A methodological approach to compare continuous and instantaneous sampling and two methods to deal with animals out of sight on dairy cattle behavior and interaction with their calf in the first hours post-partum.

Animal behavior can provide useful information about animal welfare, but methods and tools used to gather behavioral data and data treatment can influence the results. Therefore, this study was carried out on dairy cow (Bos taurus) behavior and interaction with calves early post-partum aiming at comparing two sampling rules, namely continuous and instantaneous sampling at scan intervals of 30 s, 1, 2, 3, 4, 5, and 10 min, and of two methods to deal with out of sight animals. The study was based on three assumptions: (1) continuous sampling provides the most complete and accurate data, allowing the observation of seldom behaviors and short events; (2) instantaneous sampling can provide accurate measurements of frequency and duration, especially at short scan intervals; (3) differences in behavioral results may occur depending on whether a correction for time out of sight is applied or not. Thus, 10 dams were observed from videos in the 2 h post-parturition. Ruminating, stereotypies, calf-biting and calf-butting were not recorded during the observation period. Other behaviors were observed only with continuous sampling or with continuous and instantaneous at 30-s scan intervals. The recoding of several behaviors was less accurate when applying longer scan intervals. Data from continuous and instantaneous sampling at 30-s scan intervals were compared with Wilcoxon test. Results showed no significant differences for posture, position in the pen and all behaviors (p > 0.05) except vocalizing (p = 0.003). The same test did not highlight significant differences due to method of dealing with out of sight for both sampling rules (p > 0.05). Correlation between continuous and instantaneous sampling were prevalently high at 30-s intervals and they decreased as the length of scan intervals increased for most behaviors. Results confirmed the first two assumptions suggesting that continuous sampling is more accurate, in particular for short and rare behaviors, and caution against the suitability of dam behavioral data collected using instantaneous sampling at scan intervals of minutes. The third assumption was not proven by this study. Results should be considered in light of the development of new technologies that relies on data acquired by sensors and imaging to monitor cow-calf welfare and behavior post-parturition.

Evaluation of Anatolian Water Buffalo Carcass Weights Based on a Slaughterhouse Data Collection.

This study analyzed data collected on a slaughterhouse from 2017 to 2021, belonging to five hundred and twenty one Anatolian water buffalos from different farms located in Edirne, Istanbul, and Kirklareli. Specifically, it aimed to determine the factors affecting the carcass weights and slaughter ages of the Anatolian water buffalos. The results of the study showed that the slaughter age of the buffalos was a significant determinant of their carcass weights. Meanwhile, the sex, slaughter year, and slaughter season affected the carcass weight. Differences were observed for the slaughter age regarding the sex and farm origin. Since the pricing system in local markets is based on the buffalo carcass weight, the findings of this study could be essential for farmers when determining their fattening strategies.

Uncovering Patterns in Dairy Cow Behaviour: A Deep Learning Approach with Tri-Axial Accelerometer Data.

The accurate detection of behavioural changes represents a promising method of detecting the early onset of disease in dairy cows. This study assessed the performance of deep learning (DL) in classifying dairy cows' behaviour from accelerometry data acquired by single sensors on the cows' left flanks and compared the results with those obtained through classical machine learning (ML) from the same raw data. Twelve cows with a tri-axial accelerometer were observed for 136 ± 29 min each to detect five main behaviours: standing still, moving, feeding, ruminating and resting. For each 8 s time interval, 15 metrics were calculated, obtaining a dataset of 211,720 observation units and 15 columns. The entire dataset was randomly split into training (80%) and testing (20%) datasets. The DL accuracy, precision and sensitivity/recall were calculated and compared with the performance of classical ML models. The best predictive model was an 8-layer convolutional neural network (CNN) with an overall accuracy and F1 score equal to 0.96. The precision, sensitivity/recall and F1 score of single behaviours had the following ranges: 0.93-0.99. The CNN outperformed all the classical ML algorithms. The CNN used to monitor the cows' conditions showed an overall high performance in successfully predicting multiple behaviours using a single accelerometer.

Influence of Ageing Time and Method on Beef Quality and Safety.

