Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra

Metabolic disorders in early lactation have negative effects on dairy cow health and farm profitability. One method for monitoring the metabolic status of cows is metabolic profiling, which uses associations between the concentrations of several metabolites in serum and the presence of metabolic disorders. In this cross-sectional study, we investigated the use of mid-infrared (MIR) spectroscopy of milk for predicting the concentrations of these metabolites in serum. Between July and October 2017, serum samples were taken from 773 early-lactation Holstein Friesian cows located on 4 farms in the Gippsland region of southeastern Victoria, Australia, on the same day as milk recording. The concentrations in sera of β-hydroxybutyrate (BHB), fatty acids, urea, Ca, Mg, albumin, and globulins were measured by a commercial diagnostic laboratory. Optimal concentration ranges for each of the 7 metabolites were obtained from the literature. Animals were classified as being either affected or unaffected with metabolic disturbances based on these ranges. Milk samples were analyzed by MIR spectroscopy. The relationships between serum metabolite concentrations and MIR spectra were investigated using partial least squares regression. Partial least squares discriminant analyses (PLS-DA) were used to classify animals as being affected or not affected with metabolic disorders. Calibration equations were constructed using data from a randomly selected subset of cows (n = 579). Data from the remaining cows (n = 194) were used for validation. The coefficient of determination (R²) of serum BHB, fatty acids, and urea predictions were 0.48, 0.61, and 0.90, respectively. Predictions of Ca, Mg, albumin, and globulin concentrations were poor (0.06 ≤ R² ≤ 0.17). The PLS-DA models could predict elevated fatty acid and urea concentrations with an accuracy of approximately 77 and 94%, respectively. A second independent validation data set was assembled in March 2018, comprising blood and milk samples taken from 105 autumn-calving cows of various breeds. The accuracies of BHB and fatty acid predictions were similar to those obtained using the first validation data set. The PLS-DA results were difficult to interpret due to the low prevalence of metabolic disorders in the data set. Our results demonstrate that MIR spectroscopy of milk shows promise for predicting the concentration of BHB, fatty acids, and urea in serum; however, more data are needed to improve prediction accuracies.     

Prediction of indicators of cow diet composition and authentication of feeding specifications of Protected Designation of Origin cheese using mid-infrared spectroscopy on milk

The ability of mid-infrared spectroscopy (MIR) to predict indicators (1) of diet composition in dairy herds and (2) for the authentication of the cow feeding restrictions included in the specification of 2 Protected Designation of Origin (PDO) cheeses (Cantal and Laguiole) was tested on 7,607 bulk milk spectra from 1,355 farms located in the Massif Central area of France. For each milk sample, the corresponding cow diet composition data were obtained through on-farm surveys. The cow diet compositions varied largely (i.e., from full grazing for extensive farming systems to corn silage-based diets, which are typical of more intensive farming systems). Partial least square regression and discriminant analysis were used to predict the proportion of different feedstuffs in the cows' diets and to authenticate the cow feeding restrictions for the PDO cheese specifications, respectively. The groups for the discriminant analysis were created by dividing the data set according to the threshold of a specific feedstuff. They were issued based on the specifications of the restriction of the PDO cheese. The pasture proportion in the cows' diets was predicted by MIR with an coefficient of determination in external validation (R²V) = 0.81 and a standard error of prediction of 11.7% dry matter. Pasture + hay, corn silage, conserved herbage, fermented forage, and total herbage proportion in the cows' diets were predicted with a R²V >0.61 and a standard error of prediction <14.8. The discrimination models for pasture presence, pasture ≥50%, and pasture ≥57% in the cows' diets achieved an accuracy and specificity ≥90%. A sensitivity and precision ≥85% were also observed for the pasture proportion discrimination models, but both of these indexes decreased at increasing thresholds from 0 to 50, and 57% pasture in the cows' diets. An accuracy ≥80% was also observed for pasture + hay ≥72%, herbage ≥50%, pasture + hay ≥25%, absence of fermented herbage, absence of corn silage, and corn silage ≤30% in the cows' diets, but for several models, either the sensitivity or precision was lower than the accuracy. Models built on the simultaneous respect of all the criteria of the feeding restrictions of PDO cheese specifications achieved an accuracy, specificity, sensitivity, and precision >90%. Both the regression and discriminant MIR models for bulk milk can provide useful indicators of cow diet composition and PDO cheese specifications to producers and consumers (farmers, dairy plants).     

