Advertisement

Grazing affects metabolic pattern of individual cow milk

  • G. Niero
    Affiliations
    Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy
    Search for articles by this author
  • G. Meoni
    Affiliations
    Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff,” University of Florence, 50019 Sesto Fiorentino, Italy

    Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy
    Search for articles by this author
  • L. Tenori
    Affiliations
    Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff,” University of Florence, 50019 Sesto Fiorentino, Italy

    Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy
    Search for articles by this author
  • C. Luchinat
    Affiliations
    Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff,” University of Florence, 50019 Sesto Fiorentino, Italy

    Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy
    Search for articles by this author
  • G. Visentin
    Correspondence
    Corresponding author
    Affiliations
    Department of Veterinary Medical Sciences, Alma Mater Studiorum University of Bologna, 40064 Ozzano dell'Emilia, Italy
    Search for articles by this author
  • S. Callegaro
    Affiliations
    Associazione Nazionale Allevatori delle Razze Bovine Charolaise e Limousine Italiane (ANACLI), 00187 Roma, Italy
    Search for articles by this author
  • E. Visentin
    Affiliations
    Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy
    Search for articles by this author
  • M. Cassandro
    Affiliations
    Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy

    Italian Holstein, Brown Swiss and Jersey National Breeders Association (ANAFIBJ), 26100 Cremona, Italy
    Search for articles by this author
  • M. De Marchi
    Affiliations
    Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy
    Search for articles by this author
  • M. Penasa
    Affiliations
    Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy
    Search for articles by this author
Open AccessPublished:October 25, 2022DOI:https://doi.org/10.3168/jds.2022-22072

      ABSTRACT

      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.

      Key words

      INTRODUCTION

      The use of pasture as a source of fresh forages for cattle is commonly adopted in the context of dairy farming in mountainous areas. Fresh herbages are administered as animal feed through different strategies, depending on farm location, management, and facilities. Usually, in intensive farming systems, summer forages are mowed, transported to the barn, and mixed with other feed ingredients as fresh herbages, hay, or silage. By contrast, in low-input farming systems that are widely diffused in marginal or mountainous areas, dairy farmers allow the herd to have direct access to pasture. Depending on the geographical position of the barn, grazing can be practiced according to different strategies: the first is adopted when pastures are adjacent to the barn, allowing animals to autonomously move from barn to pastures after morning and evening milking (
      • Niero G.
      • Bobbo T.
      • Callegaro S.
      • Visentin G.
      • Pornaro C.
      • Penasa M.
      • Cozzi G.
      • De Marchi M.
      • Cassandro M.
      Dairy cows' health during alpine summer grazing as assessed by milk traits, including differential somatic cell count: A case study from Italy.
      ); the second, known as transhumance, is adopted when pastures are far from the barn, and includes active moving or passive transport of animals from the lowland barn to highland pastures (
      • Niero G.
      • Koczura M.
      • De Marchi M.
      • Currò S.
      • Kreuzer M.
      • Turille G.
      • Berard J.
      Are cheese-making properties of dual purpose cattle impaired by highland grazing? A case study using Aosta Red Pied cows.
      ). Regardless of the scenario, it is acknowledged that the inclusion of fresh forages in animal diet is threatened by high labor demands and relatively low financial returns. Still, this practice remains of strategic significance due to its direct and indirect implications, ranging from environmental sustainability and animal welfare (
      • Burow E.
      • Rousing T.
      • Thomsen P.T.
      • Otten N.D.
      • Sørensen J.T.
      Effect of grazing on the cow welfare of dairy herds evaluated by a multidimensional welfare index.
      ;
      • Byrnes R.C.
      • Eastburn D.J.
      • Tate K.W.
      • Roche L.M.
      A global meta-analysis of grazing impacts on soil health indicators.
      ) to the quality of animal-derived products (
      • Niero G.
      • Bobbo T.
      • Callegaro S.
      • Visentin G.
      • Pornaro C.
      • Cassandro M.
      • Cozzi G.
      • De Marchi M.
      • Penasa M.
      Summer Pasture in mountainous area affects milk fatty acid profile of dual-purpose cows.
      ), the touristic appeal of the territory (
      • Bele B.
      • Sickel H.
      • Norderhaug A.
      Tourism and terroir products from mountain summer farming landscapes.
      ), consumers' appreciation of the final product (
      • Jackson A.
      • Green M.
      • Millar K.
      • Kaler J.
      Is it just about grazing? UK citizens have diverse preferences for how dairy cows should be managed.
      ), and the maintenance of local traditions, biodiversity, and landscapes (
      • Niero G.
      • Koczura M.
      • De Marchi M.
      • Currò S.
      • Kreuzer M.
      • Turille G.
      • Berard J.
      Are cheese-making properties of dual purpose cattle impaired by highland grazing? A case study using Aosta Red Pied cows.
      ).
      Given the notable importance of this topic, it is worth developing and implementing analytical methods based on inherent food properties that are able to distinguish between dairy foods originating from conventional and pasture-based conditions. For instance, it is well documented that both gas chromatography and mid-infrared spectroscopy techniques can be used to effectively monitor the modification of detailed milk fatty acid composition, where an increased concentration of polyunsaturated fatty acids can be used as a possible biomarker for pasture-based farming systems (
      • Leiber F.
      • Kreuzer M.
      • Nigg D.
      • Wettstein H.R.
      • Scheeder M.R.
      A study on the causes for the elevated n-3 fatty acids in cows' milk of alpine origin.
      ;
      • De Marchi M.
      • Penasa M.
      • Zidi A.
      • Manuelian C.L.
      Invited review: Use of infrared technologies for the assessment of dairy products—Applications and perspectives.
      ). Mid-infrared spectra can be also used as a fingerprinting technique to discriminate between milk produced under intensive and extensive farming conditions (
      • Capuano E.
      • Rademaker J.
      • van den Bijgaart H.
      • van Ruth S.M.
      Verification of fresh grass feeding, pasture grazing and organic farming by FTIR spectroscopy analysis of bovine milk.
      ). Furthermore, the profiling of stable isotopes and isotope ratios has been proposed as a traceability tool able to discriminate between organically and conventionally produced milk (
      • Molkentin J.
      • Giesemann A.
      Follow-up of stable isotope analysis of organic versus conventional milk.
      ) and between milks obtained through different feeding regimens (
      • Camin F.
      • Perini M.
      • Colombari G.
      • Bontempo L.
      • Versini G.
      Influence of dietary composition on the carbon, nitrogen, oxygen and hydrogen stable isotope ratios of milk.
      ).
      In the realm of food science, proton nuclear magnetic resonance (1H NMR) spectroscopy is one of the most potent analytical methods that may be used to analyze liquid or solid materials. Proton nuclear magnetic resonance has enormous potential for analyzing complex matrices and offers opportunity to advance the field of food science due to its nondestructive nature, high accuracy, and reproducibility, which are frequently achieved without using any separation or purification steps. The area of the 1H NMR signal is exactly proportional to the number of nuclei that create the signal in 1H NMR spectroscopy, which is a quantitative method that may be used to analyze a variety of nuclei based on the type of food being analyzed and the information being sought. Moreover, even though conventional quality, safety, and authenticity control in food is based on targeted strategies, high-resolution 1H-NMR, performed under well-defined instrumental specifications, offers several advantages, including producing an extremely reproducible food fingerprint and fully quantitative data by means of a single experiment (
      • Vignoli A.
      • Ghini V.
      • Meoni G.
      • Licari C.
      • Takis P.G.
      • Tenori L.
      • Turano P.
      • Luchinat C.
      High-throughput metabolomics by 1D NMR.
      ). In this scenario, 1H NMR spectroscopy has been effectively used in the dairy sector for different purposes and applications, primarily traceability of milk and cheese produced according to different farming systems (
      • Tenori L.
      • Santucci C.
      • Meoni G.
      • Morrocchi V.
      • Matteucci G.
      • Luchinat C.
      NMR metabolomic fingerprinting distinguishes milk from different farms.
      ;
      • Segato S.
      • Caligiani A.
      • Contiero B.
      • Galaverna G.
      • Bisutti V.
      • Cozzi G.
      1H NMR metabolic profile to discriminate pasture based Alpine Asiago PDO cheeses.
      ), detection of bovine milk adulteration in caprine milk (
      • Rysova L.
      • Legarova V.
      • Pacakova Z.
      • Hanus O.
      • Nemeckova I.
      • Klimesova M.
      • Havlik J.
      Detection of bovine milk adulteration in caprine milk with N-acetyl carbohydrate biomarkers by using 1H nuclear magnetic resonance spectroscopy.
      ), and characterization of metabolites in milk from cows with subclinical and clinical mastitis (
      • Luangwilai M.
      • Duangmal K.
      • Chantaprasarn N.
      • Settachaimongkon S.
      Comparative metabolite profiling of raw milk from subclinical and clinical mastitis cows using 1H-NMR combined with chemometric analysis.
      ). Still, a paucity of information exists regarding the possibility to use 1H NMR spectroscopy as an analytical traceability tool for the distinction between intensive and extensive farming conditions. Similarly, few studies investigating the effect of grazing pasture on milk metabolome exist so far.
      These research questions have been targeted through the trials of the present study, which involved dairy cows farmed on a single alpine herd adopting summer grazing. Specific aims were to (1) assess the ability of 1H NMR spectroscopy to discriminate between milk produced from cows before and after the beginning of the grazing season and (2) investigate how, and to what extent, grazing can affect milk metabolic profile.

