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Background: Extra virgin olive oil (EVOO) is a natural product with numerous health benefits and superior quality compared with other vegetable oils. To characterize a sample as EVOO, it is necessary to perform a sensory evaluation through a testing panel, in addition to conducting physicochemical measurements. Moreover, distinguishing between organic and conventional production has captured the attention of those involved in the olive oil industry. The current study demonstrates the utilization of Raman spectroscopy in combination with multivariate statistical analysis for the examination of extra virgin olive oil samples obtained from Greece (Crete) over three consecutive harvest years. Results: The Raman technique and orthogonal partial least square‐discriminant analysis (OPLS‐DA) model successfully discriminated high‐ and low‐quality conventional olive oil samples for the 2017–2018 harvest year. Additionally, both organic and conventional olive oil samples were studied and distinct discrimination was achieved using OPLS‐DA on the Raman spectroscopic data for the samples with different cultivation characteristics. The combination of Raman data and statistical models for the classification of organic and conventional olive oils into high and low, and high and medium quality for the 2018–2019 and 2019–2020 harvest years, respectively, yielded satisfactory results. The samples were previously evaluated by a certified tasting panel. Conclusions: These findings demonstrate that Raman spectroscopy, combined with multivariate statistical analysis, can serve as a complementary alternative to traditional analytical methods for the analysis of olive oils. The technique is rapid, low‐cost, and without sample pretreatment. [ABSTRACT FROM AUTHOR]

Extra virgin olive oil (EVOO) is a key component of the Mediterranean diet, with several health benefits derived from its consumption. Moreover, due to its eminent market position, EVOO has been thoroughly studied over the last several years, aiming at its authentication, but also to reveal the chemical profile inherent to its beneficial properties. In the present work, a comparative study was conducted to assess Greek EVOOs' quality and authentication utilizing different analytical approaches, both targeted and untargeted. 173 monovarietal EVOOs from three emblematic Greek cultivars (Koroneiki, Kolovi and Adramytiani), obtained during the harvesting years of 2018–2020, were analyzed and quantified as per their fatty acids methyl esters (FAMEs) composition via the official method (EEC) No 2568/91, as well as their bioactive content through liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) methodology. In addition to FAMEs analysis, EVOO samples were also analyzed via HRMS-untargeted metabolomics and optical spectroscopy techniques (visible absorption, fluorescence and Raman). The data retrieved from all applied techniques were analyzed with Machine Learning methods for the authentication of the EVOOs' variety. The models' predictive performance was calculated through test samples, while for further evaluation 30 commercially available EVOO samples were also examined in terms of variety. To the best of our knowledge, this is the first study where different techniques from the fields of standard analysis, spectrometry and optical spectroscopy are applied to the same EVOO samples, providing strong insight into EVOOs chemical profile and a comparative evaluation through the different platforms. [ABSTRACT FROM AUTHOR]

The present study was aimed at the identification, differentiation and characterization of red and white Cretan wines, which are described with Protected Geographical Indication (PGI), using ultraviolet–visible absorption spectroscopy. Specifically, the grape variety, the wine aging process and the role of barrel/container type were investigated. The combination of spectroscopic results with machine learning-based modelling demonstrated the use of absorption spectroscopy as a facile and low-cost technique in wine analysis. In this study, a clear discrimination among grape varieties was revealed. Moreover, a grouping of samples according to aging period and container type of maturation was accomplished, for the first time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The present study was aimed at the identification, differentiation and characterization of red and white Cretan wines, which are described with Protected Geographical Indication (PGI), using ultraviolet-visible absorption spectroscopy. Specifically, the grape variety, the wine aging process and the role of barrel/container type were investigated. The combination of spectroscopic results with machine learning-based modelling demonstrated the use of absorption spectroscopy as a facile and low-cost technique in wine analysis. In this study, a clear discrimination among grape varieties was revealed. Moreover, a grouping of samples according to aging period and container type of maturation was accomplished, for the first time.