Keywords:Fruit Multispectral Vision Ripeness Firmness Maturity Non-destructive Quality Visible spectra Chlorophyll Peach Two multispectral maturity classifications for red soft-flesh peaches ('Kingcrest', 'Rubyrich' and 'Richlady' n = 260) are proposed and compared based on R (red) and R/IR (red divided by infrared) images obtained with a three CCD camera (800 nm, 675 nm and 450 nm). R/IR histograms were able to correct the effect of 3D shape on light reflectance and thus more Gaussian histograms were produced than R images. As fruits ripened, the R/IR histograms showed increasing levels of intensity. Reference measurements such as firmness and visible spectra also varied significantly as the fruit ripens, firmness decreased while reflectance at 680 nm increased (chlorophyll absorption peak).

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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.

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