Characterization of microplastics in skim-milk powders.

The diffusion of microplastics in the food supply chain is prompting public concern as their impact on human health is still largely unknown. The aim of this study was to qualitatively and quantitatively characterize microplastics in skim-milk powder samples (n = 16) from different European countries (n = 8) through Fourier-transform infrared micro-spectroscopy in attenuated total reflectance mode analysis. The present study highlights that the use of hot alkaline digestion has enabled the efficacious identification of microplastics in skim-milk powders used for cheese-making across European countries. The adopted protocol allowed detection of 29 different types of polymeric matrices for a total of 536 plastic particles. The most abundant microplastics were polypropylene, polyethylene, polystyrene, and polyethylene terephthalate. Microplastics were found in skim-milk powders in 3 different shapes (fiber, sphere, and irregular fragments) and 6 different colors (black, blue, brown, fuchsia, green, and gray). Results demonstrate the presence of microplastics in all skim-milk powder samples, suggesting a general contamination. Results of the present study will help to evaluate the impact of microplastics intake on human health.

Effectiveness of near-infrared spectroscopy to predict the chemical composition of feces and total-tract apparent nutrients digestibility estimated with uNDF or AIA in lactating buffaloes' feces.

Following a comparison of nutrient total-tract digestibility estimates in lactating buffaloes using single-point undigestible NDF (uNDF) or acid-insoluble ash (AIA) as internal markers, the potential of fecal near-infrared spectroscopy (NIRS) to provide calibration equations for the assessment of the chemical composition of feces and nutrient total-tract digestibility estimated with internal markers was explored. Chemical analyses were performed on 147 fecal samples from lactating buffaloes reared in 5 farms in central Italy (Naples). Each farm fed a silage-based total mixed ration (TMR) to the buffaloes, which was sampled in the 2 d before the fecal collection. The TMR and individual fecal samples were collected and analyzed for dry matter (DM), organic matter (OM), ash, AIA, ether extract (EE), starch, fiber fractions (aNDFom, aNDF, ADFom, ADF, hemicellulose, cellulose, ADL, uNDF), N, CP and CP bound to aNDF (NDICP) and to ADF (ADICP). The uNDF content was determined through a 240-h in vitro fermentation and employed, together with AIA as markers to estimate the total-tract apparent digestibility (ttaDe) and total-tract digestibility (ttDe) of DM, OM, ash, N, CP, EE, aNDFom, aNDF, NDIP, ADFom and ADF, ADIN, ADL, hemicellulose, cellulose, starch, non-fibrous carbohydrates (NFC) and fraction B3 of N. No correlation was found between DM and OM digestibility estimated with AIA and uNDF as internal markers. Weak correlations were detected for all the other nutients digestibilities while strong correlations were observed for EE, ADFom, HC, NDIN, ADIN, NB3, NFC and starch.Inizio modulo The sample set (n = 147) was divided in a calibration set (n = 111) and a validation set (n = 36) to "train" and "validate" the fecal NIRS curve through an external validation process. An estimation usable for preliminary or initial evaluation was obtained for N, CP and aNDF fecal content. An excellent prediction was obtained for ttADINDe (R2 = 0.90) when estimated with uNDF as internal marker. The NIRS technology was not able to accurately predict all the other traits and the estimated nutrient digestibility of lactating buffalo diets from fecal spectra.

Effects of different temperature-humidity indexes on milk traits of Holstein cows: a 10-yr retrospective study.

