Combining different biomarkers to distinguish Chemlali virgin olive oils from different geographical areas of Tunisia

地理标志 偏最小二乘回归 数学 统计 地理 区域科学
作者
Aida Lazzez,Beatriz Quintanilla‐Casas,Stefania Vichi
出处
期刊:Journal of the Science of Food and Agriculture [Wiley]
卷期号:103 (7): 3295-3305 被引量:4
标识
DOI:10.1002/jsfa.12506
摘要

Abstract BACKGROUND Despite their high potential, Tunisian virgin olive oils (VOOs) are mainly exported in bulk or blended with VOOs of other origin, hindering their international market placement. To face this situation, their valorization is needed by highlighting their unique features and by developing tools to guarantee their geographical authenticity. Compositional features of Chemlali VOOs produced in three Tunisian regions were assessed to identify suitable authenticity markers. RESULTS Quality indices ensured the quality of the VOOs studied. Volatile compounds, total phenols, fatty acid (FA) and chlorophylls are significantly influenced by the region of origin, which was justified by the differences found in soil and climatic conditions of the three geographical regions. To explore the capabilities of these markers for the geographical authentication of Tunisian Chemlali VOOs, classification models based on partial least squares‐discriminant analysis (PLS‐DA) were developed by grouping the minimum number of variables allowing the highest discrimination power, minimizing in this way the analytical procedure. The PLS‐DA authentication model based on combining volatile compounds with FA or with total phenols achieved a correct classification of 95.7% of the VOOs according to their origin, as assessed by 10%‐out cross‐validation. Sidi Bouzid Chemlali VOOs achieved 100% of correct classification, while the misclassification between Sfax and Enfidha ones did not exceed 10%. CONCLUSIONS These results allowed to establish the most promising and affordable combination of markers for the geographical authentication of Tunisian Chemlali VOOs from distinct production regions and provide the basis to further develop authentication models based on wider datasets. © 2023 Society of Chemical Industry.
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