In this paper (open access) the authors propose two novel metrics—the Geographical Differentiation Index (GDI) and Environmental Heritability Index (EHI)—to quantify spatial variation in fatty acids and their environmental drivers. These methodologies are derived from classical genetic theory - traditional heritability quantifies the contribution of genes to traits by calculating the ratio of additive genetic variance to phenotypic variance. The authors applied this same methodology to the fatty acid profile of oils, in order to diagnose their geographic origin.
They systematically investigated the fatty acid profiles of four main oil-rich crops (olive, camellia, walnut, and peony seed) and revealed that fatty acid distributions follow elevation- and latitude-dependent patterns, with peony seed oils showing the strongest latitudinal sensitivity. Key fatty acids like stearic acid (C18:0) and linoleic acid (C18:2) correlated significantly with geographic factors globally, while the biomass of certain specific fatty acids varies significantly in high-altitude/low-latitude regions. They conclude that their findings establish specific fatty acid signatures as a robust tool for geographic authentication. They provide a chemical rationale for classification models, based on Machine Learning, that measure differences in fatty acid profiles.
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