Test-day records (n = 723,091) collected between 2012 and 2021 from 43,015 Holsteins in 157 farms located in Northern Italy were used to study the effects of heat load on milk production and composition a posteriori. The data consisted of milk yield (kg/d), traditional gross composition traits, somatic cell score (SCS), differential SCC (%), milk ß-hydroxybutyrate (BHB, mmol/L), milk urea (mg/dL), and milk FA composition (g/100 g milk). Test-day records were then associated to their relative temperature-humidity indexes (THI) calculated using historical environmental data registered by weather stations. Indexes were created using either yearly or summer THI data: the first included the average daily THI (adTHI) and the maximum daily THI (mdTHI) measured throughout the whole year, whereas summer indexes were focused on 3 mo only (June to August) and included the average daily THI (adTHIs), the maximum daily THI (mdTHIs), and the average daily THI of the hottest 4 h of the day (adTHI4h, 12:00 to 4:00 p.m.). All indexes had significant effects on the majority of milk traits analyzed, with in particular adTHI and mdTHI being highly significant in explaining the variation of all traits. Milk yield started to decline at a higher THI compared with protein and fat content. The reduction in fat ceased in the elevated THI experienced during the summer months as demonstrated by adTHIs, mdTHIs, and adTHI4h. The cows had a tendency for increased BHB concentration with elevated THI, suggesting that there is a greater risk of negative energy balance in presence of heat stress. What is more, the concentration of the de-novo fatty acids C14:0 and C16:0 was reduced in higher THI, reflecting the altered mammary gland activity upon elevated heat load and stress. Milk SCS tended to increase with higher adTHIS, mdTHIS, and adTHI4h. The use of yearly indexes is recommended when investigating the effects of heat load on milk composition, while summer indexes are suggested when investigating traits influenced by extreme conditions, such as SCS and milk yield. With global temperatures set to further rise in the upcoming decades, early and easy identification of cows or herds suffering from heat stress, as through changes in milk composition, is crucial for timely intervention. Adapting measures to mitigate such effects of elevated THI on milk yield and composition is a necessity for the dairy industry to prevent detrimental impacts on dairy production.

Does the age of milk affect its mid-infrared spectrum and predictions?

Milk of dairy species commonly undergo standardized official analyses, these that may require chemical preservation and transportation to a certified laboratory. In this context, storage duration is an important factor that can potential affect both milk chemical analyses and its mid-infrared spectrum. We analysed milk samples at different time points/ages to assess repeatability and reproducibility of mid-infrared predicted traits (e.g., fat and protein). Using spectral data, we also evaluated the ability of spectroscopy coupled with chemometrics to discriminate samples according to their age. Although the main components of milk remained consistently reproducible across age (days), changes in the spectrum due to sample aging and deterioration of the matrix were detectable. Using a discriminant analysis, we achieved a classification accuracy of 77% in validation. Predicting milk age using mid-infrared spectra is feasible, allowing for sample monitoring within circuits where maximum reliability is needed, e.g., bulk or individual milk samples for legal/official use or payment systems.

How animal milk and plant-based alternatives diverge in terms of fatty acid, amino acid, and mineral composition.

The decline in fresh milk in the Western world has in part been substituted by an increased consumption of plant-based beverages (PBB). These are often marketed as healthy and sustainable alternatives to milk and dairy foodstuff, although studies have suggested PBB to be of lower nutrient quality. The current study considered different brands of almond-, oat-, rice-, coconut- and soya-based beverages for a comparative analysis and found that they indeed presented lower contents of total protein, lipids, amino acids, and minerals than cow and goat milk. The only exception was given by soya-based beverages which approximated the protein content (3.47% vs. 3.42 and 3.25% in cow and goat milk, respectively) and amino acid composition of animal milk, and also demonstrated high mineral content. The natural presence of phyto-compounds in PBB characterised as antinutrients and their potential to exacerbate the issue of low nutrient quality by lowering bioavailability have been discussed.

Minerals and essential amino acids of bovine colostrum: Phenotypic variability and predictive ability of mid- and near-infrared spectroscopy.