The effectiveness of dry ageing with regard to retaining meat quality is still subject to debate. At 4 d post mortem, samples of boneless strip loins were excised from young Charolais carcasses and then stored for a further 26 d in a cooler, either vacuum-packaged (VP) or dried-aged (DA). Loin samples were also dissected 7 d post mortem as a control treatment (CT). Chemical, instrumental and microbiological data (n = 18) were determined in longissimus dorsi and underwent ANOVA to estimate the differences in the ageing fixed factor split into two orthogonal contrasts: control vs. aged and VP vs. DA. Ageing loss (both surface dehydration and water purge) was greater in DA compared to VP samples, resulting in the lowest moisture content and highest crude protein and fat percentage in DA loins. The ageing method did not affect meat surface colour, except for redness, which had the lowest value in DA samples. Meat tenderness improved a similar amount following both VP and DA ageing treatments. Compared to the control, prolonged ageing raised both the peroxide value and the total microbial count, especially in DA samples, though both remained within the recommended limits. In summation, both ageing methods improved beef meat tenderisation, preserving its shelf life.

Comparison of Nonlinear Growth Models to Estimate Growth Curves in Kivircik Sheep under a Semi-Intensive Production System.

The Kivircik is an indigenous sheep breed from Turkey, and it has superior meat quality compared to other indigenous breeds. Therefore, farmers prioritize Kivircik lamb fattening instead of milk production. Here, we aimed to determine the best nonlinear growth model, i.e., Gompertz, Logistic, Von Bertalanffy, and Brody, to describe the growth curve of Kivircik lambs. The body weight data from birth until 150 days of age belonging to 612 lambs were used as the material of this study. The best fitting model was selected by considering the adjusted coefficient of determination (R2adj), residual mean square, and Akaike's (AIC) and Bayesian information criteria (BIC). Even though the Brody model had a better statistical fit, considering its biological interpretation, the Gompertz model was identified as an appropriate model for describing Kivircik lamb growth. Male lambs, twin lambs, and lambs born in winter had higher mature live weights (44.2 kg, 71.2 kg, and 38.5 kg, respectively) and rate of weight gain (2.1, 2.6, and 2.0, respectively). However, our subgroups revealed a similar rate of maturity (0.01). Growth models are important tools for deciding the optimal slaughter age and they provide valuable information on the management practices of both sexes, birth types, and birth seasons. These results can be applied to breeding programs for early selection, enabling intervention strategies when needed.

Herd Level Yield Gap Analysis in a Local Scale Dairy Farming System: A Practical Approach to Discriminate between Nutritional and Other Constraining Factors.

This study performed a yield gap analysis to help farmers understand whether their constraints were mainly due to nutritional factors or management and health issues. Twenty-nine farms were periodically evaluated. Milk yield (MY), dry matter intake (DMI), total mixed ration (TMR) composition and homogeneity index (HI), TMR digestibility, income over feed cost (IOFC), and MY summer-winter ratio (SWR) were collected. Farms were divided and compared according to the average annual MY: Low (L), Medium (M) and High (H), characterised by <31.1, 31.1-36.7 and >36.7 kg/head/day. An ANOVA mixed model and a stepwise regression to assess the relationship between nutritional variables and MY were run. H farms showed higher IOFC (p < 0.001), DMI (p = 0.006), DDM (p < 0.001), digestible crude protein (DCP, p = 0.019), HI (p = 0.09), SWR (p = 0.041) and lower HI coefficient of variation (p = 0.04). The conversion of DDM into milk was higher in H and M farms. Stepwise regression for MY selected DDM and CP (R2 = 0.716, p < 0.05). M farms were mainly constrained by nutritional factors, whereas L farms were also affected by other factors such as those related to management and health.

Characteristics of Buffalo Farming Systems in Turkey Based on a Multivariate Aggregation of Indicators: A Survey Study.

This study aimed to determine the heterogeneity that exists in water buffalo husbandry systems in Marmara Region, Turkey. A questionnaire containing a total of 60 indicators was submitted to 52 farmers. A Principal Component Analysis was performed to reduce original variables into a simplified and latent structure, which was characterized by six orthogonal components: milk productivity, economic efficiency, roughage management, dual-purpose farming, concentrate supply, and fodder production. An ANOVA model was applied to the six components to investigate the effects of the province, investment levels, grazing type, milk production, and profitability. Differences in milk productivity, roughage management, concentrate supply, and fodder management were significant according to the province and grazing type, which indicated a difference in intensification levels among the cities. Economic efficiency and dual-purpose farming differed significantly for milk production levels as well as milk productivity, and economic efficiency differed for profitability levels. We found a tendency regarding the impact of roughage management on profitability. The results conclude that profitability was associated with improving the milking traits of buffaloes and roughage management of the farms.

Assessment of the Effectiveness of a Portable NIRS Instrument in Controlling the Mixer Wagon Tuning and Ration Management.

