Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas (IESL-FORTH), 700 13 Heraklion, Crete, Greece.
Department of Chemistry, University of Crete, 700 13 Heraklion, Crete, Greece.
Molecules. 2020 Sep 12;25(18):4180. doi: 10.3390/molecules25184180.
Olive oil samples from three different Greek regions (Crete, Peloponnese and Lesvos) were examined by optical spectroscopy in a wide spectral region from ultraviolet to near infrared using absorption, fluorescence and Raman spectroscopies. With the aid of machine learning methods, such as multivariate partial least squares discriminant analysis, a clear classification of samples originating from the different Greek geographical regions was revealed. Moreover, samples produced in different subareas of Crete and Peloponnese were also well discriminated. Furthermore, mixtures of olive oils from different geographical origins were studied employing partial least squares as a tool to establish a model between the actual and predicted compositions of the mixtures. The results demonstrated that optical spectroscopy combined with multivariate statistical analysis can be used as an emerging innovative alternative to the classical analytical methods for the identification of the origin and authenticity of olive oils.
对来自希腊三个不同地区(克里特岛、伯罗奔尼撒半岛和莱斯沃斯岛)的橄榄油样本进行了研究,使用吸收、荧光和拉曼光谱法在从紫外到近红外的宽光谱范围内进行了光学光谱分析。借助于机器学习方法,如多元偏最小二乘判别分析,可以清楚地对来自不同希腊地理区域的样本进行分类。此外,还可以很好地区分克里特岛和伯罗奔尼撒半岛不同地区生产的样本。此外,还研究了来自不同地理来源的橄榄油混合物,使用偏最小二乘法建立混合物实际和预测成分之间的模型。结果表明,光学光谱结合多元统计分析可以作为一种新兴的创新替代方法,用于识别橄榄油的来源和真实性,替代传统的分析方法。