      MATERIALS AND METHODS

      Herd

      The trials were performed during routine milking procedures and were not invasive; therefore, animal welfare committee authorization was not required. The commercial dairy farm involved in this study was located in Veneto Region (Pian del Cansiglio, Belluno, Italy) at 1,100 m above sea level. The farm was enrolled in the official milk recording system of the Breeders Association of Veneto Region (Vicenza, Italy). The herd comprised Simmental (SI), Holstein (HO), and HO × SI crossbred (CR) lactating cows (n = 72). Between October and May, animals were housed indoors in a freestall barn and were fed with TMR based on grass silage, alfalfa hay, high-moisture corn, cereal meal, and a protein-mineral-vitamin mix (9.5 kg as fed, 3 kg, 7 kg, 3 kg, and 3.5 kg, respectively). Milking took place twice per day, in the morning (0500 h) and in the afternoon (1700 h). Between late spring and the beginning of autumn, animals had access to the pasture located near the farm. Cows grazed fresh herbage following the Voisin rotational system (
      • Voisin A.
      Grass Productivity.
      ), with animals being moved to a different paddock every day. Cows received 2 kg of high-moisture corn and cereal meals as an energy supplement during each milking event. Further details on chemical composition of the fresh herbage grazed by the animals can be retrieved from
      • Niero G.
      • Bobbo T.
      • Callegaro S.
      • Visentin G.
      • Pornaro C.
      • Penasa M.
      • Cozzi G.
      • De Marchi M.
      • Cassandro M.
      Dairy cows' health during alpine summer grazing as assessed by milk traits, including differential somatic cell count: A case study from Italy.
      .

      Sample Collection and Gross Chemical Composition

      Individual milk samples from the entire herd were repeatedly collected during the afternoon milking from late May to June 2020. Milk sampling started 3 d and 1 d before the beginning of grazing, to characterize milk obtained under indoor farming conditions. Milk samples were also collected 2, 3, 7, 10, and 14 d after the beginning of grazing, to characterize the milk obtained at pasture (Figure 1). After sample collection, 200 µL of Bronopol (2-bromo-2-nitropropan-1,3-diol; ANA.LI.TIK. Austria) preservative were added to 40 mL of milk. Samples were transferred at 4°C to the laboratory of the Breeders Association of Veneto Region (Padova, Italy) and analyzed within 12 h for gross chemical composition (fat, protein, casein, and lactose percentages) and urea content (mg/dL), using a MilkoScan FT6000 (Foss Analytical A/S). A Fossomatic 7 DC (Foss Analytical A/S) was used to determine SCC (cells/µL) and differential SCC (%). Values of SCC were log-transformed to SCS using the formula SCS = 3 + log2(SCC/100) to achieve the normality and homogeneity of variances (
      • Ali A.K.A.
      • Shook G.E.
      An optimum transformation for somatic cell concentration in milk.
      ). A total of 451 milk samples from 72 cows were collected for 1H NMR analysis (291 samples from 49 SI cows; 21 samples from 3 HO cows; 139 samples from 20 CR cows).
      Figure thumbnail gr1
      Figure 1Test-days flowchart of the experimental design.

