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Computational fluid dynamics (CFD) may aid the design of barn ventilation systems by simulating indoor cattle thermal welfare. In the literature, CFD models of mechanically and naturally ventilated barns are proposed separately. Hybrid ventilation relies on cross effects between air change mechanisms that cannot be studied using existing models. The objective of this study was to develop a CFD methodology for modelling animal thermal comfort in hybrid ventilated barns. To check the capability of CFD as a design evaluation tool, a real case study (with exhaust blowers) and an alternative roof layout (with ridge gaps) were simulated in summer and winter weather. Typical phenomena of natural and mechanical ventilation were considered: buoyancy, solar radiation, and wind together with high-speed fans and exhaust blowers. Cattle thermal load was determined from a daily animal energy balance, and the assessment of thermal welfare was performed using thermohygrometric indexes. Results highlight that the current ventilation layout ensures adequate thermal welfare on average, despite large nonuniformity between stalls. The predicted intensity of heat stress was successfully compared with experimental measurements of heavy breathing duration. Results show strong interactions between natural and mechanical ventilation, underlining the need for an integrated simulation methodology.

In this work, milk samples collected in a cohort of intensive dairy farms of the Po Valley (Italy) were screened for their (poly)-phenolic profile to check the occurrence of phenolic metabolites of biological interest. The selected dairy farms were previously classified on the basis of their cow feeding system, considering the utilization of corn silage as the main ingredient of the rations. Overall, ultra-high-pressure liquid chromatography coupled with mass spectrometry using an Orbitrap analyzer, followed by unsupervised and supervised statistics, allowed identifying clear different phenolic distributions in the milk samples. Accordingly, a great variability in the phenolic profiles of the different milk samples was observed, with two main phenolic clusters outlined by the unsupervised hierarchical clustering approach and not fully correlated to the nutritional strategy considered. The variables’ importance in the projection approach allowed selecting the most important metabolites, resulting in samples’ discrimination. Among the most discriminative compounds, we found phenolic metabolites (such as hippuric acid and 4-hydroxyhippuric acid), followed by lignans (such as enterolactone) and isoflavonoids (such as equol and O-desmethylangolensin). Taken together, our findings suggested that both the feeding systems and the ability of dairy cows to process parent phenolic compounds were the main factors providing the final (poly)-phenolic profile of the milk samples. Future targeted and ad hoc studies appear of great interest to evaluate the potential biological effects of these compounds on cow health.

In this work, a retrospective screening based on ultra-high-performance liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry (HRMS) based on Orbitrap-Q-Exactive Focus™ was used to check the occurrence of regulated and emerging mycotoxins in bulk milk samples. Milk samples were collected from dairy farms in which corn silage was the main ingredient of the feeding system. The 45 bulk milk samples were previously analyzed for a detailed untargeted metabolomic profiling and classified into five clusters according to the corn silage contamination profile, namely: (1) low levels of Aspergillus- and Penicillium-mycotoxins; (2) low levels of fumonisins and other Fusarium-mycotoxins; (3) high levels of Aspergillus-mycotoxins; (4) high levels of non-regulated Fusarium-mycotoxins; (5) high levels of fumonisins and their metabolites. Multivariate statistics based on both unsupervised and supervised analyses were used to evaluate the significant fold-change variations of the main groups of mycotoxins detected when comparing milk samples from clusters 3, 4, and 5 (high contamination levels of the corn silages) with cluster 1 and 2 (low contamination levels of the corn silages). Overall, 14 compounds showed a significant prediction ability, with antibiotic Y (VIP score = 2.579), bikaverin (VIP score = 1.975) and fumonisin B2 (VIP score = 1.846) being the best markers. The k-means clustering combined with supervised statistics showed two discriminant groups of milk samples, thus revealing a hierarchically higher impact of the whole feeding system (rather than the only corn silages) together with other factors of variability on the final mycotoxin contamination profile. Among the discriminant metabolites we found some Fusarium mycotoxins, together with the tetrapeptide tentoxin (an Alternaria toxin), the α-zearalenol (a catabolite of zearalenone), mycophenolic acid and apicidin. These preliminary findings provide new insights into the potential role of UHPLC-HRMS to evaluate the contamination profile and the safety of raw milk to produce hard cheese.