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10-fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-by-herd external validation, using basic on-farm information (model 1) was not sufficient to classify good- and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or animal genotype from model 7 resulted in a reduction of the prediction accuracy of only 0.01 and 0.02, respectively. In conclusion, this study indicates that MIR and other on-farm data could be used to classify cows of good and poor likelihood of conception with promising accuracy.     

Assessing the effect of pregnancy stage on milk composition of dairy cows using mid-infrared spectra

Changes in milk production traits (i.e., milk yield, fat, and protein contents) with the pregnancy stage are well documented. To our knowledge, the effect of pregnancy on the detailed milk composition has not been studied so far. The mid-infrared (MIR) spectrum reflects the detailed composition of a milk sample and is obtained by a nonexhaustive and widely used method for milk analysis. Therefore, this study aimed to investigate the effect of pregnancy on milk MIR spectrum in addition to milk production traits (milk yield, fat, and protein contents). A model including regression on the number of days pregnant was applied on milk production traits (milk yield, fat, and protein contents) and on 212 spectral points from the MIR spectra of 9,757 primiparous Holstein cows from Walloon herds. Effects of pregnancy stage were expressed on a relative scale (effect divided by the squared root of the phenotypic variance); this allowed comparisons between effects on milk traits and on 212 spectral points. Effect of pregnancy stage on production traits were in line with previous studies indicating that the model accounted well for the pregnancy effect. Trends of the relative effect of the pregnancy stage on the 212 spectral points were consistent with known and observed effect on milk traits. The highest effect of the pregnancy was observed in the MIR spectral region from 968 to 1,577 cm^?1. For some specific wavenumbers, the effect was higher than for fat and protein contents in the beginning of the pregnancy (from 30 to 90 or 120 d pregnant). In conclusion, the effect of early pregnancy can be observed in the detailed milk composition through the analysis of the MIR spectrum of bovine milk. Further analyses are warranted to explore deeply the use of MIR spectra of bovine milk for breeding and management of dairy cow pregnancy.     

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.     

Persistence of differences between dairy cows categorized as low or high methane emitters,as estimated from milk mid-infrared spectra and measured by GreenFeed

Currently, various attempts are being made to implement breeding schemes aimed at producing low methane (CH₄) emitting cows. We investigated the persistence of differences in CH₄ emission between groups of cows categorized as either low or high emitters over a 5-mo period. Two feeding regimens (pasture vs. indoors) were used. Early- to mid-lactation Holstein Friesian cows were categorized as low or high emitters (n = 10 each) retrospectively, using predictions from milk mid-infrared (MIR) spectra, before the start of the experiment. Data from MIR estimates and from measurements with the GreenFeed (GF; C-Lock Technology Inc., Rapid City, SD) system over the 5-mo experiment were combined into 7-, 14-, and 28-d periods. Feed intake, eating and ruminating behavior, and ruminal fluid traits were determined in two 7-d measurement periods in the grazing season. The CH₄ emission data were analyzed using a split-plot ANOVA, and the repeatability of each of the applied methods for determining CH₄ emission was calculated. Traits other than CH₄ emission were analyzed for differences between low and high emitters using a linear mixed model. The initial category-dependent differences in daily CH₄ production persisted over the subsequent 5 mo and across 2 feeding regimens with both methods. The repeatability analysis indicated that the biweekly milk control scheme, and even a monthly scheme as practiced on farms, might be sufficient for confirming category differences. However, the relationship between CH₄ data estimated by MIR and measured with GF for individual cows was weak (R² = 0.26). The categorization based on CH₄ production also generated differences in CH₄ emission per kilogram of milk; differentiation between cow categories was not persistent based on milk MIR spectra and GF. Compared with the high emitters, low emitters tended to show a lower acetate-to-propionate ratio in ruminal volatile fatty acids, whereas feed intake and ruminating time did not differ. Interestingly, the low emitters spent less time eating than the high emitters. In conclusion, the CH₄ estimation from analyzing the milk MIR spectra is an appropriate proxy to form and regularly control categories of cows with different CH₄ production levels. The categorization was also sufficient to secure similar and persistent differences in emission intensity when estimated by MIR spectra of the milk. Further studies are needed to determine whether MIR data from individual cows are sufficiently accurate for breeding.     