      Proton Nuclear Magnetic Resonance Spectroscopy

      Each milk sample was dissolved in dichloromethane (CH2Cl2) 1:1 (vol/vol;
      • Tenori L.
      • Santucci C.
      • Meoni G.
      • Morrocchi V.
      • Matteucci G.
      • Luchinat C.
      NMR metabolomic fingerprinting distinguishes milk from different farms.
      ), homogenized by vortexing, and incubated for 10 min at room temperature. The mixture was centrifuged at 5,000 × g at 4°C for 30 min, and 350 µL of the supernatant were added to 350 µL of sodium phosphate buffer [70 mM Na2HPO4; 20% (vol/vol) H2O, 6.1 mM NaN3; 4.6 mM sodium trimethylsilyl (2,2,3,3-H4)-propionate; pH 7.4]. A total of 600 µL of the resulting mixture were transferred into a 5-mm NMR tube (Bruker BioSpin) and kept at −80°C until further analysis at the Magnetic Resonance Center (Sesto Fiorentino, Florence, Italy).
      One-dimensional (1D) 1H NMR spectra of milk aqueous extracts were recorded on a Bruker spectrometer operating at 600.13-MHz proton Larmor frequency and equipped with a 5-mm PATXI 1 H-13C-15N probe including a z-axis gradient coil, automatic tuning-matching, and an automatically refrigerated sample-changer (SampleJet, Bruker BioSpin). A BTO 2000 thermocouple provided thermal stabilization of the samples, ensuring a maximum fluctuation of approximately 0.1 K. For temperature equilibration, samples were maintained inside the NMR probe for 5 min prior to measurement (310 K). Nuclear Overhauser effect spectroscopy (NOESY) pulse sequence (noesygppr1d, Bruker BioSpin) was used to acquire 1D 1H NMR spectra with 64 scans, 98 k data points, a spectral width of 18,028 Hz, an acquisition time of 2.7 s, a relaxation time of 4 s, and a mixing time of 10 ms. The NOESY pulse sequence suppresses solvent signal and yields a spectrum in which both the signals of metabolites and high molecular weight molecules are visible (
      • McKay R.T.
      How the 1D-NOESY suppresses solvent signal in metabonomics NMR spectroscopy: An examination of the pulse sequence components and evolution.
      ). Before applying Fourier transformation, free induction decays were multiplied by an exponential function equivalent to a 0.3-Hz line-broadening factor. TopSpin software (Bruker) was used to automatically adjust for phase and baseline aberrations in transformed spectra. To calibrate spectra, the α-lactose doublet (5.24 δ1H ppm) was used. Each 1D spectra in the range of 0.02 to 10.00 δ1H ppm was segmented into 0.05 δ1H ppm chemical shift buckets for multivariate analysis. Buckets are spectral areas calculated under spectral segments, reported in arbitrary units. Prior to performing the statistical analyses, the portions of NOESY spectra corresponding to the water (4.61–4.77 δ1H ppm) and dichloromethane (5.30–5.33 and 5.42–5.65 δ1H ppm) resonating regions were removed and the buckets were normalized using probabilistic quotient normalization (
      • Dieterle F.
      • Ross A.
      • Schlotterbeck G.
      • Senn H.
      Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics.
      ), resulting in a data matrix of 451 rows (samples) and 228 columns (buckets). A total of 40 signals were characterized in the 1H NMR spectra (Supplemental Table S1, https://figshare.com/articles/figure/Supplemental_Table_S1_pdf/20712427;

      Visentin G., 2022a. Supplementary Table S1. figshare. Figure. https://doi.org/10.6084/m9.figshare.20712427.v3.

      ), resulting in a data matrix of 451 rows (samples) and 40 columns (metabolites). Signal identification was performed using a library of NMR spectra of pure organic compounds (Assure NMR 2.2 software, Bruker BioSpin), public databases storing references (FooDB, https://foodb.ca/; Milk Composition Database, http://www.mcdb.ca/), and literature data (
      • Tenori L.
      • Santucci C.
      • Meoni G.
      • Morrocchi V.
      • Matteucci G.
      • Luchinat C.
      NMR metabolomic fingerprinting distinguishes milk from different farms.
      ;
      • Meoni G.
      • Tenori L.
      • Luchinat C.
      Nuclear magnetic resonance-based metabolomic comparison of breast milk and organic and traditional formula milk brands for infants and toddlers.
      ).

      Data Editing and Statistical Analyses

      Among the 451 acquired records, only samples that satisfied specific inclusion criteria were used for statistical analyses. In particular, (1) DIM and parity were restricted to range from 5 to 500 d and between 1 and 8, respectively; (2) records with milk yield (MY) <2 kg/milking were discarded; and (3) only samples collected from animals that were present in at least 5 of the 7 test-days (days before and after the beginning of grazing) were kept. Additionally, for MY and milk quality traits, samples exceeding 3 standard deviations from the respective mean were treated as missing values. Following these criteria, 421 milk samples from 72 animals (256 samples from 49 SI cows; 21 samples from 3 HO cows; 135 samples from 20 CR cows) remained for statistical analysis. A total of 118 individual milks were collected 3 d and 1 d before the beginning of grazing, and 303 samples were obtained 2, 3, 7, 10, and 14 d after the beginning of grazing. Descriptive statistics of production-related traits, MY, and quality traits are presented in Supplemental Table S2 (https://figshare.com/articles/figure/Untitled_Item/20712499;

      Visentin G., 2022b. Supplementary Table S2. figshare. Figure. https://doi.org/10.6084/m9.figshare.20712499.v2.

      ).
      Data analyses were performed using R software (version 3.5.3; R Core Team, 2020). Principal component analysis was used as the first exploratory unsupervised analysis on bucketed spectra, to evaluate the presence of a net discrimination between the days of sampling and potential presence of outliers. Random forest (RF) discriminant analysis (“Random Forest” R package) was applied to bucketed spectra. Here, each tree of the forest tree was used to predict the day of collection for each sample. For this study, RF was used with double cross-validation, and samples from various collection times (days) of the same animal were deleted from the training at each test cycle of the double cross-validation to obtain an unbiased predictive accuracy. For each computation, a forest of 5,000 trees was employed.
      The univariate analysis was carried out on the 40 identified metabolites integrated from 1D 1H-NMR spectra. Data were processed according to the following linear mixed model, using PROC MIXED of SAS software v. 9.4 (SAS Institute Inc.):
      yijklm = periodi + breedj + stagek + parityl + cowm(breedj) + eijklm,
      []


      where yijklm is a milk metabolite; periodi is the fixed effect of the ith period of sampling [2 classes: d −3 to d 3 (representing TMR and early-stage grazing diets) and d 7 to d 14 (representing prolonged grazing diet)]; breedj is the fixed effect of the jth cow breed (3 classes: SI, HO, and CR); stagek is the fixed effect of the kth stage of lactation (8 classes: 5–45, 46–90, 91–135, 136–180, 181–225, 226–270, 271–315, and >315 d); parityl is the fixed effect of the lth parity (6 classes: 1, 2, 3, 4, 5, and >5); cowm is the random effect of the mth cow nested within the jth breed; and eijklm is the random residual term. Variation of milk metabolites in the 2 sampling periods were reported as log2(fold change), and differences were tested with the Wilcoxon test with Bonferroni correction for P-value determination. Furthermore, for each metabolite, the Cliff's delta effect size was calculated by means of the R package “effsize” ().

