The present experiment compared the proximate composition, the amino acid content, and profile of the breast meat of a commercial broiler hybrid (Hybrid-Normal) vs. a broiler hybrid affected by the simultaneous presence of white striping (WS) and wooden breast (WB) myopathies (Hybrid-WSWB) vs. the Italian purebred Polverara chicken (Polverara). To this purpose, a total of n = 30 breast meat cuts from male chickens/group were subjected to meat quality evaluations. Chickens were slaughtered at their commercial age. The meat of the Polverara breed showed the highest protein (P < 0.0001) and the lowest lipids (P < 0.0001) contents, whereas that of the Hybrid-WSWB broiler chickens had the lowest protein and ash (P < 0.0001) contents and the highest (P < 0.0001) amount of fat. Meat of Hybrid-Normal chickens displayed intermediate values. Polverara meat was the richest in most amino acids (g/100 g meat), whereas Hybrid-WSWB one had the lowest content (P < 0.0001). Taurine was not detected in the meat of the Polverara chicken. The meat of Hybrid-Normal chickens was the richest in valine and taurine amino acids (P < 0.0001). Results on the amino acids proportions (% of total amino acids) highlighted that lysine, arginine, leucine, glutamic acid, aspartic acid, alanine, and serine were the most representative essential and nonessential amino acids, respectively, in all 3 meat-types chickens. The study confirmed that WS and WB myopathies negatively affect the meat proximate composition and the amino acid content. The meat of the Polverara breed displayed a remarkable meat quality including a high protein content of very good quality. This may represent a tool to promote its meat among consumers and help the survival of this endangered breed. Furthermore, research efforts should be conducted to understand why taurine was absent in the breast meat of Polverara chicken.

The aim of the present study was to develop a model to identify posture and behavior from data collected by a triaxial accelerometer located on the left flank of dairy cows and evaluate its accuracy and precision. Twelve Italian Red-and-White lactating cows were equipped with an accelerometer and observed on average for 136 ± 29 min per cow by two trained operators as a reference. The acceleration data were grouped in time windows of 8 s overlapping by 33.0%, for a total of 35,133 rows. For each row, 32 different features were extracted and used by machine learning algorithms for the classification of posture and behavior. To build up a predictive model, the dataset was split in training and testing datasets, characterized by 75.0 and 25.0% of the observations, respectively. Four algorithms were tested: Random Forest, K Nearest Neighbors, Extreme Boosting Algorithm (XGB), and Support Vector Machine. The XGB model showed the best accuracy (0.99) and Cohen’s kappa (0.99) in predicting posture, whereas the Random Forest model had the highest overall accuracy in predicting behaviors (0.76), showing a balanced accuracy from 0.96 for resting to 0.77 for moving. Overall, very accurate detection of the posture and resting behavior were achieved.

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.

This study evaluated near-infrared (NIR) and visible-NIR (Vis-NIR) spectroscopy as a way to distinguish fresh (F) from frozen-thawed (T) swordfish cutlets (Xiphias gladius). A total of 90 F and 60 T samples were used. The T samples were stored at a high and low frozen temperature (HT: −10°C; LT: −18°C). Spectra were collected using a Vis-NIR portable spectrophotometer (380–1080 nm) and a NIR monochromator (1100–2500 nm). The percentage of correctly classified samples obtained with Vis-NIR spectroscopy was ≥ 96.7%, whereas that for NIR was ≥ 90.0%. The best classification was observed comparing F and HT samples using Vis-NIR (100 vs. 96.7%, respectively). The more descriptive principal component scores (PCS) of NIR and Vis-NIR were used with a multivariate binary logistic regression. The model with the PCS of the first two Vis-NIR principal components accounted for 81.1% of the classification. Vis-NIR could be a strategic tool to screen the cold treatment of swordfish.