Colostrum quality and volume are fundamental for calves because it is the primary supplier of antibodies and the first source of energy, carbohydrates, lipids, proteins, minerals, and vitamins for the newborn. Assessing the detailed composition (i.e., AA and mineral content) of bovine colostrum (BC) on-line and at a reasonable cost would help dairy stakeholders such as farmers or veterinarians for precision feeding purposes and industries producing preparations containing BC such as foodstuff, supplements, and medicaments. In the present study we evaluated mid- (MIRS) and near-infrared spectroscopy (NIRS) prediction ability for AA and mineral composition of individual BC. Second, we the investigated the major factors affecting the phenotypic variability of such traits also evaluating the correlations with the Ig concentration. Results demonstrated that MIRS and NIRS were able to provide sufficiently accurate predictions for all the AA. The coefficient of determination in external validation (R2V) fell, in fact, within the range of 0.70 to 0.86, with the exception of Ile, His, and Met. Only some minerals reached a sufficient accuracy (i.e., Ca, P, S, and Mg; R2V ≥ 0.66) using MIRS, and also S (R2V = 0.87) using NIRS. Phenotypically, both parity and calving season affected the variability of these BC composition traits. Heifers' colostrum was the one with the greatest concentration of Ca and P, the 2 most abundant minerals. These minerals were however very low in cows calving in summer compared with the rest of the year. The pattern of AA across parities and calving season was not linear, likely because their variability was scarcely (or not) affected by these effects. Finally, samples characterized by high IgG concentration were those presenting on average greater concentration of AA. Findings suggest that infrared spectroscopy has the potential to be used to predict certain AA and minerals, outlining the possibility of implementing on-site analyses for the evaluation of the broad-sense BC quality.

A short-term comparison of wheat straw and poplar wood chips used as litter in tiestalls on hygiene, milk, and behavior of lactating dairy cows.

A short-term study was conducted to compare the effect of using poplar wood chips (PWC) instead of wheat straw (WS) litter in dairy cows. A total of 38 lactating Holstein cows (204 ± 119 days in milk, 26.9 ± 6.5 kg of milk yield [MY]) were housed in a tiestall farm for a 10-d trial including 5 d of adaptation followed by 5 sampling days (from d 5 to 10). Cows were divided into 2 homogeneous groups: one group was bedded with WS, and the second with PWC. Both litter materials were provided in the amount of 7 kg/stall per d. Each group was composed of 3 subgroups of 6 or 7 cows; the subgroups were physically separated along the feeding line by wooden boards. During the sampling days, fecal composition, used litter composition, and bacterial count (Clostridium spp., Salmonella spp., Escherichia coli, Lactobacillus, and total bacterial count) were analyzed by subgroup twice a day. On d 1 and from d 5 to 10, udder hygiene score and cow cleanliness score were also evaluated individually twice a day. Meanwhile MY, milk hygiene (total bacterial count [TBC], coliform bacterial count [CBC], and spore-forming units [SFU]) and quality were measured and analyzed from 9 animals per group. Moreover, individual animal behavior (body position and behavioral traits) and subgroup dry matter intake were measured on d 9 and 10. Fecal dry matter did not differ between groups, PWC had the lowest used litter moisture and N content favoring the highest clean cow frequency, but also gave rise to the greatest used litter microbial contamination. The MY, milk quality, TBC, SFU, and CBC were similar. The lying behavior frequency was similar between groups. However, the PWC group showed the lowest sleeping frequency, the highest frequency of other behaviors (including discomfort signs), and the lowest dry matter intake. However, despite this apparent reduction in cow comfort, no biologically important differences were observed in this short-term study between cows on PWC and WS in milk production or hygiene.

Mid-infrared spectroscopy for large-scale phenotyping of bovine colostrum gross composition and immunoglobulin concentration.