      RESULTS AND DISCUSSION

      Classification of Samples Based on 1H NMR Spectra

      The capacity of the RF model to discriminate between the different days of sampling is reported in Table 1. The overall accuracy of the model was 58.7%, and the proportion of samples correctly classified by the model (i.e., the sensitivity) ranged from 37.1% (d 3) to 80.3% (d 14). In general, the proportion of samples wrongly attributed was greater between adjacent days of sampling. For example, 35.6% of samples collected in d −3 (i.e., 3 d before access to pasture) were attributed to d −1 (i.e., the day before access to pasture), whereas almost none of such samples were attributed to samples collected from d 7 onward (Table 1). It is worth noting that samples collected from d −3 to d 3 tended to be confounded among each other, as well as samples collected from d 7 to 14. This suggests that milk NMR spectra, and therefore the metabolic profile of dairy cows, is to some extent influenced by the diet of the dairy cows, which become evident only after some days of continued access to pasture and fresh herbage (i.e., from d 7 onward). This is also in agreement with previous studies, which reported that several days of metabolic and behavioral adaptation are needed when animals move from TMR to pasture (
      • Schären M.
      • Jostmeier S.
      • Ruesink S.
      • Hüther L.
      • Frahm J.
      • Bulang M.
      • Meyer U.
      • Rehage J.
      • Isselstein J.
      • Breves G.
      • Dänicke S.
      The effects of a ration change from a total mixed ration to pasture on health and production of dairy cows.
      ). Accordingly, studies involving Latin square designs commonly include a transition period before starting new sampling with different nutritional regimen, to avoid the influence of the previous treatment.
      Table 1Confusion matrix (%) of random forest model
      The diagonal of the confusion matrix reports the sensitivity (%) for the classification of each animal. Overall predictive accuracy = 58.7%.
      built to classify different days of sampling
      Days −3 and −1 refer to 3 and 1 d before the beginning of grazing; d 2, 3, 7, 10, and 14 refer to 2, 3, 7, 10 and 14 d after the beginning of grazing.
      Actual day of samplingPredicted day of sampling
      −3−12371014
      −347.535.65.110.21.70.00.0
      −127.154.28.56.81.71.70.0
      24.93.360.716.41.613.10.0
      34.811.322.637.13.221.00.0
      75.20.00.01.756.910.325.9
      104.91.69.84.93.373.81.6
      140.00.01.60.018.00.080.3
      1 The diagonal of the confusion matrix reports the sensitivity (%) for the classification of each animal. Overall predictive accuracy = 58.7%.
      2 Days −3 and −1 refer to 3 and 1 d before the beginning of grazing; d 2, 3, 7, 10, and 14 refer to 2, 3, 7, 10 and 14 d after the beginning of grazing.
      We therefore applied the RF model to only 2 periods of days of sampling, created by grouping together samples collected in d −3, −1, 2, and 3 (period 1) and samples collected in d 7, 10, and 14 (period 2; Table 2). In this case, the overall accuracy of the model increased to 93.1%, with 12.9% of samples in period 2 attributed to period 1, and only 2.4% of samples in period 1 attributed to period 2 (Table 2). It is clear, in particular from Table 2, that exploitable information exists in milk NMR spectra to discriminate between milk samples collected from cows before moving to pasture and cows at pasture. The effect of feeding regimen on the metabolic profile of dairy cows has been reported in previous studies (
      • Tenori L.
      • Santucci C.
      • Meoni G.
      • Morrocchi V.
      • Matteucci G.
      • Luchinat C.
      NMR metabolomic fingerprinting distinguishes milk from different farms.
      ;
      • Billa P.A.
      • Faulconnier Y.
      • Larsen T.
      • Leroux C.
      • Pires J.A.A.
      Milk metabolites as noninvasive indicators of nutritional status of mid-lactating Holstein and Montbéliarde cows.
      ). For example, a restriction in concentrate administration is associated with increased milk glucose-6-phosphate and isocitrate, and with decreased milk BHB, glucose, glutamate, uric acid, and free amino acids concentrations (
      • Billa P.A.
      • Faulconnier Y.
      • Larsen T.
      • Leroux C.
      • Pires J.A.A.
      Milk metabolites as noninvasive indicators of nutritional status of mid-lactating Holstein and Montbéliarde cows.
      ).
      • Tenori L.
      • Santucci C.
      • Meoni G.
      • Morrocchi V.
      • Matteucci G.
      • Luchinat C.
      NMR metabolomic fingerprinting distinguishes milk from different farms.
      applied a canonical analysis to partial least squares latent variables to discriminate milk samples based on their herd of origin. This is extremely useful, for example, for food traceability purposes or in a production context in which the administration of some feedstuffs is prohibited by specific voluntary regulations, as in the case of the Parmigiano Reggiano cheese area. In this production area, located in some provinces of the Emilia-Romagna and Lombardy regions (Northern Italy), silage is not permitted as feed for lactating animals, to minimize the risk of anaerobic bacteria presence in milk, which could potentially lead to undesired fermentation during cheese ripening (
      • Buonaiuto G.
      • Palmonari A.
      • Ghiaccio F.
      • Visentin G.
      • Cavallini D.
      • Campidonico L.
      • Formigoni A.
      • Mammi L.M.E.
      Effect of complete replacement of corn flour with sorghum flour in dairy cows fed Parmigiano Reggiano dry hay-based ration.
      ). Exploitable information to discriminate between diets offered to dairy cattle is also available in spectra generated from milk mid-infrared spectroscopy analyses.
      • Frizzarin M.
      • O'Callaghan T.F.
      • Murphy T.B.
      • Hennessy D.
      • Casa A.
      Application of machine-learning methods to milk mid-infrared spectra for discrimination of cow milk from pasture or total mixed ration diets.
      applied different machine-learning algorithms to more than 4,000 milk spectra collected from grazing or TMR-fed dairy cows and demonstrated that linear discriminant analysis and partial least squares discriminant analysis were the most effective algorithms, with an overall accuracy of 0.968 in both cases.
      Table 2Confusion matrix (%) of random forest model
      The diagonal of the confusion matrix reports the sensitivity (%) for the classification of each animal. Overall predictive accuracy = 93.1%.
      built to classify different periods of sampling
      Period 1 refers to 3 and 1 d before the beginning of grazing and 2 and 3 d after the beginning of grazing; period 2 refers to 7, 10, and 14 d after the beginning of grazing.
      Actual period of samplingPredicted period of sampling
      12
      197.62.4
      212.987.1
      1 The diagonal of the confusion matrix reports the sensitivity (%) for the classification of each animal. Overall predictive accuracy = 93.1%.
      2 Period 1 refers to 3 and 1 d before the beginning of grazing and 2 and 3 d after the beginning of grazing; period 2 refers to 7, 10, and 14 d after the beginning of grazing.