Prediction of blood metabolites from milk mid-infrared spectra in early-lactation cows

Dairy cows commonly experience an unbalanced energy status in early lactation, and this condition can lead to the onset of several metabolic disorders. Blood metabolic profile testing is a valid tool to monitor and detect the most common early lactation disorders, but blood sampling and analysis are time-consuming and expensive, and the procedure is invasive and stressful for the cows. Mid-infrared (MIR) spectroscopy is routinely used to analyze milk composition, being a cost-effective and nondestructive method. The present study aimed to assess the feasibility of using routine milk MIR spectra for the prediction of main blood metabolites in dairy cows, and to investigate associations between measured blood metabolites and milk traits. Twenty herds of Holstein Friesian, Brown Swiss, or Simmental cows located in Northeast Italy were visited 1 to 4 times between December 2017 and June 2018, and blood and milk samples were collected from all lactating cows within 35 d in milk. Concentrations of main blood metabolites and milk MIR spectra were recorded from 295 blood and milk samples and used to develop prediction models for blood metabolic traits through backward interval partial least squares analysis. Blood β-hydroxybutyrate (BHB), urea, and nonesterified fatty acids were the most predictable traits, with coefficients of determination of 0.63, 0.58, and 0.52, respectively. On the contrary, predictive performance for blood glucose, triglycerides, cholesterol, glutamic oxaloacetic transaminase, and glutamic pyruvic transaminase were not accurate. Associations of blood BHB and urea with their respective contents in milk were moderate to strong, whereas all other correlations were weak. Predicted blood BHB showed an improved performance in detecting cows with hyperketonemia (blood BHB ≥ 1.2 mmol/L), compared with commercial calibration equation for milk BHB. Results highlighted the opportunity of using milk MIR spectra to predict blood metabolites and thus to collect routine information on the metabolic status of early-lactation cows at a population level.     

Phenotypic and genetic characterization of the occurrence of noncoagulating milk in dairy sheep

Milk coagulation ability is of central importance for the sheep dairy industry because almost all sheep milk is destined for cheese processing. The occurrence of milk with impaired coagulation properties is an obstacle to cheese processing and, in turn, to the profitability of the dairy companies. In this work, we investigated the causes of noncoagulation of sheep milk; specifically, we studied the effect of milk physicochemical properties on milk coagulation status [coagulating and noncoagulating (NC) milk samples, which do or do not coagulate within 30 min, respectively], and whether mid-infrared spectroscopy (MIR) could be used to assess variability in coagulation status. We also investigated the genetic background of milk coagulation ability. Individual milk samples were collected from 996 Sarda ewes farmed in 47 flocks located in Sardinia (Italy). Considered traits were daily milk yield, milk composition traits, and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness), and MIR spectra were acquired. About 9% of samples did not coagulate within 30 min. A logistic regression approach was used to test the effect of milk-related traits on milk coagulation status. A principal component (PC) analysis was carried out on the milk MIR spectra, and PC scores were then used as covariates in a logistic regression model to assess their relationship with milk coagulation status. Results of the present work demonstrated that the probability of having NC samples increases as milk contents of proteins and chlorides and somatic cell score increase. The analysis of PC extracted from milk spectra that influenced coagulation status highlighted key regions associated with lactose and protein concentrations, and others not associated with routinely collected milk composition traits. These results suggest that the occurrence of NC is mostly related to damage of the epithelium secretory mammary cells, which occurs with the advancement of a lactation or due to unhealthy mammary gland status. Genetic analysis of milk coagulation status and of the extracted PC confirmed the genetic background of the milk coagulability of sheep milk.     

Short communication: Prediction of milk coagulation and acidity traits in Mediterranean buffalo milk using Fourier-transform mid-infrared spectroscopy