In this study, an untargeted metabolomics approach based on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) was used for investigating changes in chemical profiles of cow milk considering diets based on mycotoxins-contaminated corn silages. For this purpose, 45 milk samples were classified into five clusters according to the corn silage contamination profile, namely (1) low levels of Aspergillus- and Penicillium-mycotoxins; (2) low levels of fumonisins and other Fusarium-mycotoxins; (3) high levels of Aspergillus-mycotoxins; (4) high levels of non-regulated Fusarium-mycotoxins; (5) high levels of fumonisins and their metabolites, and subsequently analyzed by UHPLC-HRMS followed by a multivariate statistical analysis (both unsupervised and supervised statistical approaches). Overall, the milk metabolomic profile highlighted potential correlations between the quality of contaminated corn silages (as part of the total mixed ration) and milk composition. Metabolomics allowed to identify 628 significant milk metabolites as affected by the five levels of corn silage contamination considered, with amino acids and peptides showing the highest metabolite set enrichment (134 compounds). Additionally, 78 metabolites were selected as the best discriminant of the prediction model built, possessing a variable importance in projection score >1.2. The average Log Fold-Change variations of the discriminant metabolites provided evidence that sphingolipids, together with purine and pyrimidine-derived metabolites were the most affected chemical classes. Also, metabolomics revealed a significant accumulation of oxidized glutathione in milk samples belonging to the silage cluster contaminated by emerging Aspergillus toxins, likely involved in the oxidative imbalance. These preliminary findings provide new insights into the potential role of milk metabolomics to provide chemical indicators of mycotoxins-contaminated corn silage feeding systems.

The chemical composition of milk can be significantly affected by different factors across the dairy supply chain, including primary production practices. Among the latter, the feeding system could drive the nutritional value and technological properties of milk and dairy products. Therefore, in this work, a combined foodomics approach based on both untargeted metabolomics and metagenomics was used to shed light onto the impact of feeding systems (i.e., hay vs. a mixed ration based on hay and fresh forage) on the chemical profile of raw milk for the production of hard cheese. In particular, ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF) was used to investigate the chemical profile of raw milk (n = 46) collected from dairy herds located in the Po River Valley (Italy) and considering different feeding systems. Overall, a total of 3320 molecular features were putatively annotated across samples, corresponding to 734 unique compound structures, with significant differences (p < 0.05) between the two feeding regimens under investigation. Additionally, supervised multivariate statistics following metabolomics-based analysis allowed us to clearly discriminate raw milk samples according to the feeding systems, also extrapolating the most discriminant metabolites. Interestingly, 10 compounds were able to strongly explain the differences as imposed by the addition of forage in the cows’ diet, being mainly glycerophospholipids (i.e., lysophosphatidylethanolamines, lysophosphatidylcholines, and phosphatidylcholines), followed by 5-(3′,4′-Dihydroxyphenyl)-gamma-valerolactone-4′-O-glucuronide, 5a-androstan-3a,17b-diol disulfuric acid, and N-stearoyl glycine. The markers identified included both feed-derived (such as phenolic metabolites) and animal-derived compounds (such as lipids and derivatives). Finally, although characterized by a lower prediction ability, the metagenomic profile was found to be significantly correlated to some milk metabolites, with Staphylococcaceae, Pseudomonadaceae, and Dermabacteraceae establishing a higher number of significant correlations with the discriminant metabolites. Therefore, taken together, our preliminary results provide a comprehensive foodomic picture of raw milk samples from different feeding regimens, thus supporting further ad hoc studies investigating the metabolomic and metagenomic changes of milk in all processing conditions.

Lectins, phytic acid and condensed tannins exert major antinutritional effects in common bean when grains are consumed as a staple food. In addition, phaseolin, i.e., the major storage protein of the bean seed, is marginally digested when introduced in the raw form. Our breeding target was to adjust the nutrient/antinutrient balance of the bean seed for obtaining a plant food with improved nutritional value for human consumption. In this study, the seeds of twelve phytohaemagglutinin-E-free bean lines carrying the mutations low phytic acid, phytohaemagglutinin-L-free, α-Amylase inhibitors-free, phaseolin-free, and reduced amount of condensed tannins, introgressed and differently combined in seven genetic groups, were analyzed for their nutrient composition. Inedited characteristics, such as a strong positive correlation (+0.839 **) between the genetic combination “Absence of phaseolin + Presence of the α-Amylase Inhibitors” and the amount of “accumulated iron and zinc”, were detected. Three lines carrying this genetic combination showed a much higher iron content than the baseline (+22.4%) and one of them in particular, achieved high level (+29.1%; 91.37 µg g−1) without any specific breeding intervention. If confirmed by scientific verification, the association of these genetic traits might be usefully exploited for raising iron and zinc seed content in a bean biofortification breeding program.