      Phenotypic Correlations

      Pearson correlation coefficients (r) between milk metabolites, milk chemical composition, and SCS are presented in Figure 2. Only 2 out of the 40 detected metabolites (namely citrate and galactose-1-phosphate) were positively associated with MY. The remaining metabolites were negatively correlated with MY, meaning that higher MY was associated with a decreased relative abundance of these compounds, probably due to a dilution effect.
      Figure thumbnail gr2
      Figure 2Correlation heatmap of milk metabolites with milk yield, chemical composition, SCS, and differential somatic cell count (DSCC). Pearson correlations are shown as different degree of color intensity (red, positive correlations; blue, negative correlation). Asterisks refer to statistically significant correlations (P < 0.001).
      N-Acetyl carbohydrates were found to positively correlate with both protein (r = 0.61; P < 0.001) and CN content (r = 0.62; P < 0.001). The specific role of N-acetyl carbohydrates in milk is still largely unknown (
      • Sischo W.M.
      • Short D.M.
      • Geissler M.
      • Bunyatratchata A.
      • Barile D.
      Comparative composition, diversity, and abundance of oligosaccharides in early lactation milk from commercial dairy and beef cows.
      ;
      • Giorgio D.
      • Di Trana A.
      • Claps S.
      Oligosaccharides, polyamines and sphingolipids in ruminant milk.
      ;
      • Rysova L.
      • Legarova V.
      • Pacakova Z.
      • Hanus O.
      • Nemeckova I.
      • Klimesova M.
      • Havlik J.
      Detection of bovine milk adulteration in caprine milk with N-acetyl carbohydrate biomarkers by using 1H nuclear magnetic resonance spectroscopy.
      ), even if the physiological and biochemical patterns regarding these compounds are gradually being elucidated. Consistent with the negative association between N-acetyl carbohydrates and MY observed in this study,
      • Tomassini A.
      • Curone G.
      • Solè M.
      • Capuani G.
      • Sciubba F.
      • Conta G.
      • Miccheli A.
      • Vigo D.
      NMR-based metabolomics to evaluate the milk composition from Friesian and autochthonous cows of Northern Italy at different lactation times.
      and
      • Zhu D.
      • Kebede B.
      • Chen G.
      • McComb K.
      • Frew R.
      Changes in milk metabolome during the lactation of dairy cows based on 1H NMR and UHPLC–QToF/MS.
      reported an increasing trend of N-acetyl carbohydrates across the lactation period, probably due to qualitative and quantitative changes in the synthesis of oligosaccharides in different lactation stages. It has been reported that N-acetyl carbohydrates can originate from catabolism of glycoproteins. In particular, during processes involving proteolysis, glycans become detached and are released from proteins, thereby becoming detectable in 1H NMR spectroscopy (
      • Rysova L.
      • Legarova V.
      • Pacakova Z.
      • Hanus O.
      • Nemeckova I.
      • Klimesova M.
      • Havlik J.
      Detection of bovine milk adulteration in caprine milk with N-acetyl carbohydrate biomarkers by using 1H nuclear magnetic resonance spectroscopy.
      ). This argument can explain the positive association between protein content and relative abundance of N-acetyl carbohydrates observed in the present study.
      Citrate exhibited weak negative correlations with both protein and CN content (r = −0.22; P < 0.001). Milk citrate acts as a calcium chelating agent, creating soluble calcium-citrate complexes that are in competition with colloidal calcium-phosphate complexes. Therefore, increased concentrations of soluble citrate are likely to decrease the level of colloidal calcium phosphate, weakening the stability of casein micelles and, ultimately, deteriorating milk coagulation properties (
      • Sundekilde U.K.
      • Frederiksen P.D.
      • Clausen M.R.
      • Larsen L.B.
      • Bertram H.C.
      Relationship between the metabolite profile and technological properties of bovine milk from two dairy breeds elucidated by NMR-based metabolomics.
      ).
      Lecithin was positively correlated with fat (r = 0.32; P < 0.001), protein (r = 0.52; P < 0.001), and CN content (r = 0.51; P < 0.001). The positive association between lecithin and milk fat was expected, due to the fat-soluble properties of this compound, which is found in liposome structures (
      • Holmes H.C.
      • Snodgrass G.J.A.I.
      • Iles R.A.
      Changes in the choline content of human breast milk in the first 3 weeks after birth.
      ). As in the present study,
      • Holmes H.C.
      • Snodgrass G.J.A.I.
      • Iles R.A.
      Changes in the choline content of human breast milk in the first 3 weeks after birth.
      reported a direct association between milk lecithin and milk protein content, even if the physiological reasons at the basis of this phenomenon need further detailed study. Moreover, milk lecithin has been discussed by
      • Bobbo T.
      • Meoni G.
      • Niero G.
      • Tenori L.
      • Luchinat C.
      • Cassandro M.
      • Penasa M.
      Nuclear magnetic resonance spectroscopy to investigate the association between milk metabolites and udder quarter health status in dairy cows.
      , who observed that milk samples with greater SCC had lower levels of lecithin.
      As expected, a strong positive correlation (r = 0.76; P < 0.001) was observed between the relative abundance of lactose determined through H1 NMR and lactose content predicted through mid-infrared spectroscopy, documenting a good level of agreement between the 2 analytical techniques.
      Similar to acetate and lecithin, galactose exhibited positive correlations with both protein (r = 0.50; P < 0.001) and CN contents (r = 0.53; P < 0.001).
      • Li Q.
      • Yu Z.
      • Zhu D.
      • Meng X.
      • Pang X.
      • Liu Y.
      • Frew R.
      • Chen H.
      • Chen G.
      The application of NMR-based milk metabolite analysis in milk authenticity identification.
      suggested that different polymorphisms of the UDP-galactose-4-epimerase bovine gene, which codes for an enzyme regulating galactose metabolism, can be used as a molecular marker implicated in milk protein concentration in dairy cattle. This may corroborate the association between milk galactose and milk protein contents observed in the present study.
      Milk samples with greater SCC were characterized by lower levels of sarcosine and dimethylamine (r = −0.22; P < 0.001), lactose (r = −0.27; P < 0.001), and mannose (r = −0.21; P < 0.001). By contrast, valine, lactate, and O-acetilcarnitine had weak positive correlations with SCS (r = 0.27, 0.24, and 0.23, respectively; P < 0.01). The decreased content of lactose in milk with high SCC is well documented in literature and can be ascribed to (1) an increased transfer of lactose from milk to blood, to keep osmotic pressure constant, and (2) an impaired biosynthesis of lactose at the mammary gland level (
      • Costa A.
      • Lopez-Villalobos N.
      • Sneddon N.W.
      • Shalloo L.
      • Franzoi M.
      • De Marchi M.
      • Penasa M.
      Invited review: Milk lactose—Current status and future challenges in dairy cattle.
      ). Furthermore, in accordance with the results of the present study,
      • Luangwilai M.
      • Duangmal K.
      • Chantaprasarn N.
      • Settachaimongkon S.
      Comparative metabolite profiling of raw milk from subclinical and clinical mastitis cows using 1H-NMR combined with chemometric analysis.
      indicated that increased levels of valine and lactate (together with acetate and phenylalanine) are associated with greater milk SCC and mastitis events.

