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Near-Infrared (NIR) Spectroscopy was used for the non-destructive assessment of physico-chemical quality parameters in olive oil. At the same time, the influence of the sample presentation mode (spinning versus static cup) was evaluated using two spectrophotometers with similar optical characteristics. A total of 478 olive oil samples were used to develop calibration models, testing various spectral signal pre-treatments. The models obtained by applying MPLS regression to spectroscopic data yielded promising results for olive oil quality measurements, particularly for acidity, the peroxide index and alkyl and ethyl ester content. The results obtained indicate that this non-invasive technology can be used successfully by the olive oil sector to categorize olive oils, to detect potential fraud and to provide consumers with more reliable information. Although both sample presentation modes yielded comparable results, equations constructed with samples scanned using the spinning mode provided greater predictive capacity.

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Since the bovine spongiform encephalopathy (BSE) crisis, the use of animal proteins in animal feed has been prohibited. From October 2003, the European Union (EU) adopted Regulation (EC) no. 1774/2002 governing animal by-products (ABPs), which seeks to address the possible risks inherent in recycling potential infectivity due to the absence of barriers within species and to exclude the cannibalism which may be induced by intra-species recycling. There is an urgent need to develop fast and reliable methods for identification of low-level ABP origins. In this study, near infrared (NIR) microscopy was used to identify different classes of ABPs. low-level ABP origins. In this study, near infrared (NIR) microscopy was used to identify different classes of ABPs. ABP origins. In this study, near infrared (NIR) microscopy was used to identify different classes of ABPs. (NIR) microscopy was used to identify different classes of ABPs. microscopy was used to identify different classes of ABPs. Samples of fish meals (n = 10) and meals of land-animal origin (n = 50) were ground, sedimented and analysed using an Auto Image Microscope connected to a Fourier transform near infrared spectrometer (FT-NIR). Sediment fraction particles were spread on aSpectralon plate, presented to the NIR microscope and scanned in the 1112-2500 nm region. The support vector machine (SVM) algorithm was used to construct models to identify class origin. Models correctly classified 100% of the samples in the calibration set and between 95 and 95.5% in the validation set. The results demonstrated the potential of FT-NIR microscopy as a rapid method for distinguishing between fish and land-animal particles.

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