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Simple Summary: Accurate classification of lung lesions detected in porcine respiratory disease outbreaks is a prerequisite in the overall diagnostic process and greatly contributes to appropriate disease management. The use of rapid and non-destructive techniques during necropsy, such as near-infrared spectroscopy, could enhance the achievement of this objective. In this view, the aim of the present study was to investigate the potential of such a technique in differentiating between normal, congested, and pathological pig lung tissue and between different patterns of pulmonary inflammation. Overall, an effective discrimination between normal and pathological lung tissue was observed, with congested tissues exhibiting an intermediate behavior. Remarkable classification performances were also attained for the identification of the different pathological lesions, demonstrating high levels of accuracy, precision, and sensitivity. In conclusion, these findings underscore the promising utility of near-infrared spectroscopy as an invaluable tool in veterinary diagnostics. Respiratory diseases significantly affect intensive pig farming, causing production losses and increased antimicrobial use. Accurate classification of lung lesions is crucial for effective diagnostics and disease management. The integration of non-destructive and rapid techniques would be beneficial to enhance overall efficiency in addressing these challenges. This study investigates the potential of near-infrared (NIR) spectroscopy in classifying pig lung tissues. The NIR spectra (908–1676 nm) of 101 lungs from weaned pigs were analyzed using a portable instrument and subjected to multivariate analysis. Two distinct discriminant models were developed to differentiate normal (N), congested (C), and pathological (P) lung tissues, as well as catarrhal bronchopneumonia (CBP), fibrinous pleuropneumonia (FPP), and interstitial pneumonia (IP) patterns. Overall, the model tailored for discriminating among pathological lesions demonstrated superior classification performances. Major challenges arose in categorizing C lungs, which exhibited a misclassification rate of 30% with N and P tissues, and FPP samples, with 30% incorrectly recognized as CBP samples. Conversely, IP and CBP lungs were all identified with accuracy, precision, and sensitivity higher than 90%. In conclusion, this study provides a promising proof of concept for using NIR spectroscopy to recognize and categorize pig lungs with different pathological lesions, offering prospects for efficient diagnostic strategies. [ABSTRACT FROM AUTHOR]

An untargeted Nuclear Magnetic Resonance (NMR) spectroscopy-based metabolomics approach was applied as a first attempt to explore the metabolome of pigs treated with antibiotics. The final goal was to investigate the possibility of discriminating between antibiotic-treated (TX group) and untreated pigs (CTRL group), with the further perspective of identifying the authentication tools for antibiotic-free pork supply chains. In particular, 41 samples of pig liver were subjected to a biphasic extraction to recover both the polar and the non-polar metabolites, and the 1H NMR spectroscopy analysis was performed on the two separate extracts. Unsupervised (principal component analysis) and supervised (orthogonal partial least squares discriminant analysis) multivariate statistical analysis of 1H NMR spectra data in the range 0–9 ppm provided metabolomic fingerprinting useful for the discrimination of pig livers based on the antibiotic treatment to which they were exposed. Moreover, within the signature patterns, significant discriminating metabolites were identified among carbohydrates, choline and derivatives, amino acids and some lipid-class molecules. The encouraging findings of this exploratory study showed the feasibility of the untargeted metabolomic approach as a novel strategy in the authentication framework of pork supply chains and open a new horizon for a more in-depth investigation. [ABSTRACT FROM AUTHOR]

Hen eggs from farms with different production systems (organic, free range, barn, and in cage) sited in two regions of Italy (Emilia-Romagna and Lombardy) were collected from 2017 to 2019 to monitor the levels of 17 polychlorinated dibenzo-p-dioxin/dibenzofurans (PCDD/Fs), 12 dioxin-like polychlorinated biphenyls (dl-PCBs) and the 6 non-dioxin-like polychlorinated biphenyl indicators (ndl-PCBs). Average concentrations of PCDD/Fs (0.21 pg WHO(2005)-TEQ/g fat), PCDD/Fs + dl-PCBs (0.43 pg WHO(2005)-TEQ/g fat) and ndl-PCBs (6.41 ng/g fat) were below the maximum limits established at European level in eggs, but significantly higher amounts of PCDD/Fs and PCDD/Fs + dl-PCBs were found in eggs from free-range housing system compared to barn, cage, and organic ones. The potential dietary intake of the monitored contaminants by Italian population age groups through the consumption of locally produced eggs was also evaluated. Exposure levels to PCDD/Fs plus dl-PCBs were below the tolerable weekly intake (TWI) of 2 pg WHO(2005)-TEQ/kg bw/week recently set by the European Food Safety Authority (EFSA). However, the younger population was found to be more vulnerable than other age groups to exposure to these contaminants. Specifically, the preferential consumption of free-range eggs by infants, toddlers, and children contributed more than 30% to the TWI. The results confirm the need to continuously monitor the levels of chemical contaminants in the environment and provide a reminder of the importance of targeted intervention measures aimed to reduce their occurrence in food and feed, firstly by improving the management practices on egg-producing farms. [ABSTRACT FROM AUTHOR]

In the present study near-infrared (NIR) spectroscopy was used to assess the geographical traceability of salted ripened anchovies, whose raw product originated from fishing areas of Morocco, Spain, Tunisia, and Croatia. Two different products were tested: semi-finished and finished salted anchovies. The development and optimization of combined discrimination models based on orthogonal partial least square-discriminant analysis successfully led to the identification of the geographical origin of both anchovy datasets with >98% sensitivity, >99% specificity, and >99% accuracy on average. While NIR absorption bands related to proteins and degradation compounds highly characterized samples from Morocco and those of unsaturated lipids and derivatives globally contradistinguished anchovies from Tunisia, absorptions of both protein and lipid compounds were responsible for the discrimination of samples from Croatia and Spain. The proposed method is particularly helpful to guarantee the authenticity of salted ripened anchovies and, thus, to deter commercial frauds throughout the fish value chain and ensure traceability along the whole food chain. • Chemometric analysis of near infrared spectra of anchovies was performed. • Discriminant models were internally and externally validated. • Variables responsible for discrimination were identified. • Untargeted authentication of anchovies from different origins was feasible. [ABSTRACT FROM AUTHOR]