      Effect of Grazing on Milk Metabolites

      Results from the ANOVA of milk metabolites are summarized in Table 3. The model adjusted for the fixed effects of breed, parity, DIM (results not shown), and period of sampling. Out of 40 detected metabolites, the period of sampling significantly affected 29 compounds (P < 0.05). Among the latter, and in terms of effect size, 8 metabolites showed negligible variation, 12 small variation, 3 medium variation, and 6 large variation.
      Table 3F-values, significance of period of sampling, and effect size for the identified metabolites
      MetaboliteF-valueP-valueEffect size
      Isoleucine0.01NSNegligible
      Valine13.88
      P < 0.001.
      Small
      Lactate14.70
      P < 0.001.
      Small
      Alanine0.32NSNegligible
      Butyrate6.08
      P < 0.05
      Negligible
      Acetate38.85
      P < 0.001.
      Large
      N-acetyl carbohydrates41.08
      P < 0.001.
      Small
      Acetone32.34
      P < 0.001.
      Negligible
      3-Hydroxybutyrate48.40
      P < 0.001.
      Medium
      Glutamate56.06
      P < 0.001.
      Small
      Succinate4.40
      P < 0.05
      Negligible
      2-Oxoglutarate11.57
      P < 0.001.
      Negligible
      Carnitine4.84
      P < 0.05
      Small
      O-Acetyl carnitine1.47NSNegligible
      Citrate0.06NSSmall
      Sarcosine+dimethylamine127.24
      P < 0.001.
      Large
      N,N-Dimethylglycine26.50
      P < 0.001.
      Medium
      Creatine+phosphocreatine+creatinine25.22
      P < 0.001.
      Small
      Lecithin7.68
      P < 0.01
      Negligible
      Dimethylsulfone332.76
      P < 0.001.
      Large
      Choline1.09NSNegligible
      Glycerophosphocholine0.32NSNegligible
      Methanol250.32
      P < 0.001.
      Large
      Glucose87.74
      P < 0.001.
      Medium
      Unknown 16.14
      P < 0.05
      Negligible
      Unknown 21.36NSNegligible
      Lactose total5.23
      P < 0.05
      Negligible
      Mannose2.42NSNegligible
      Galactose5.96
      P < 0.05
      Negligible
      Galactose 1-phosphate48.30
      P < 0.001.
      Small
      Cis-aconitate15.18
      P < 0.001.
      Small
      Cytidine2.57NSNegligible
      Unknown aldehyde 19.78
      P < 0.01
      Small
      Unknown aldehyde 213.46
      P < 0.001.
      Small
      Orotate65.53
      P < 0.001.
      Small
      Fumarate118.50
      P < 0.001.
      Large
      Unknown phenyl 140.15
      P < 0.001.
      Small
      Hippurate449.29
      P < 0.001.
      Large
      Riboflavine0.28NSNegligible
      Formate1.73NSNegligible
      * P < 0.05
      ** P < 0.01
      *** P < 0.001.
      Figure 3 shows the log2(fold change) of the area under the peaks of the identified metabolites. Out of the 40 detected compounds, 14 metabolites were significantly lower in stable grazing conditions (i.e., 1 wk after the beginning of the grazing period), including fumarate, glucose, galactose-1-phosphate, orotate, glutamate, cis-aconitate, valine, phosphocreatine+creatine, 2-oxoglutarate, N-acethylcarbohydrates, 2 unknown species of aldehyde, and 1 unknown metabolite (P < 0.05). In contrast, grazing resulted in a significant increase of 15 milk metabolites, comprising methanol, hippurate, acetate, 3-hydroxybuthirate, sarcosine+dimethylamine, N-dimethylglycine, dimethylsulfone, butyrate, lactate, carnitine, galactose, acetone, lecithin, succinate, and 1 unknown species of phenyl (P < 0.05).
      Figure thumbnail gr3
      Figure 3Logarithmic fold change [Log2(FC)] of the area under the peaks of the identified metabolites. Negative Log2(FC) bars indicate lower metabolite levels in samples of period 2 than 1, and positive Log2(FC) bars indicate higher metabolite levels in samples of period 2 than 1. All metabolites were significantly affected by the period of sampling. *P < 0.05; **P < 0.01; ***P < 0.001.
      In accordance with results of the current study, previous authors have observed that milk from pasture-based systems has significantly greater amount of hippuric acid, which has been associated with the presence of caffeoylquinic compounds in fresh forages (
      • Besle J.M.
      • Viala D.
      • Martin B.
      • Pradel P.
      • Meunier B.
      • Berdagué J.L.
      • Fraisse D.
      • Lamaison J.L.
      • Coulon J.B.
      Ultraviolet-absorbing compounds in milk are related to forage polyphenols.
      ;
      • Carpio A.
      • Rodríguez-Estévez V.
      • Sánchez-Rodríguez M.
      • Arce L.
      • Valcárcel M.
      Differentiation of organic goat's milk based on its hippuric acid content as determined by capillary electrophoresis.
      ). This variation was confirmed for both cow (
      • Besle J.M.
      • Viala D.
      • Martin B.
      • Pradel P.
      • Meunier B.
      • Berdagué J.L.
      • Fraisse D.
      • Lamaison J.L.
      • Coulon J.B.
      Ultraviolet-absorbing compounds in milk are related to forage polyphenols.
      ;
      • O'Callaghan T.F.
      • Vázquez-Fresno R.
      • Serra-Cayuela A.
      • Dong E.
      • Mandal R.
      • Hennessy D.
      • McAuliffe S.
      • Dillon P.
      • Wishart D.S.
      • Stanton C.
      • Ross R.P.
      Pasture feeding changes the bovine rumen and milk metabolome.
      ) and goat milk (
      • Carpio A.
      • Rodríguez-Estévez V.
      • Sánchez-Rodríguez M.
      • Arce L.
      • Valcárcel M.
      Differentiation of organic goat's milk based on its hippuric acid content as determined by capillary electrophoresis.
      ). Therefore, based on findings of the present study and in accordance with previous literature, it seems appropriate to propose hippurate as a robust marker for milk produced under pasture conditions. Increased levels of 3-hydroxybutyrate in milk obtained under pasture conditions may be due to greater fat mobilization in grazing animals as a result of lower energy intake and higher motility (
      • Benedet A.
      • Manuelian C.
      • Zidi A.
      • Penasa M.
      • De Marchi M.
      Invited review: β-hydroxybutyrate concentration in blood and milk and its associations with cow performance.
      ).
      • Ashokan M.
      • Ramesha K.P.
      • Hallur S.
      • Karthikkeyan G.
      • Rana E.
      • Azharuddin N.
      • Reshma Raj S.
      • Jeyakumar S.
      • Kumaresan A.
      • Kataktalware M.A.
      • Das D.N.
      • Keshava Prasa T.S.
      Differences in milk metabolites in Malnad Gidda (Bos indicus) cows reared under pasture-based feeding system.
      reported that grazing significantly affected 35 milk metabolites. Among them, tyrosyl-threonine, histidinyl-cysteine, 1-methyladenine, cysteine, and selenocysteine showed a sharp increase in milk of grazing cows.
      • Tenori L.
      • Santucci C.
      • Meoni G.
      • Morrocchi V.
      • Matteucci G.
      • Luchinat C.
      NMR metabolomic fingerprinting distinguishes milk from different farms.
      reported significant variations on bulk milk metabolites depending on different feeding regimens. In particular, the inclusion of silages in animal feed rations caused lower levels of choline, methionine, and hippurate, and greater levels of creatinine, lactate, and an unknown compound. Still, a direct comparison with the results of the present study is not always feasible, due to differences in test hypothesis, experimental design (i.e., bulk milk samples and individual milk samples), and analytical conditions (i.e., protocols for sample preparation and metabolite extraction).
      Evidence from the present research indicates that 1H NMR can be exploited to guarantee food origin, but also that some milk metabolites can potentially be used as markers to differentiate among diets. Although costly and therefore challenging to be implemented on a routine basis, this laboratory technique could be proposed as a food authentication technique, in particular for dairy foods sold at a greater marker price because of processing from grazing cows' milk. This is the case, for example, for Asiago d'Allevo Protected Designation of Origin cheese (
      • Segato S.
      • Caligiani A.
      • Contiero B.
      • Galaverna G.
      • Bisutti V.
      • Cozzi G.
      1H NMR metabolic profile to discriminate pasture based Alpine Asiago PDO cheeses.
      ), which, in some cases, is produced only from grazing animals and marketed with a premium market value. Indeed, it has been demonstrated that consumers were willing to pay more for dairy products with certified practices of improved animal welfare, including pasture access (
      • Olynk N.J.
      • Ortega D.L.
      Consumer preferences for verified dairy cattle management practices in processed dairy products.
      ). This would encourage the development of a geographical traceability system for authentication of milk origin, whose cost could be, at least partially, covered by the additional market value of dairy foods from pasture-based cows' milk. Such a strategy would increase the breadth, depth, and precision of a geographical traceability system, all of which are desirable characteristics in terms of establishing products' origins and histories (
      • Dalvit C.
      • De Marchi M.
      • Cassandro M.
      Genetic traceability of livestock products: A review.
      ), and consumers would benefit in terms of increased confidence when purchasing and paying a higher price for a certified labeled food.

