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The determination of the fatty acid profile in almonds has a huge interest to establish the nutritional value of the product. Hyperspectral Imaging (HSI) integrates both the spectral and spatial dimensions, enabling a rapid and non-destructive evaluation of the composition and distribution of quality indexes in agricultural products. The objective of this study was the determination of the two main unsaturated fatty acids -oleic and linoleic, in shelled almonds analysed in bulk using a HSI system working in the spectral range 946.6 to 1648.0 nm. The predictive models were developed using the mean spectrum extracted from the ROI of each sample and applying Partial Least Squares (PLS) regression. Subsequently, the external validation of the best models was carried out using the mean spectrum of each ROI and pixel-by-pixel. The results showed a good performance for the fatty acids analysed (R2cv = 0.78 and SECV = 2.17 for oleic and R2cv = 0.77 and SECV = 1.83 for linoleic), confirming the feasibility of using HSI as a non-destructive analytical tool to assess the lipid composition and its distribution in the almonds processed in bulk, as well as to include their nutritional properties in the labelling.
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