In this study, a comprehensive and comparative analysis was conducted on Italian Asiago-PDO cheese obtained from two different dairies named Dairy I and Dairy II using industrial and natural fermented milk, respectively. The analysis encompassed the evaluation of chemical composition, the succession of the microbiota during manufacture and ripening, and proteolysis mainly focusing on free individual amino acid (FAA) profiles. A metagenomic approach was used to investigate the cheese microbiome functionality. Differences in gross chemical composition were more evident during ripening, with Dairy II showing higher variability within batches. The microbiota varied significantly between the two dairies and ripening stages. The choice of starter culture shaped the microbiota during production and affected the microbial diversity of non-starter lactic acid bacteria (NSLAB) originated from the raw milk during ripening. Peptide chromatographic profiles and FAA concentrations increased as ripening progressed, with Dairy I showing higher production of FAA. Functional analysis of the metagenomes linked species to specific amino acid metabolism/catabolism pathways. The amino acid metabolism pathways, particularly those related to aromatic amino acids, lysine, and branched-chain amino acids, were affected by the presence of specific NSLAB species, which differed between the two dairies. The results obtained in this study reveal the impact of starter culture on peculiar cheese microbiota assemblies, which selectively targets amino acid pathways, providing insights into the potential flavor and aroma characteristics of Asiago-PDO cheese.

The rising demand for nondairy and nonanimal protein sources has increased plant-based beverages (PBB) consumption. However, research on their functional properties, metabolic profile, and discrimination potential is limited. This study evaluated the potential of proton nuclear magnetic resonance (¹H NMR) spectroscopy as an authentication method to discriminate milk (cow and goat) and PBB macro-groups, including soy-based, fruit-based (almond and coconut), and cereal-based (rice and oat) beverages, based on their metabolic profile. A total of 22 PBB (soy-, almond-, coconut-, rice-, and oat-based beverages), 4 cow milk, and 4 goat milk cartons were analyzed with ¹H NMR spectroscopy to obtain their metabolic profile. Relevant metabolites to discriminate PBB macro-groups and cow and goat milk were identified through the Mann-Whitney U test and partial least squares-discriminant analysis. Results revealed that uridine diphosphate, glucose, and adenosine were key metabolites for the identification of goat and cow milk. At the same time, choline and guanosine emerged as important markers for different PBB macro-group detection. In addition, lactose played a significant role in differentiating milk from PBB. In conclusion, these findings represent an initial step toward applying ¹H NMR spectroscopy for authentication and nutritional analysis of PBB, opening the door for further research into their authenticity and metabolic profiling.

Milk and whey are subjected to quick deterioration due to bacterial growth. Turning them into powder allows for extended shelf life, easier storage, and more effective transport. Monitoring mineral elements in dairy powders is crucial for both technological and nutritional implications. Current analysis of minerals in food matrices is time consuming and requires high costs and labor. In this view, energy dispersive x-ray fluorescence (ED-XRF) may represent a potential alternative technique to rapidly determine mineral content of food matrices. The present study aimed to investigate the effectiveness of ED-XRF technique for the prediction of Na, Mg, P, S, K, Ca, Cu, and Zn in skim milk and whey powders. Reference analysis for mineral concentration in dairy powders was inductively coupled plasma optical emission spectrometry. The accuracy of ED-XRF for the prediction of mineral elements was assessed on all available samples (global calibration set; n = 23), and after splitting the whole data in a training set (n = 16) and a testing set (n = 7), comprising 70% and 30% of the available samples, respectively. The accuracy of prediction was moderate to low for Na and Mg, whereas robust predictions were obtained for P, S, K, Ca, Cu, and Zn, with coefficients of determination in global calibration from 0.88 to 0.99, and ratios performance to deviation in testing from 2.14 to 24.74. Results suggest that ED-XRF can be considered for accurate prediction of main mineral elements in dairy powders.