      CONCLUSIONS

      Results of the present study highlighted that the RF models applied to 1H NMR spectra were able to distinguish between samples obtained under different farming conditions with an overall accuracy from 58.7 to 93.1%. Moreover, our study suggests that grazing conditions are associated with specific alterations in the metabolic pattern of milk. Indeed, 29 out of the 40 detected metabolites (e.g., fumarate, methanol, hippurate, and 3-hydroxybutyrate) had significantly different levels of expression depending on the sampling period. These findings represent the first step toward the development of more specific trials to screen for milk metabolites, which have been shown to vary significantly according to grazing, in the view of future traceability applications. Particularly in this perspective, results of the present study may contribute to the development of rapid cow-side tests to discriminate milk from indoor and pasture-grazed animals.

      ACKNOWLEDGMENTS

      This research was funded by the Veneto Region (DGR n. 376/2018, Misura 16.1.1 and 16.1.2, FITOCHE project, Veneto Region, Italy). The Lissandri farm (Pian del Cansiglio, Belluno, Italy) and Mirko Breda are gratefully acknowledged for the opportunity to collect samples and data for the realization of the present study. The authors have not stated any conflicts of interest.

      REFERENCES

        • Ali A.K.A.
        • Shook G.E.
        An optimum transformation for somatic cell concentration in milk.
        J. Dairy Sci. 1980; 63: 487-490
        • Ashokan M.
        • Ramesha K.P.
        • Hallur S.
        • Karthikkeyan G.
        • Rana E.
        • Azharuddin N.
        • Reshma Raj S.
        • Jeyakumar S.
        • Kumaresan A.
        • Kataktalware M.A.
        • Das D.N.
        • Keshava Prasa T.S.
        Differences in milk metabolites in Malnad Gidda (Bos indicus) cows reared under pasture-based feeding system.
        Sci. Rep. 2021; 11 (33531582)2831
        • Bele B.
        • Sickel H.
        • Norderhaug A.
        Tourism and terroir products from mountain summer farming landscapes.
        J. Gastron. Touris. 2017; 2: 232-244
        • Benedet A.
        • Manuelian C.
        • Zidi A.
        • Penasa M.
        • De Marchi M.
        Invited review: β-hydroxybutyrate concentration in blood and milk and its associations with cow performance.
        Animal. 2019; 13 (30854998): 1676-1689
        • Besle J.M.
        • Viala D.
        • Martin B.
        • Pradel P.
        • Meunier B.
        • Berdagué J.L.
        • Fraisse D.
        • Lamaison J.L.
        • Coulon J.B.
        Ultraviolet-absorbing compounds in milk are related to forage polyphenols.
        J. Dairy Sci. 2010; 93 (20630201): 2846-2856
        • Billa P.A.
        • Faulconnier Y.
        • Larsen T.
        • Leroux C.
        • Pires J.A.A.
        Milk metabolites as noninvasive indicators of nutritional status of mid-lactating Holstein and Montbéliarde cows.
        J. Dairy Sci. 2020; 103 (32059860): 3133-3146
        • Bobbo T.
        • Meoni G.
        • Niero G.
        • Tenori L.
        • Luchinat C.
        • Cassandro M.
        • Penasa M.
        Nuclear magnetic resonance spectroscopy to investigate the association between milk metabolites and udder quarter health status in dairy cows.
        J. Dairy Sci. 2022; 105 (34656344): 535-548
        • Buonaiuto G.
        • Palmonari A.
        • Ghiaccio F.
        • Visentin G.
        • Cavallini D.
        • Campidonico L.
        • Formigoni A.
        • Mammi L.M.E.
        Effect of complete replacement of corn flour with sorghum flour in dairy cows fed Parmigiano Reggiano dry hay-based ration.
        Ital. J. Anim. Sci. 2021; 20: 826-833
        • Burow E.
        • Rousing T.
        • Thomsen P.T.
        • Otten N.D.
        • Sørensen J.T.
        Effect of grazing on the cow welfare of dairy herds evaluated by a multidimensional welfare index.
        Animal. 2013; 7 (23244468): 834-842
        • Byrnes R.C.
        • Eastburn D.J.
        • Tate K.W.
        • Roche L.M.
        A global meta-analysis of grazing impacts on soil health indicators.
        J. Environ. Qual. 2018; 47 (30025044): 758-765
        • Camin F.
        • Perini M.
        • Colombari G.
        • Bontempo L.
        • Versini G.
        Influence of dietary composition on the carbon, nitrogen, oxygen and hydrogen stable isotope ratios of milk.
        Rapid Commun. Mass Spectrom. 2008; 22 (18446770): 1690-1696
        • Capuano E.
        • Rademaker J.
        • van den Bijgaart H.
        • van Ruth S.M.
        Verification of fresh grass feeding, pasture grazing and organic farming by FTIR spectroscopy analysis of bovine milk.
        Food Res. Int. 2014; 60: 59-65
        • Carpio A.
        • Rodríguez-Estévez V.
        • Sánchez-Rodríguez M.
        • Arce L.
        • Valcárcel M.
        Differentiation of organic goat's milk based on its hippuric acid content as determined by capillary electrophoresis.
        Electrophoresis. 2010; 31 (20593395): 2211-2217
        • Costa A.
        • Lopez-Villalobos N.
        • Sneddon N.W.
        • Shalloo L.
        • Franzoi M.
        • De Marchi M.
        • Penasa M.
        Invited review: Milk lactose—Current status and future challenges in dairy cattle.
        J. Dairy Sci. 2019; 102 (31079905): 5883-5898
        • Dalvit C.
        • De Marchi M.
        • Cassandro M.
        Genetic traceability of livestock products: A review.
        Meat Sci. 2007; 77 (22061927): 437-449
        • De Marchi M.
        • Penasa M.
        • Zidi A.
        • Manuelian C.L.
        Invited review: Use of infrared technologies for the assessment of dairy products—Applications and perspectives.
        J. Dairy Sci. 2018; 101 (30268617): 10589-10604
        • Dieterle F.
        • Ross A.
        • Schlotterbeck G.
        • Senn H.
        Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics.
        Anal. Chem. 2006; 78 (16808434): 4281-4290
        • Frizzarin M.
        • O'Callaghan T.F.
        • Murphy T.B.
        • Hennessy D.
        • Casa A.
        Application of machine-learning methods to milk mid-infrared spectra for discrimination of cow milk from pasture or total mixed ration diets.
        J. Dairy Sci. 2021; 104 (34593222): 12394-12402
        • Giorgio D.
        • Di Trana A.
        • Claps S.
        Oligosaccharides, polyamines and sphingolipids in ruminant milk.
        Small Rumin. Res. 2018; 160: 23-30
        • Holmes H.C.
        • Snodgrass G.J.A.I.
        • Iles R.A.
        Changes in the choline content of human breast milk in the first 3 weeks after birth.