Immunoglobulin G is the fundamental antibody for acquisition of passive transfer of immunity in ruminant newborns. Colostrum, in fact, must be administered as soon as possible after birth to ensure a successful transfer of IgG from the dam to the calf. Assessment of colostrum Ig concentration and gross composition via gold standards is expensive, time consuming, and hardly implementable for large-scale investigations. Therefore, in the present study we evaluated the predictive ability of mid-infrared spectroscopy (MIRS) as an indirect determination method. A total of 714 colostrum samples collected within 6 h from parturition from Italian Holstein cows, 30% primiparous and 70% pluriparous, were scanned using a benchtop spectrometer after dilution in pure water. The prediction models were developed by correlating spectral information with the reference measurements: IgG concentration (93.54 ± 33.87 g/L), total Ig concentrations (102.82 ± 35.04 g/L), and content of protein (14.71 ± 3.51%), fat (4.61 ± 3.04%), and lactose (2.36 ± 0.51 mg/100 mg). We found a good to excellent performance in prediction of colostrum IgG concentration and traditional composition traits in cross-validation (R2CV ≥ 0.92) and a promising and good predictive ability in external validation with R2V equal to 0.84, 0.89, and 0.74 for IgG, protein, and fat, respectively. In the case of IgG and protein content, for example, the coefficient of determination in external validation was greater than 0.84. The other Ig fractions, A and M, presented insufficient prediction accuracy likely due to their extremely low concentration compared with IgG (4.56 and 5.06 g/L vs. 93.54 g/L). The discriminant ability of MIRS-predicted IgG and protein content was outstanding when trying to classify samples according to the quality level (i.e., low vs. high concentration of IgG). In particular, the cut-off that better discriminate low- from high-quality colostrum was 75.40 g/L in the case of the MIRS-predicted IgG and 13.32% for the MIRS-predicted protein content. Therefore, MIRS is proposed as a rapid and cheap tool for large-scale punctual IgG, protein, and lactose quantification and for the screening of low-quality samples. From a practical perspective, there is the possibility to install colostrum models in the MIRS benchtop machineries already present in laboratories in charge of official milk testing. Colostrum phenotypes collected on an individual basis will be useful to breeders for the definition of specific selection strategies and to farmers for management scopes. Finally, our findings may be relevant for other stakeholders, given the fact that colostrum is an emerging ingredient for the animal and human food and pharmaceutical industry.

Assessment of energy dispersive X-ray fluorescence (ED-XRF) for quantification of iodine in non-lyophilized milk.

Iodine represents a fundamental element for human health, with particular regard to thyroid function. Dietary intake of milk naturally rich in iodine becomes of primary importance in the prevention of syndromes related to iodine deficiency. The concentration of iodine in milk is characterized by wide variability, mainly related to animal feed and level of mineral supplementation. Therefore, there is interest in the development of fast analytical techniques which are able to predict milk iodine concentration. The aim of the present study was to investigate the effectiveness of energy-dispersive X-ray fluorescence (ED-XRF) for the prediction of iodine in cow milk. Results showed moderate accuracy of the ED-XRF technique, with a coefficient of determination in cross validation of 0.60. This study represents a first contribution towards the possibility to discriminate milk with high or low iodine concentration, as an essential preliminary step for the introduction into the market of naturally fortified milk.

Effectiveness of mid-infrared spectroscopy for the prediction of cow milk metabolites.

Proton nuclear magnetic resonance (1H NMR) spectroscopy is acknowledged as one of the most powerful analytical methods with cross-cutting applications in dairy foods. To date, the use of 1H NMR spectroscopy for the collection of milk metabolic profile is hindered by costly and time-consuming sample preparation and analysis. The present study aimed at evaluating the accuracy of mid-infrared spectroscopy (MIRS) as a rapid method for the prediction of cow milk metabolites determined through 1H NMR spectroscopy. Bulk milk (n = 72) and individual milk samples (n = 482) were analyzed through one-dimensional 1H NMR spectroscopy and MIRS. Nuclear magnetic resonance spectroscopy identified 35 milk metabolites, which were quantified in terms of relative abundance, and MIRS prediction models were developed on the same 35 milk metabolites, using partial least squares regression analysis. The best MIRS prediction models were developed for galactose-1-phosphate, glycerophosphocholine, orotate, choline, galactose, lecithin, glutamate, and lactose, with coefficient of determination in external validation from 0.58 to 0.85, and ratio of performance to deviation in external validation from 1.50 to 2.64. The remaining 27 metabolites were poorly predicted. This study represents a first attempt to predict milk metabolome. Further research is needed to specifically address whether developed prediction models may find practical application in the dairy sector, with particular regard to the screening of dairy cows' metabolic status, the quality control of dairy foods, and the identification of processed milk or incorrectly stored milk.