Milk coagulation and acidity traits are important factors to inform the cheesemaking process. Those traits have been deeply studied in bovine milk, whereas scarce information is available for buffalo milk. However, the dairy industry is interested in a method to determine milk coagulation and acidity features quickly and in a cost-effective manner, which could be provided by Fourier-transform mid-infrared (FT-MIR) spectroscopy. The aim of this study was to evaluate the potential of FT-MIR to predict coagulation and acidity traits of Mediterranean buffalo milk. A total of 654 records from 36 herds located in central Italy with information on milk yield, somatic cell score, milk chemical composition, milk acidity [pH, titratable acidity (TA)], and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness) were available for statistical analysis. Reference measures of milk acidity and coagulation properties were matched with milk spectral information, and FT-MIR prediction models were built using partial least squares regression. The data set was divided into a calibration set (75%) and a validation set (25%). The capacity of FT-MIR spectroscopy to correctly classify milk samples based on their renneting ability was evaluated by a canonical discriminant analysis. Average values for milk coagulation traits were 13.32 min, 3.24 min, and 39.27 mm for rennet coagulation time, curd firming time, and curd firmness, respectively. Milk acidity traits averaged 6.66 (pH) and 7.22 Soxhlet-Henkel degrees/100 mL (TA). All milk coagulation and acidity traits, except for pH, had high variability (17 to 46%). Prediction models of coagulation traits were moderately to scarcely accurate, whereas the coefficients of determination of external validation were 0.76 and 0.66 for pH and TA, respectively. Canonical discriminant analysis indicated that information on milk coagulating ability is present in the MIR spectra, and the model correctly classified as noncoagulating the 91.57 and 67.86% of milk samples in the calibration and validation sets, respectively. In conclusion, our results can be relevant to the dairy industry to classify buffalo milk samples before processing.     

Development of a subacute ruminal acidosis risk score and its prediction using milk mid-infrared spectra in early-lactation cows

A routine monitoring for subacute ruminal acidosis (SARA) on the individual level could support the minimization of economic losses and the ensuring of animal welfare in dairy cows. The objectives of this study were (1) to develop a SARA risk score (SRS) by combining information from different data acquisition systems to generate an integrative indicator trait, (2) the investigation of associations of the SRS with feed analysis data, blood characteristics, performance data, and milk composition, including the fatty acid (FA) profile, (3) the development of a milk mid-infrared (MIR) spectra-based prediction equation for this novel reference trait SRS, and (4) its application to an external data set consisting of MIR data of test day records to investigate the association between the MIR-based predictions of the SRS and the milk FA profile. The primary data set, which was used for the objectives (1) to (3), consisted of data collected from 10 commercial farms with a total of 100 Holstein cows in early lactation. The data comprised barn climate parameters, pH and temperature logging from intrareticular measurement boluses, as well as jaw movement and locomotion behavior recordings of noseband-sensor halters and pedometers. Further sampling and data collection included feed samples, blood samples, milk performance, and milk samples, whereof the latter were used to get the milk MIR spectra and to estimate the main milk components, the milk FA profile, and the lactoferrin content. Because all measurements were characterized by different temporal resolutions, the data preparation consisted of an aggregation into values on a daily basis and merging it into one data set. For the development of the SRS, a total of 7 traits were selected, which were derived from measurements of pH and temperature in the reticulum, chewing behavior, and milk yield. After adjustment for fixed effects and standardization, these 7 traits were combined into the SRS using a linear combination and directional weights based on current knowledge derived from literature studies. The secondary data set was used for objective (4) and consisted of test day records of the entire herds, including performance data, milk MIR spectra and MIR-predicted FA. At farm level, it could be shown that diets with higher proportions of concentrated feed resulted in both lower daily mean pH and higher SRS values. On the individual level, an increased SRS could be associated with a modified FA profile (e.g., lower levels of short- and medium-chain FA, higher levels of C17:0, odd- and branched-chain FA). Furthermore, a milk MIR-based partial least squares regression model with a moderate predictability was established for the SRS. This work provides the basis for the development of routine SARA monitoring and demonstrates the high potential of milk composition-based assessment of the health status of lactating cows.     

Prediction of fatty acid profiles in cow,ewe, and goat milk by mid-infrared spectrometry

Mid-infrared (MIR) spectrometry was used to estimate the fatty acid (FA) composition in cow, ewe, and goat milk. The objectives were to compare different statistical approaches with wavelength selection to predict the milk FA composition from MIR spectra, and to develop equations for FA in cow, goat, and ewe milk. In total, a set of 349 cow milk samples, 200 ewe milk samples, and 332 goat milk samples were both analyzed by MIR and by gas chromatography, the reference method. A broad FA variability was ensured by using milk from different breeds and feeding systems. The methods studied were partial least squares regression (PLS), first-derivative pretreatment + PLS, genetic algorithm + PLS, wavelets + PLS, least absolute shrinkage and selection operator method (LASSO), and elastic net. The best results were obtained with PLS, genetic algorithm + PLS and first derivative + PLS. The residual standard deviation and the coefficient of determination in external validation were used to characterize the equations and to retain the best for each FA in each species. In all cases, the predictions were of better quality for FA found at medium to high concentrations (i.e., for saturated FA and some monounsaturated FA with a coefficient of determination in external validation >0.90). The conversion of the FA expressed in grams per 100 mL of milk to grams per 100 g of FA was possible with a small loss of accuracy for some FA.     

Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk

The aims of this study were to investigate variation of milk coagulation property (MCP) measures and their predictions obtained by mid-infrared spectroscopy (MIR), to investigate the genetic relationship between measures of MCP and MIR predictions, and to estimate the expected response from a breeding program focusing on the enhancement of MCP using MIR predictions as indicator traits. Individual milk samples were collected from 1,200 Brown Swiss cows (progeny of 50 artificial insemination sires) reared in 30 herds located in northern Italy. Rennet coagulation time (RCT, min) and curd firmness (a₃₀, mm) were measured using a computerized renneting meter. The MIR data were recorded over the spectral range of 4,000 to 900 cm⁻¹. Prediction models for RCT and a₃₀ based on MIR spectra were developed using partial least squares regression. A cross-validation procedure was carried out. The procedure involved the partition of available data into 2 subsets: a calibration subset and a test subset. The calibration subset was used to develop a calibration equation able to predict individual MCP phenotypes using MIR spectra. The test subset was used to validate the calibration equation and to estimate heritabilities and genetic correlations for measured MCP and their predictions obtained from MIR spectra and the calibration equation. Point estimates of heritability ranged from 0.30 to 0.34 and from 0.22 to 0.24 for RCT and a₃₀, respectively. Heritability estimates for MCP predictions were larger than those obtained for measured MCP. Estimated genetic correlations between measures and predictions of RCT were very high and ranged from 0.91 to 0.96. Estimates of the genetic correlation between measures and predictions of a₃₀ were large and ranged from 0.71 to 0.87. Predictions of MCP provided by MIR techniques can be proposed as indicator traits for the genetic enhancement of MCP. The expected response of RCT and a₃₀ ensured by the selection using MIR predictions as indicator traits was equal to or slightly less than the response achievable through a single measurement of these traits. Breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where routine acquisition of spectra from individual milk samples is already performed.     

Validation of mid-infrared spectrometry in milk for predicting body energy status in Holstein-Friesian cows

Cow energy balance is known to be associated with cow health and fertility; therefore, routine access to data on energy balance can be useful in both management and breeding decisions to improve cow performance. The objective of this study was to determine if individual cow milk mid-infrared spectra (MIR) could be useful to predict cow energy balance across contrasting production systems. Direct energy balance was calculated as the differential between energy intake and energy output in milk and maintenance (maintenance was predicted using body weight). Body energy content was calculated from (change in) body weight and body condition score. Following editing, 2,992 morning, 2,742 midday, and 2,989 evening milk MIR records from 564 lactations on 337 Scottish cows, managed in a confinement system on 1 of 2 diets, were available. An additional 844 morning and 820 evening milk spectral records from 338 lactations on 244 Irish cows offered a predominantly grazed grass diet were also available. Equations were developed to predict body energy status using the milk spectral data and milk yield as predictor variables. Several different approaches were used to test the robustness of the equations calibrated in one data set and validated in another. The analyses clearly showed that the variation in the validation data set must be represented in the calibration data set. The accuracy (i.e., square root of the coefficient of multiple determinations) of predicting, from MIR, direct energy balance, body energy content, and energy intake was 0.47 to 0.69, 0.51 to 0.56, and 0.76 to 0.80, respectively. This highlights the ability of milk MIR to predict body energy balance, energy content, and energy intake with reasonable accuracy. Very high accuracy, however, was not expected, given the likely random errors in the calculation of these energy status traits using field data.     