The adoption of the mixer wagon and total mixed ration aimed to decrease dysmetabolic diseases and improve feed efficiency in dairy cows. Differences between theoretical and eaten diets are imputable to errors in diet preparation or cow feed sorting. We proposed a method to measure the chemical composition and particle size distribution of the ration and determined its peNDF content through a portable Near Infra-Red spectrophotometer that allowed the calculation of two indexes: the homogeneity and the sorting indexes. In a cohort of 19 Italian Holstein breeding farms, we studied the correlation of these indexes with the mixer wagon settings. Determination coefficients in the validation (Rv2) for dry matter, crude protein, aNDF, and starch were 0.91, 0.54, 0.86, and 0.67, respectively. The ration fractions (%, w/w of wet weight) retained by the 3.8 and 1.8 mm sieves, and the bottom showed Rv2 of 0.46, 0.49, and 0.53, respectively. The homogeneity index regressed negatively with the mixer wagon load fullness (R2 = 0.374). The homogeneity-binary classification showed an odds ratio of 1.72 for dry matter and 0.39 for aNDF (p < 0.05). The sorting-binary classification showed an odds ratio of 2.54 for aNDF (p < 0.05). The studied farms showed low peNDF values (median = 17.9%).

Machine Learning to Detect Posture and Behavior in Dairy Cows: Information from an Accelerometer on the Animal's Left Flank.

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.

Fast and Green Method to Control Frauds of Geographical Origin in Traded Cuttlefish Using a Portable Infrared Reflective Instrument.

An appropriate seafood origin identification is essential for labelling regulation but also economic and ecological issues. Near infrared (NIRS) reflectance spectroscopy was employed to assess the origins of cuttlefish caught from five fishing FAO areas (Adriatic Sea, northeastern and eastern central Atlantic Oceans, and eastern Indian and western central Pacific Oceans). A total of 727 cuttlefishes of the family Sepiidae (Sepia officinalis and Sepiella inermis) were collected with a portable spectrophotometer (902-1680 nm) in a wholesale fish plant. NIR spectra were treated with standard normal variate, detrending, smoothing, and second derivative before performing chemometric approaches. The random forest feature selection procedure was executed to select the most significative wavelengths. The geographical origin classification models were constructed on the most informative bands, applying support vector machine (SVM) and K nearest neighbors algorithms (KNN). The SVM showed the best performance of geographical classification through the hold-out validation according to the overall accuracy (0.92), balanced accuracy (from 0.83 to 1.00), sensitivity (from 0.67 to 1.00), and specificity (from 0.88 to 1.00). Thus, being one of the first studies on cuttlefish traceability using NIRS, the results suggest that this represents a rapid, green, and non-destructive method to support on-site, practical inspection to authenticate geographical origin and to contrast fraudulent activities of cuttlefish mislabeled as local.

Pyrrolizidine alkaloids in bee pollen identified by LC-MS/MS analysis and colour parameters using multivariate class modeling.

Toxic pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs) can be present in bee pollen depending on the plants visited by bees. A liquid chromatography-mass spectrometry (LC-MS/MS) method was developed and validated to monitor 17 PAs/PANOs in 44 bee pollens. The CIE-L∗a∗b∗ colour coordinates with the specular component either included or excluded were recorded in pellets and ground aliquots. Lightness (L∗) and yellowness (b∗) of ground bee pollen were significantly correlated to PAs/PANOs content. The L∗ and b∗ cut-offs sorted by a receiver operating characteristic analysis to predict PAs/PANOs presence showed a significant increase in the relative risk to detect amounts higher than 84 µg kg-1. Two supervised canonical discriminant analyses confirmed that pollen without PAs could be distinguished from those containing PAs/PANOs. The data suggest that instrumental colour coupled with supervised models could be used as a screening test for PAs/PANOs in bee pollen, before the confirmatory LC-MS/MS analysis.

Research Note: Effect of chicken genotype and white striping-wooden breast condition on breast meat proximate composition and amino acid profile.

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.

Application of near-infrared spectroscopy for frozen-thawed characterization of cuttlefish (Sepia officinalis).

