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The use of a common environment for processing different powder foods in the industry has increased the risk of finding peanut traces in powder foods. The analytical methods commonly used for detection of peanut such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR) represent high specificity and sensitivity but are destructive and time-consuming, and require highly skilled experimenters. The feasibility of NIR hyperspectral imaging (HSI) is studied for the detection of peanut traces down to 0.01% by weight. A principal-component analysis (PCA) was carried out on a dataset of peanut and flour spectra. The obtained loadings were applied to the HSI images of adulterated wheat flour samples with peanut traces. As a result, HSI images were reduced to score images with enhanced contrast between peanut and flour particles. Finally, a threshold was fixed in score images to obtain a binary classification image, and the percentage of peanut adulteration was compared with the percentage of pixels identified as peanut particles. This study allowed the detection of traces of peanut down to 0.01% and quantification of peanut adulteration from 10% to 0.1% with a coefficient of determination (r 2 ) of 0.946. These results show the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA to facilitate enhanced quality-control surveillance on food-product processing lines.

Keywords:Hyperspectral imaging Vision Ripeness Non-destructive assessment Quality PeachThe present research is focused on the application of artificial visión to assess the ripening of red skinned soft-flesh peach ('Richlady'). Artificial visión allows a spatially detailed determination of the ripening stage of the fruit. The considered optical indexes (Indi and Ind2, proposed in the present research, and Ind 3 and I AD , proposed by other authors) are based on the combination of wavelengths cióse to the chlorophyll absorption peak at 680 nm. Ind} corresponds approximately to the depth of the absorption peak, and Ind 2 corresponds to the relative absorption peak. An artificial image of each Índex was obtained by computing the corresponding reflectance images, which were acquired with a hyperspectral camera. All indexes were able to correct convexity (except for the just-harvested peaches and for Ind}). Ind 2 is the preferred Índex; it showed the highest discriminating power between ripening stages and no influence of convexity. Ind 2 also allowed the differentiation of ripening regions within the fruits, and it showed the evolution of those regions during ripening.

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