Invited review: Bovine colostrum, a promising ingredient for humans and animals-Properties, processing technologies, and uses.

Mammalian colostrum, known as "liquid gold," is considered a valuable source of essential nutrients, growth factors, probiotics, prebiotics, antibodies, and other bioactive compounds. Precisely for this reason, bovine colostrum (BC) is an emerging ingredient for the feed, food, and pharmaceutical industries, being nowadays commercially available in a variety of forms in several countries. Moreover, quite a large number of functional foods and supplements for athletes, human medicines, pet nutrition plans, and complementary feed for some livestock categories, such as piglets and calves, contain BC. The amount of BC yielded by a cow after calving represents approximately 0.5% of the yearly output in dairy breeds. For its nutritional properties and low availability, BC is characterized by a greater market value and an increasing demand compared with other by-products of the dairy sector. However, information regarding the market size of BC for the food and pharmaceutical industries, as well as future developments and perspectives, is scarcely available in the scientific literature. This lack can be attributed to industrial secrecy as well as to the relatively small scale of the BC business when compared with other dairy products, which makes the BC market limited, specific, and intended for a restricted audience. From a legal perspective, regulations assign BC to the large family of milk-derived powders; thus, collecting specific production data, as well as import-export trend information, is not straightforward and can result in unprecise estimates. Given that the interest in BC is increasing in different fields, it is important to have an overview of the production steps and of pros and cons of this emerging ingredient. The present narrative review discloses why BC has started to be considered a product rather than a by-product of the dairy industry. Moreover, the present document aims to summarize the existing methodologies used to assess BC quality in terms of immunoglobulin concentration, the different applications of BC in the industry, and the BC processing technologies. Finally, a panoramic view of the current international market is provided for the first time for this dairy product.

Comparison of chemical composition of organic and conventional Italian cheeses from parallel production.

Although there are several studies comparing organic and conventional milk characteristics, very few focused on dairy processed products such as cheese. Thus, this study aimed for a detailed controlled examination of gross composition, minerals, and the fatty acid profile of organic (ORG) and conventional (CON) Italian cheeses from parallel production. Four Italian cheese types were analyzed: Latteria (ORG, n = 9; CON, n = 10); Asiago Protected Designation of Origin (PDO) fresco (ORG, n = 9; CON, n = 9); Caciotta (ORG, n = 8; CON, n = 8); and Mozzarella Traditional Specialty Guaranteed (TSG; ORG, n = 14; CON, n = 14). Cheese samples were collected from September 2020 to August 2021. Gross composition, minerals, and fatty acids were determined using infrared spectroscopy. Within each cheese type, paired ORG and CON samples were compared using a nonparametric Wilcoxon signed-rank test. Latteria showed lower PUFA, n-3, and n-6 content, and greater Fe, K, C10:0, C12:0, and C16:0 content in ORG than in CON. Asiago PDO fresco showed lower protein and Zn content and greater salt, ash, and Na content in ORG than in CON. Caciotta showed lower ash, n-3, and n-6 content and greater K, C4:0, C8:0, C10:0, C14:0, and C16:0 content in ORG than in CON. Mozzarella TSG showed lower fat and, therefore, fatty acid content, and greater moisture, ash, and Mg content in ORG than in CON. In conclusion, few significant differences in chemical composition were observed between ORG and CON cheeses, regardless of the type considered. Moreover, Asiago PDO fresco showed fewer significant differences between ORG and CON compared with Latteria, Caciotta, and Mozzarella TSG.

Udder health-related traits in cow milk: phenotypic variability and effect on milk yield and composition.

