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• W4386805130 abstract "Abstract 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." @default.
• W4386805130 created "2023-09-17" @default.
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• W4386805130 date "2023-10-05" @default.
• W4386805130 modified "2023-10-09" @default.
• W4386805130 title "Classification of Greek extra virgin olive oils by Raman spectroscopy in conjunction with sensory and cultivation characteristics, and multivariate analysis" @default.
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• W4386805130 doi "https://doi.org/10.1002/jsf2.156" @default.
• W4386805130 hasPublicationYear "2023" @default.
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