        Eur. J. Pediatr. 2000; 159 (10664235): 198-204
        • Jackson A.
        • Green M.
        • Millar K.
        • Kaler J.
        Is it just about grazing? UK citizens have diverse preferences for how dairy cows should be managed.
        J. Dairy Sci. 2020; 103 (32057434): 3250-3263
        • Leiber F.
        • Kreuzer M.
        • Nigg D.
        • Wettstein H.R.
        • Scheeder M.R.
        A study on the causes for the elevated n-3 fatty acids in cows' milk of alpine origin.
        Lipids. 2005; 40 (15884768): 191-202
        • Li Q.
        • Yu Z.
        • Zhu D.
        • Meng X.
        • Pang X.
        • Liu Y.
        • Frew R.
        • Chen H.
        • Chen G.
        The application of NMR-based milk metabolite analysis in milk authenticity identification.
        J. Sci. Food Agric. 2017; 97 (27790701): 2875-2882
        • Luangwilai M.
        • Duangmal K.
        • Chantaprasarn N.
        • Settachaimongkon S.
        Comparative metabolite profiling of raw milk from subclinical and clinical mastitis cows using 1H-NMR combined with chemometric analysis.
        Int. J. Food Sci. Technol. 2021; 56: 493-503
        • McKay R.T.
        How the 1D-NOESY suppresses solvent signal in metabonomics NMR spectroscopy: An examination of the pulse sequence components and evolution.
        Concepts Magn. Reson. Part A Bridg. Educ. Res. 2011; 38A: 197-220
        • Meoni G.
        • Tenori L.
        • Luchinat C.
        Nuclear magnetic resonance-based metabolomic comparison of breast milk and organic and traditional formula milk brands for infants and toddlers.
        OMICS. 2020; 24 (32522087): 424-436
        • Molkentin J.
        • Giesemann A.
        Follow-up of stable isotope analysis of organic versus conventional milk.
        Anal. Bioanal. Chem. 2010; 398 (20658229): 1493-1500
        • Niero G.
        • Bobbo T.
        • Callegaro S.
        • Visentin G.
        • Pornaro C.
        • Cassandro M.
        • Cozzi G.
        • De Marchi M.
        • Penasa M.
        Summer Pasture in mountainous area affects milk fatty acid profile of dual-purpose cows.
        Agriculture. 2021; 11: 928
        • Niero G.
        • Bobbo T.
        • Callegaro S.
        • Visentin G.
        • Pornaro C.
        • Penasa M.
        • Cozzi G.
        • De Marchi M.
        • Cassandro M.
        Dairy cows' health during alpine summer grazing as assessed by milk traits, including differential somatic cell count: A case study from Italy.
        Animals (Basel). 2021; 11 (33915759): 981
        • Niero G.
        • Koczura M.
        • De Marchi M.
        • Currò S.
        • Kreuzer M.
        • Turille G.
        • Berard J.
        Are cheese-making properties of dual purpose cattle impaired by highland grazing? A case study using Aosta Red Pied cows.
        Ital. J. Anim. Sci. 2018; 17: 827-834
        • O'Callaghan T.F.
        • Vázquez-Fresno R.
        • Serra-Cayuela A.
        • Dong E.
        • Mandal R.
        • Hennessy D.
        • McAuliffe S.
        • Dillon P.
        • Wishart D.S.
        • Stanton C.
        • Ross R.P.
        Pasture feeding changes the bovine rumen and milk metabolome.
        Metabolites. 2018; 8 (29642378): 27
        • Olynk N.J.
        • Ortega D.L.
        Consumer preferences for verified dairy cattle management practices in processed dairy products.
        Food Control. 2013; 30: 298-305
        • Rysova L.
        • Legarova V.
        • Pacakova Z.
        • Hanus O.
        • Nemeckova I.
        • Klimesova M.
        • Havlik J.
        Detection of bovine milk adulteration in caprine milk with N-acetyl carbohydrate biomarkers by using 1H nuclear magnetic resonance spectroscopy.
        J. Dairy Sci. 2021; 104 (34099301): 9583-9595
        • Schären M.
        • Jostmeier S.
        • Ruesink S.
        • Hüther L.
        • Frahm J.
        • Bulang M.
        • Meyer U.
        • Rehage J.
        • Isselstein J.
        • Breves G.
        • Dänicke S.
        The effects of a ration change from a total mixed ration to pasture on health and production of dairy cows.
        J. Dairy Sci. 2016; 99 (26627855): 1183-1200
        • Segato S.
        • Caligiani A.
        • Contiero B.
        • Galaverna G.
        • Bisutti V.
        • Cozzi G.
        1H NMR metabolic profile to discriminate pasture based Alpine Asiago PDO cheeses.
        Animals (Basel). 2019; 9 (31557876): 722
        • Sischo W.M.
        • Short D.M.
        • Geissler M.
        • Bunyatratchata A.
        • Barile D.
        Comparative composition, diversity, and abundance of oligosaccharides in early lactation milk from commercial dairy and beef cows.
        J. Dairy Sci. 2017; 100 (28318588): 3883-3892
        • Sundekilde U.K.
        • Frederiksen P.D.
        • Clausen M.R.
        • Larsen L.B.
        • Bertram H.C.
        Relationship between the metabolite profile and technological properties of bovine milk from two dairy breeds elucidated by NMR-based metabolomics.
        J. Agric. Food Chem. 2011; 59 (21650175): 7360-7367
        • Tenori L.
        • Santucci C.
        • Meoni G.
        • Morrocchi V.
        • Matteucci G.
        • Luchinat C.
        NMR metabolomic fingerprinting distinguishes milk from different farms.
        Food Res. Int. 2018; 113 (30195505): 131-139
        • Tomassini A.
        • Curone G.
        • Solè M.
        • Capuani G.
        • Sciubba F.
        • Conta G.
        • Miccheli A.
        • Vigo D.
        NMR-based metabolomics to evaluate the milk composition from Friesian and autochthonous cows of Northern Italy at different lactation times.
        Nat. Prod. Res. 2019; 33 (29658316): 1085-1091
        • Torchiano M.
        effsize: Efficient effect size computation.
        (R package version 0.8.1.)
        • Vignoli A.
        • Ghini V.
        • Meoni G.
        • Licari C.
        • Takis P.G.
        • Tenori L.
        • Turano P.
        • Luchinat C.
        High-throughput metabolomics by 1D NMR.
        Angew. Chem. Int. Ed. Engl. 2019; 58 (29999221): 968-994
      1. Visentin G., 2022a. Supplementary Table S1. figshare. Figure. https://doi.org/10.6084/m9.figshare.20712427.v3.

      2. Visentin G., 2022b. Supplementary Table S2. figshare. Figure. https://doi.org/10.6084/m9.figshare.20712499.v2.

        • Voisin A.
        Grass Productivity.
        Philosophical Library Inc, 1959
        • Zhu D.
        • Kebede B.
        • Chen G.
        • McComb K.
        • Frew R.
        Changes in milk metabolome during the lactation of dairy cows based on 1H NMR and UHPLC–QToF/MS.
        Int. Dairy J. 2020; 111104836