504 Patras, Greece.
Foods. 2025 Jan 18;14(2):321. doi: 10.3390/foods14020321.
The fast detection of Extra Virgin Olive Oil (EVOO) adulteration with poorer quality and lower price vegetable oils is important for the protection of consumers and the market of olive oil from fraudulent activities, the latter exhibiting an increasing trend worldwide during the last few years. In this work, two optical spectroscopic techniques, namely, Laser-Induced Breakdown Spectroscopy (LIBS) and UV-Vis-NIR absorption spectroscopy, are employed and are assessed for EVOO adulteration detection, using the same set of olive oil samples. In total, 184 samples were studied, including 40 EVOOs and 144 binary mixtures with pomace, soybean, corn, and sunflower oils, at various concentrations (ranging from 10 to 90% /). The emission data from LIBS, related to the elemental composition of the samples, and the UV-Vis-NIR absorption spectra, related to the organic ingredients content, are analyzed, both separately and combined (i.e., fused), by Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs), and Logistic Regression (LR). In all cases, very highly predictive accuracies were achieved, attaining, in some cases, 100%. The present results demonstrate the potential of both techniques for efficient and accurate olive oil authentication issues, with the LIBS technique being better suited as it can operate much faster.
快速检测特级初榨橄榄油（EVOO）与质量较差、价格较低的植物油的掺假情况，对于保护消费者以及橄榄油市场免受欺诈活动影响至关重要，近年来后者在全球范围内呈上升趋势。在这项工作中，采用了两种光学光谱技术，即激光诱导击穿光谱（LIBS）和紫外-可见-近红外吸收光谱，并使用同一组橄榄油样品评估其对EVOO掺假检测的效果。总共研究了184个样品，包括40种EVOO以及144种与果渣油、大豆油、玉米油和葵花籽油的二元混合物，浓度各异（范围从10%到90%）。通过线性判别分析（LDA）、支持向量机（SVM）和逻辑回归（LR）分别并联合（即融合）分析了来自LIBS的与样品元素组成相关的发射数据以及与有机成分含量相关的紫外-可见-近红外吸收光谱。在所有情况下，都实现了非常高的预测准确率，在某些情况下达到了100%。目前的结果表明这两种技术在高效、准确的橄榄油认证问题上的潜力，其中LIBS技术更适合，因为它运行速度更快。
