The possibility of using near-infrared spectroscopy (NIRS) for the authentication of wild European sea bass ( Dicentrarchus labrax ) was investigated in this study. Three different chemometric techniques to process the NIR spectra were developed, and their ability to discriminate between wild and farmed sea bass samples was evaluated. One approach used spectral information to directly build the discrimination model in a latent variable space; the second approach first used wavelets to transform the spectral information and subsequently derived the discrimination model using the transformed spectra; in the third approach a cascaded arrangement was proposed whereby very limited chemical information was first estimated from spectra using a regression model, and this estimated information was then used to build the discrimination model in a latent variable space. All techniques showed that NIRS can be used to reliably discriminate between wild and farmed sea bass, achieving the same classification performance as classification methods that use chemical properties and morphometric traits. However, compared to methods based on chemical analysis, NIRS-based classification methods do not require reagents and are simpler, faster, more economical, and environmentally safer. All proposed techniques indicated that the most predictive spectral regions were those related to the absorbance of groups CH, CH(2), CH(3), and H(2)O, which are related to fat, fatty acids, and water content.

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In this work the feasibility of near infrared spectroscopy was evaluated combined with chemometric approaches, as a tool for the botanical origin prediction of 119 honey samples. Four varieties related to polyfloral, acacia, chestnut, and linden were first characterized by their physical-chemical parameters and then analyzed in triplicate using a near infrared spectrophotometer equipped with an optical path gold reflector. Three different classifiers were built on distinct multivariate and machine learning approaches for honey botanical classification. A partial least squares discriminant analysis was used as a first approach to build a predictive model for honey classification. Spectra pretreatments named autoscale, standard normal variate, detrending, first derivative, and smoothing were applied for the reduction of scattering related to the presence of particle size, like glucose crystals. The values of the descriptive statistics of the partial least squares discriminant analysis model allowed a sufficient floral group prediction for the acacia and polyfloral honeys but not in the cases of chestnut and linden. The second classifier, based on a support vector machine, allowed a better classification of acacia and polyfloral and also achieved the classification of chestnut. The linden samples instead remained unclassified. A further investigation, aimed to improve the botanical discrimination, exploited a feature selection algorithm named Boruta, which assigned a pool of 39 informative averaged near infrared spectral variables on which a canonical discriminant analysis was assessed. The canonical discriminant analysis accounted a better separation of samples according to the botanical origin than the partial least squares discriminant analysis. The approach used has permitted to achieve a complete authentication of the acacia honeys but not a precise segregation of polyfloral ones. The comparison between the variables important in projection and the Boruta pool showed that the informative wavelengths are partially shared especially in the middle and far band of the near infrared spectral range.

Chemical and physical traits and fatty acid composition of meat samples from 148 Piemontese beef samples were predicted by near infrared spectroscopy. Coefficients of determination in calibration (R2) ranged between 0.44 and 0.99 for chemical composition and between 0.02 and 0.98 for fatty acid (FA) profile, being in general more accurate for the major FA. The calibration results gave inaccurate prediction for cholesterol and collagen content and for most physical traits, such as Warner-Bratzler shear force, cooking loss, drip loss, colour (L, a, b) and pH

In the context of dairy cow feeding, it is increasingly important to know the quality of the maize silage used in the ration and therefore, it appears to be crucial optimizing the techniques necessary to assess it. The aim of this study was to evaluate whether the Flieg-Zimmer's score (FZS), could properly estimate the quality of fermentations of maize silage made in a lab-scale ensiling system, and to calculate and validate new quality indexes suitable for lab-scale fermentations. The experimental dataset was obtained by analysing through near-infrared spectroscopy 522 samples of whole maize crop ensiled immediately after the harvest, using the vacuum-packing technique. The five (I1-I5) new indexes were calculated on the basis of seven parameters chosen among pH, lactic, acetic, propionic and butyric acids, ethanol, mannitol and ammonia. All the indexes were tested for normality with the Shapiro-Wilk test. In order to define the accuracy with which the new indexes ranked the maize silage on the basis of its fermentation quality, a ROC analysis was performed, using the FZS as gold standard test and dichotomizing the FZS in two levels according to a cutoff (FZS < 2 80, non-excellent vs. FZS ≥ 80, excellent). Accuracy was determined through the value of the area under the curve (AUC). Finally, a one-way ANOVA model was used to compare the quality of maize silage with low (< 320 g/kg), medium (320-360 g/kg) and high (> 360 g/kg) dry matter (DM). In the lab-scale silages the new indexes were normally distributed, whereas the FZS was not. The new indexes showed values of AUC ranging between 0.78 and 0.89, with the I5 index showing the best combination of sensitivity (0.87) and specificity (0.77) in discriminating between good and poor quality silage. The cutoff of the new indexes ranged between 45.5 and 57.4 points. The lab-scale silages were all excellent, no matter the category of DM. However, while the FZS did not differ among the 3 categories (mean FZS = 98.7), all the other indexes were significantly higher in silages with low DM (P < 0.001). Silages with low DM had the highest concentrations of lactic acid (56.4 g/kg DM, P < 0.001), ammonia (61.4 g/kg DM, P < 0.001) and butyric acid (0.62 g/kg DM, P < 0.001) as well. Data confirmed that the new proposed indexes are promising in describing the fermentation quality of maize silage in both field and lab-scale conditions.