Moving from conventional (CMS) to automatic (AMS) milking systems could affect milk quality. Moreover, the type and preservation methods of the forages used in the TMR, such as alfalfa hay (HTMR) or corn silage (STMR) have been demonstrated to modify milk composition. Thus, this study investigated the effect of implementing AMS and different diet forage types on the quality of Italian Holstein-Friesian bulk milk. Milk samples (n = 168) were collected monthly from 21 commercial farms in northern Italy during a period of 8 mo. Farms were categorized into 4 groups according to their milking system (CMS vs. AMS) and diet forage type (HTMR vs. STMR). Milk quality data were analyzed through the mixed procedure for repeated measurement of SAS with the milking system, diet forage type, and sampling day as fixed effects. Milking through the AMS led to lower milk fat, freezing point, and β-LG A; longer coagulation time; and higher K content, pH, and β-LG B than CMS. Cows fed STMR produced milk with greater fat, protein, casein, Mg content, titratable acidity, and β-LG A, but with reduced curd firming time, freezing point, and β-LG B than those fed HTMR. In conclusion, milk quality is not only altered by the diet's forage type and characteristics but also by the milking system.

Iodine deficiency is the leading preventable cause of neurological damage worldwide. Dairy foods represent an important dietary iodine source. This study aimed to assess iodine concentration in milk, ricotta cheese, and yogurt, and to evaluate their contribution toward the recommended daily iodine intake. Whole pasteurized milk (WM; n = 12), partially skimmed pasteurized milk (PM; n = 21), skimmed pasteurized milk (SM; n = 7), ricotta cheese (RC; n = 26), whole yogurt (WY; n = 13), and low-fat yogurt (LY; n = 15) were purchased in local stores. Samples were analyzed through inductively coupled plasma mass spectrometry for iodine quantification. After removing outliers, the final dataset comprised 11 WM, 19 PM, 7 SM, 26 RC, 13 WY and 15 LY samples. Data were investigated through a mixed model with iodine concentration as the dependent variable, product type as fixed effect, and brand as random effect. Low-fat yogurt exhibited the greatest estimated iodine concentration (293.76 µg/kg), while SM and WM exhibited the lowest (211.92 and 197.63 µg/kg, respectively). Based on these results, a serving of milk (250 g) would provide 31.82–39.08% of the average daily iodine requirement, a serving of ricotta (125 g) 21.66%, and a yogurt jar (125 g) 21.54–24.11%. These findings confirm the nutritional relevance of dairy products as primary iodine sources.

Colostrum is the first secretion produced by the mammary gland after calving, providing essential macronutrients and micronutrients to newborns. Monitoring elemental concentrations in bovine colostrum (BC) is crucial, as it serves for the physiological development and health of the neonates. Current gold-standard methods to determine elemental composition of BC are inductively coupled plasma optical-emission spectrometry or inductively coupled plasma MS. These techniques, however, are time-consuming, expensive, labor-intensive, and destructive, and they require sample preparation such as chemical matrix decomposition. In this view, energy dispersive X-ray fluorescence (ED-XRF) could represent a handy alternative. This study evaluated the effectiveness of ED-XRF for the prediction of Na, Mg, P, S, K, Ca, and Zn in BC measured through inductively coupled plasma optical-emission spectrometry as reference method. The performances of ED-XRF in predicting BC elemental composition were assessed on the entire dataset (comprising 160 samples of individual BC), on a training set (comprising 70% of the samples), and on a testing set (comprising 30% of the samples). The models demonstrated satisfactory goodness of fit for S (coefficient of determination in external validation, R²_TE = 0.77) and Zn (R²_TE = 0.74), indicating their suitability for rough screening of such elements, while showing lower association for Na, Mg, P, K, and Ca. Results of the present study could support the development of rapid screening approaches to assess S and Zn levels in BC, contributing to more informed supplementation strategies of such minerals for calves, thereby preventing deficiencies and supporting neonatal development.