The milk differential somatic cell count (DSCC) has been proposed in recent years as a mean by which to better monitor the udder health status (UHS) in dairy cows. Milk DSCC is the amount of polymorphonuclear neutrophils and lymphocytes contributing to the total somatic cell count (SCC) and can be determined on a routine basis in individual milk samples subjected to official analysis. In the present study, 522 865 milk test-day records of 77 143 cows were scrutinised to identify factors affecting the variability of both DSCC and SCC in Holstein Friesian, Jersey, Simmental and Rendena cows through linear mixed models. The fixed effects were breed, parity, lactation stage, sampling season, and all the first-order interactions of breed. Cow and herd-test-date were considered as random. Subsequently, four UHS groups were created (1: SCC ≤ 200 000 cells/mL and DSCC ≤ 65%; 2: SCC ≤ 200 000 cells/mL and DSCC > 65%; 3: SCC > 200 000 cells/mL and DSCC > 65%; 4: SCC > 200 000 cells/mL and DSCC ≤ 65%) to compare milk yield and quality. Milk SCS and DSCC differed across lactation, parity, sampling season and breed. In particular, Simmental cows had the lowest SCC and Jersey the lowest DSCC. Depending on the breed, UHS affected daily milk yield and composition to a different extent. The UHS group 4, i.e. the one grouping test-day records with high SCC and low DSCC, presented the lowest estimate of milk yield and lactose content no matter the breeds. Our findings support that udder health-related traits (SCS and DSCC) are useful information to improve udder health at individual cow and herd levels. Moreover, the combination of SCS and DSCC is useful to monitor milk yield and composition.

How heat stress conditions affect milk yield, composition, and price in Italian Holstein herds.

An edited data set of 700 bulk and 46,338 test-day records collected between 2019 and 2021 in 42 Holstein-dominated farms in the Veneto Region (North of Italy) was available for the present study. Information on protein, fat and lactose content, somatic cell count, and somatic cell score was available in bulk milk as well as individual test-day records, whereas urea concentration (mg/dL), differential somatic cell count (%), and milk yield (kg/d) were available for test-day records only. Milk features were merged with meteorological data retrieved from 8 weather stations located maximum 10 km from the farms. The daily and weekly temperature-humidity index (THI; wTHI) and maximum daily (MTHI) and weekly temperature-humidity index were associated with each record to evaluate the effect of heat stress conditions on milk-related traits through linear mixed models. Least squares means were estimated to evaluate the effect of THI and, separately, of MTHI on milk characteristics correcting for conventional systematic factors. Overall, heat stress conditions lowered the quality of both bulk milk and test-day records, with fat and protein content being greatly reduced, and somatic cell score and differential somatic cell count augmented. Milk yield was not affected by either THI or MTHI in this data set, but the effect of elevated THI and MTHI was in general stronger on test-day records than on bulk milk. Farm-level economic losses of reduced milk quality rather than reduced yield as consequence of elevated THI or MTHI was estimated to be between $23.57 and $43.98 per farmer per day, which is of comparable magnitude to losses resulting from reduced production. Furthermore, MTHI was found to be a more accurate indicator of heat stress experienced by a cow, explaining more variability of traits compared with THI. The negative effect of heat stress conditions on quality traits commences at lower THI/MTHI values compared with milk yield. Thus, a progressive farmers' income loss due to climatic changes is already a reality and it is mainly due to deterioration of milk quality rather than quantity in the studied area.

Invited review: Iodine level in dairy products-A feed-to-fork overview.

The theme of iodine in the dairy sector is of particular interest due to the involvement and the interconnection of several stakeholders along the dairy food chain. Iodine plays a fundamental role in animal nutrition and physiology, and in cattle it is an essential micronutrient during lactation and for fetal development and the calf's growth. Its correct use in food supplementation is crucial to guarantee the animal's recommended daily requirement to avoid excess intake and long-term toxicity. Milk iodine is fundamental for public health, being one of the major sources of iodine in Mediterranean and Western diets. Public authorities and the scientific community have made great efforts to address how and to what extent different drivers may affect milk iodine concentration. The scientific literature concurs that the amount of iodine administered through animal feed and mineral supplements is the most important factor affecting its concentration in milk of most common dairy species. Additionally, farming practices related to milking (e.g., use of iodized teat sanitizers), herd management (e.g., pasture vs. confinement), and other environmental factors (e.g., seasonality) have been identified as sources of variation of milk iodine concentration. Overall, the aim of this review is to provide a multilevel overview on the mechanisms that contribute to the iodine concentration of milk and dairy products.