Mid infrared and fluorescence spectroscopies coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems

Mid infrared spectroscopy (MIR) combined with multivariate data analysis was used to discriminate between ewes milk samples according to their feeding systems (controls, ewes fed scotch bean and ewes fed soybean). The MIR spectra were scanned throughout the first 11 weeks of the lactation stage. When factorial discriminant analysis (FDA) with leave one-out cross-validation was applied, separately, to the three spectral regions in the MIR (i.e. 3000–2800, 1700–1500 and 1500–900 cm⁻¹), the classification rate was not satisfactory. Therefore, the first principal component (PCs) scores (corresponding to 3, 10 and 10 for, respectively, the 3000–2800, 1700–1500 and 1500–900 cm⁻¹) of the principal component analysis (PCA) extracted from each of the data sets were pooled (concatenated) into a single matrix and analysed by FDA. Correct classification amounting to 71.7% was obtained. Finally, the same procedure was applied to the MIR and fluorescence data sets and 98% of milk samples were found to be correctly classified. Milk samples belonging to control and soybean groups were 100% correctly classified. Regarding milk samples originating from the scotch bean group, only 2 out of 33 samples were misclassified. It was concluded that concatenation of the data sets collected from the two spectroscopic techniques is an efficient tool for authenticating milk samples according to their feeding systems, regardless of the lactation stage.     

Invited review: A 2020 perspective on pasture-based dairy systems and products

Grazing pasture is the basis for dairy production systems in regions with temperate climates, such as in Ireland, New Zealand, parts of Australia, the United States, and Europe. Milk and dairy products from cows on pasture-based farms predominantly consuming fresh grazed grass (typically classified as “grass-fed” milk) have been previously shown to possess a different nutrient profile, with potential nutritional benefits, compared with conventional milk derived from total mixed ration. Moreover, pasture-based production systems are considered more environmentally and animal welfare friendly by consumers. As such, there is significant potential for market capitalization on grass-fed dairy products. As competition in this space increases, the regulations of what constitutes as grass-fed vary between different regions of the world. With this in mind, there is a need for clear and independently accredited grass-fed standards, defining the grass-fed criteria for labeling of products as such, subsequently increasing the clarity and confidence for the consumer. This review outlines the numerous effects of pasture production systems on dairy product composition, nutritional profile, and sustainability, and highlights potential future methods for authentication.     

The use of mid-infrared spectra to map genes affecting milk composition

The aim of this study was to investigate the feasibility of using mid-infrared (MIR) spectroscopy analysis of milk samples to increase the power and precision of genome-wide association studies (GWAS) for milk composition and to better distinguish linked quantitative trait loci (QTL). To achieve this goal, we analyzed phenotypic data of milk composition traits, related MIR spectra, and genotypic data comprising 626,777 SNP on 5,202 Holstein, Jersey, and crossbred cows. We performed a conventional GWAS on protein, lactose, fat, and fatty acid concentrations in milk, a GWAS on individual MIR wavenumbers, and a partial least squares regression (PLS), which is equivalent to a multi-trait GWAS, exploiting MIR data simultaneously to predict SNP genotypes. The PLS detected most of the QTL identified using single-trait GWAS, usually with a higher significance value, as well as previously undetected QTL for milk composition. Each QTL tends to have a different pattern of effects across the MIR spectrum and this explains the increased power. Because SNP tracking different QTL tend to have different patterns of effect, it was possible to distinguish closely linked QTL. Overall, the results of this study suggest that using MIR data through either GWAS or PLS analysis applied to genomic data can provide a powerful tool to distinguish milk composition QTL.     

Energy profiling of dairy cows from routine milk mid-infrared analysis

The balance of body energy within and across lactations can have health and fertility consequences for the dairy cow. This study aimed to create a large calibration data set of dairy cow body energy traits across the cow's productive life, with concurrent milk mid-infrared (MIR) spectral data, to generate a prediction tool for use in commercial dairy herds. Detailed phenotypic data from 1,101 Holstein Friesian cows from the Langhill research herd (SRUC, Scotland) were used to generate energy balance (EB) and effective energy intake (EI), both in megajoules per day. Pretreatment of spectral data involved standardization to account for drift over time and machine. Body energy estimates were aligned with their spectral data to generate a prediction of these traits based on milk MIR spectroscopy. After data edits, partial least squares analysis generated prediction equations with a coefficient of determination from split sample 10-fold cross validation of 0.77 and 0.75 for EB and EI, respectively. These prediction equations were applied to national milk MIR spectra on over 11 million animal test dates (January 2013 to December 2016) from 4,453 farms. The predictions generated from these were subject to phenotypic analyses with a fixed regression model highlighting differences between the main dairy breeds in terms of energy traits. Genetic analyses generated heritability estimates for EB and EI ranging from 0.12 to 0.17 and 0.13 to 0.15, respectively. This study shows that MIR-based predictions from routinely collected national data can be used to generate predictions of dairy cow energy turnover profiles for both animal management and genetic improvement of such difficult and expensive-to-record traits.     