Few works to date have reported the application of near-infrared spectroscopy (NIRS) for the characterization and authentication of cephalopods. This study investigated the feasibility of a portable NIRS instrument for the non-destructive freshness evaluation of fresh (F) and frozen-thawed (FT) cuttlefish (Sepia officinalis). Samples were examined by chemical, microbiological and sensorial analyses, during 13 days of conservation at 3 °C. The spectral data were collected on lateral mantle of cuttlefish, and different partial least squares discriminant analyses (PLS-DA) were applied for classification purposes. The interpretation of spectra was also investigated by applying the specific water coordinates, using aquaphotomics. Few significant differences in the wet chemistry and microbiological data were detected between F and FT during storage. The quality index method and microbiological analyses suggested similar behavior between F and FT samples until to near 9 days of shelf life. PLS-DA models with the spectral range 900-1650 nm achieved a classification precision of 0.91 between F and FT, while the performances for the prediction of storage days were less effective. The results of aquaphotomics plotted in aquagrams were suitable for the interpretation of the main physicochemical changes of cuttlefish throughout the shelf life. The water coordinates suggested a different molecular conformation of water species in the FT than F samples, with more free water molecules and a lower amount of bound species and the water solvation shell, respectively.

Spectrophotometric techniques for the characterization of strains involved in the blue pigmentation of food: Preliminary results.

Near infrared spectroscopy (NIRs) and ultraviolet visible spectroscopy (UV-vis) have been investigated as rapid techniques to characterize foodborne bacteria through the analysis of the spectra of whole cells or microbial suspensions. The use of spectra collected from broth cultures could be used as a fingerprint for strain classification using a combined polyphasic approach. The aim of this study was to evaluate the feasibility of NIRs and UV-vis for the characterization of blue strains belonging to the Pseudomonas fluorescens group. The bacteria were isolated from different food matrices, including some spoiled samples (blue discoloration). Eightyone strains previously identified at the species level were grown in Minimal Bacterial Medium broth under standard conditions at 22°C. Two biological replicates were centrifuged in order to separate the bacterial cells from the extracellular products. Six aliquots per strain were analyzed on a small ring cup in transflectance mode (680-2500 nm, gap 2 nm). A subset of 39 strains was evaluated by UV-vis to determine changes in the spectral characteristics at 48 and 72 hours. Several chemometric approaches were tested to assess the performance of NIRs and UVvis. According to the variable importance in projection (VIP), the 1892-2020 nm spectral region showed the highest level of discrimination between blue strains and others. Additional information was provided in the 680-886 and 1454-1768 nm regions (aromatic C-H bonds) and in the 2036-2134 nm region (fatty acids). Changes in UV-vis spectral data (at 48 and 72 hours) appear to indicate the presence of phenazine and catecholic compounds in extracellular products.

Study of ingredients and nutrient composition of commercially available treats for dogs.

Forty-one dog treats were selected from the market with the aim of providing more insight into supplemental pet food composition. Thirty-two products (four biscuits, nine tender treats, two meat-based strips, five rawhides, eight chewable sticks, four dental care sticks) were analysed for proximate nutrient composition and quantification of minerals, hydroxyproline (Hyp), starch, glucose, fructose and sucrose. Labelled ingredients were often expressed as non-specific categories. A treat supplied a mean of 332.0±39.2 kcal metabolisable energy (ME)/100 g, and the most energy-dense product was a tender treat (475.0 kcal ME/100 g). Small dogs receive the highest percentage of maintenance energy requirement when producers' feeding instructions are followed. Treat categories revealed variability in dry matter, crude protein, ash, Hyp and starch. Rawhides showed the highest Hyp content. Simple sugars were identified in most treats, and sucrose was the most prevalent. Results of the study suggest treat labelling should include more information on the ingredients used, and the varying nutrient and caloric density of treats should be considered. Specific attention should be given to the use of treats in dogs with specific ingredient sensitivities or nutrient considerations.

A correlative study on data from pork carcass and processed meat (Bauernspeck) for automatic estimation of chemical parameters by means of near-infrared spectroscopy.

The use of near-infrared spectroscopy (NIRS) is proposed in this study for the characterization of the quality parameters of a smoked and dry-cured meat product known as Bauernspeck (originally from Northern Italy), as well as of some technological traits of the pork carcass used for its manufacturing. In particular, NIRS is shown to successfully estimate several key quality parameters (including water activity, moisture, dry matter, ash and protein content), suggesting its suitability for real time application in replacement of expensive and time consuming chemical analysis. Furthermore, a correlative approach based on canonical correlation analysis was used to investigate the spectral regions that are mostly correlated to the characteristics of interest. The identification of these regions, which can be linked to the absorbance of the main functional chemical groups, is intended to provide a better understanding of the chemical structure of the substrate under investigation.