Grazing affects metabolic pattern of individual cow milk.

Effective traceability tools able to characterize milk from pasture are important to safeguard low-input farming systems, niche dairy products, and local traditions. The aims of the present study were to investigate the ability of proton nuclear magnetic resonance (1H NMR) spectroscopy to discriminate between milk produced from cows before and after the beginning of the grazing season, and to assess the effects of grazing on milk metabolites. The research trial involved a single alpine holding with 72 lactating cows. Individual milks were repeatedly sampled from the same animals before (i.e., d -3 and -1) and after (i.e., d 2, 3, 7, 10, and 14) the onset of the grazing period. One-dimensional 1H NMR spectra of milk extracts were collected through a Bruker spectrometer. Random forest discriminant analysis was applied to 1H NMR spectra to predict the period of collection for each sample. Data concerning the relative abundance of milk metabolites were analyzed through a linear mixed model, which included the fixed effects of period of sampling, cow breed, stage of lactation, and parity, and the random effect of cow nested within breed. The random forest model exhibited great accuracy (93.1%) in discriminating between samples collected on d -3, -1, 2, and 3 and those collected on d 7, 10, and 14. Univariate analysis performed on the 40 detected metabolites highlighted that milk samples from pasture had lower levels of 14 compounds (with fumarate being the most depressed metabolite) and greater levels of 15 compounds (with methanol and hippurate being the most elevated metabolites). Results indicate that milk 1H NMR spectra are promising to identify milk produced in different conditions. Also, our study highlights that grazing is associated with significant changes of milk metabolic profile, suggesting the potential use of several metabolites as indicators of farm management.

Effects of preservative, storage time, and temperature of analysis on detailed milk protein composition determined by reversed-phase high-performance liquid chromatography.

Milk preservative and freezing are used as strategies to prevent microbial growth and milk degradation, especially when immediate analytical processing is not feasible. The effects of the addition of preservative and freezing procedures have been investigated mainly in relation to milk gross chemical composition predicted through mid-infrared spectroscopy. This study aimed to determine whether different preservatives (i.e., no preservative, hydrogen peroxide, Bronopol, and Azidiol), freezing times (i.e., 0, 7, and 30 d), and temperatures of analysis (i.e., 5 and 21°C) influence the composition of milk protein fractions determined through reversed-phase HPLC. Bulk milk samples for the analysis of protein profile were collected from 5 commercial dairy farms. Data were analyzed with a linear mixed model, which included type of preservative, time of storage, temperature of analysis, and the interaction between type of preservative and time of storage as fixed effects, with the farm and the residual as random effects. Samples with no preservative had the greatest amount of all protein fractions, whereas Bronopol-preserved milk had the lowest amount. Increasing storage time under freezing conditions had a nonlinear detrimental effect on milk protein fractions. The temperature of analysis significantly contributed to the variation of κ-casein, ß-casein, αS1-casein, ß-lactoglobulin, and α-lactalbumin fractions. The z-scores were calculated to evaluate the similarity between detailed protein profile of fresh milk without preservative analyzed at 5°C and detailed protein profile of milk treated according to the tested conditions. Overall results suggested a good agreement between different analytical conditions. Still, short storage time under freezing conditions is recommended to avoid degradation of milk protein fractions and consequent analytical underestimation.

Characterization of the genetic polymorphism linked to the ß-casein A1/A2 alleles using different molecular and biochemical methods.