Predicting pregnancy status from mid-infrared spectroscopy in dairy cow milk using deep learning

Accurately identifying pregnancy status is imperative for a profitable dairy enterprise. Mid-infrared (MIR) spectroscopy is routinely used to determine fat and protein concentrations in milk samples. Mid-infrared spectra have successfully been used to predict other economically important traits, including fatty acid content, mineral content, body energy status, lactoferrin, feed intake, and methane emissions. Machine learning has been used in a variety of fields to find patterns in vast quantities of data. This study aims to use deep learning, a sub-branch of machine learning, to establish pregnancy status from routinely collected milk MIR spectral data. Milk spectral data were obtained from National Milk Records (Chippenham, UK), who collect large volumes of data continuously on a monthly basis. Two approaches were followed: using genetic algorithms for feature selection and network design (model 1), and transfer learning with a pretrained DenseNet model (model 2). Feature selection in model 1 showed that the number of wave points in MIR data could be reduced from 1,060 to 196 wave points. The trained model converged after 162 epochs with validation accuracy and loss of 0.89 and 0.18, respectively. Although the accuracy was sufficiently high, the loss (in terms of predicting only 2 labels) was considered too high and suggested that the model would not be robust enough to apply to industry. Model 2 was trained in 2 stages of 100 epochs each with spectral data converted to gray-scale images and resulted in accuracy and loss of 0.97 and 0.08, respectively. Inspection on inference data showed prediction sensitivity of 0.89, specificity of 0.86, and prediction accuracy of 0.88. Results indicate that milk MIR data contains features relating to pregnancy status and the underlying metabolic changes in dairy cows, and such features can be identified by means of deep learning. Prediction equations from trained models can be used to alert farmers of nonviable pregnancies as well as to verify conception dates.     

Impact of fatty acid composition on the accuracy of mid-infrared fat analysis of farm milks

Our objective was to determine whether data from a previous study using model milk emulsions to characterize the influence of variation in fatty acid chain length and unsaturation on mid-infrared (MIR) fat predictions could be used to identify a strategy to improve the accuracy of MIR fat predictions on a population of farm milks with a wide variation in fatty acid chain length and unsaturation. The mean fatty acid chain length for 45 farm milks was 14.417 carbons, and the mean unsaturation was 0.337 double bonds per fatty acid. The range of fatty acid chain lengths across the 45 farm milks was 1.23 carbons, and the range in unsaturation was 0.167 double bonds per fatty acid. Fat B (absorbance by the carbon-hydrogen stretch) MIR predictions increased and fat A MIR (absorbance by the ester carbonyl stretch) predictions decreased relative to reference chemistry with increasing fatty acid chain length. When the fat B MIR fat predictions were corrected for sample-to-sample variation in unsaturation, the positive correlation between fat B and fatty acid chain length increased from a coefficient of determination of 0.42 to 0.89. A 45:55 ratio of fat B corrected for unsaturation and fat A gave a smaller standard deviation of the difference between MIR prediction and reference chemistry than any ratio of the fat B (without correction for unsaturation) and fat A or either fat B or fat A alone. This demonstrates the technical feasibility of this approach to improve MIR testing accuracy for fat, if a simple procedure could be developed to determine the unsaturation of fat in milk rapidly and to correct the fat B reading for the effect of unsaturation before being combined with fat A.     

低场核磁技术结合化学计量学法快速检测掺假牛奶

应用低场核磁共振技术结合主成分分析法(PCA)、偏最小二乘判别法(PLS-DA)、线性判别法(LDA)对掺水、食盐、尿素和蔗糖的牛奶及纯牛奶进行测定。结果表明:在主成分得分图中,不同掺假牛奶随掺假物质的掺假比例呈一定规律性分布,并得到了很好的区分。利用PLS-DA和LDA方法建立不同掺假牛奶的判别模型,对掺水、掺尿素牛奶的判别准确率均为100%,掺食盐牛奶的判别准确率分别为83.33%和100%,掺蔗糖牛奶的判别准确率分别为73.33%和76.67%。可见PCA法、PLS-DA法、LDA法可用于快速处理分析低场核磁共振数据,并且LDA法鉴别准确率最高。