The 2 major subvariants of ß-casein (A1 and A2), coded by CSN2 gene, have received great interest in the last decade both from the scientific community and the dairy sector due to their influence on milk quality. The consumption of the A1 variant, compared with the A2 variant, has a potential negative effect on human health after its digestion but, at the same time, its presence improves the milk technological properties. The aim of the present study was to compare the best method in terms of time required, costs, and technical engagement for the identification of ß-casein A1 and A2 variants (homozygous and heterozygous animals) in milk to offer a reliable service for large-scale screening studies. Two allele-specific PCR procedures, namely RFLP-PCR and amplification refractory mutation system (ARMS-PCR), and one biochemical technique (HPLC) were evaluated and validated through sequencing. Manual and automated DNA extraction protocols from milk somatic cells were also compared. Automated DNA extraction provided better yield and purity. Chromatographic analysis was the most informative and the cheapest method but unsuitable for large-scale studies due to lengthy procedures (45 min per sample). Both allele-specific PCR techniques proved to be fast and reliable for differentiating between A1 and A2 variants but more expensive than HPLC analysis. Specifically, RFLP-PCR was the most expensive and labor-demanding among the evaluated techniques, whereas ARMS-PCR was the fastest while also requiring less technical expertise. Overall, automated extraction of DNA from milk matrix combined with ARMS-PCR is the most suitable technique to provide genetic characterization of the CSN2 gene on a large scale.

Detailed comparison between organic and conventional milk from Holstein-Friesian dairy herds in Italy.

Several studies have reported gross composition differences between organic and conventional milk; however, most studies have not considered other factors such as breed and diet ingredients, which are known to influence milk composition. Thus, this study aimed to provide a detailed characterization of Holstein-Friesian cow milk from organic (ORG) and conventional (CONV) herds with similar diet ingredients and in the same geographic area. Bulk milk samples (n = 225) of 12 ORG and 12 CONV farms were collected from September 2019 to August 2020. Farms were located in Northern Italy, included corn (meal, silage, or both) in the lactating diets, and had similar management conditions, but ORG herds spent a period on pasture. Factors affecting milk composition were tested using a linear mixed model, which included calendar month, farming system (ORG and CONV), and their interactions as fixed effects, and farm nested within farming system as random effect. Results showed that total fat, lactose, vitamin E, and AA did not significantly differ between farming systems. Total protein and casein contents were significantly lower in ORG than CONV herds, and somatic cell score (SCS) was greater in ORG than CONV. Among minerals, differences were observed for Fe, K, Mg, and S in some months, being lower in ORG than CONV for K, Mg, and S and greater or lower for Fe depending on the month. Among fatty acid (FA) groups, index, and ratios, only polyunsaturated FA and n-3 FA tended to be greater in ORG than CONV, and cis-FA were greater in ORG than CONV during October. Among the most abundant individual FA, only C16:1n-9 differed, being lower in ORG than CONV. The calendar month (and hence seasonal feed ration) was significant for milk gross composition, SCS, vitamin E, mineral profile (except for Mo, Sr, and Zn), AA profile, FA groups (except for medium-chain FA), FA index and ratios, and individual FA (except C16:0). We conclude that the overall milk composition was quite similar between the 2 farming systems. This could be related to the similarity of the selected farms, the Holstein-Friesian breed, and generally high level of intensity in both farming systems.

Effectiveness of visible - Near infrared spectroscopy coupled with simulated annealing partial least squares analysis to predict immunoglobulins G, A, and M concentration in bovine colostrum.

Visible - near infrared spectroscopy coupled with variable selection using simulated annealing PLS regression was tested to predict immunoglobulin fractions (g/L) of bovine colostrum, namely IgG, IgA and IgM. Immunoglobulins were quantified in 678 samples using the gold standard radial immunodiffusion. Samples were divided in calibration (50%) and validation (50%) datasets. Maximum number of selected variables were limited to 200 and root mean squared error in cross validation (RMSECV) was used as loss function. Performance of the final model developed using the calibration dataset was assessed on the validation dataset. Overall, simulated annealing PLS improved validation RMSECV compared to ordinary PLS regression by 3% to 17%. The present study demonstrated the effectiveness of the calibration model for accurate quantification of IgG, the most abundant immunoglobulin of bovine colostrum (RMSECV = 13.28 g/L; R2 = 0.83). These outcomes could be useful to assess colostrum quality intended for animal and human usage.