Usefulness of mid-infrared spectroscopy as a tool to estimate body condition score change from milk samples in intensively fed dairy cows.

Directly measuring individual cow energy balance is not trivial. Other traits such as body condition score (BCS) and BCS change (ΔBCS) can, however, be used as an indicator of cow energy status. Body condition score is a metric used worldwide to estimate cow body reserves, but the estimation of ΔBCS was, until now, conditional on the availability of multiple BCS assessments. The aim of the present study was to estimate ΔBCS from milk mid-infrared (MIR) spectra and days in milk (DIM) in intensively fed dairy cows using statistical prediction methods. Daily BCS was interpolated from cubic splines fitted through the BCS records and daily ΔBCS was calculated from these splines. The ΔBCS records were merged with milk MIR spectra recorded on the same week. The dataset comprised 37,077 ΔBCS phenotypes across 9,403 lactations from 6,988 cows in 151 herds based in Quebec, Canada. Partial least squares regression (PLSR) and a neural network (NN) were then used to estimate ΔBCS from (1) MIR spectra only, (2) DIM only, or (3) MIR spectra and DIM together. The ΔBCS data in both the first 120 and 305 DIM of lactation were used to develop the estimates. Daily ΔBCS had a standard deviation of 4.40 × 10-3 BCS units in the 120-d dataset and of 3.63 × 10-3 BCS units in the 305-d dataset. A 4-fold cross-validation was used to calibrate and test the prediction equations. External validation was also conducted using more recent years of data. Irrespective of whether based on the first 120 or 305 DIM, or when MIR spectra only, DIM only or MIR spectra and DIM were jointly used as prediction variables, NN produced the lowest root mean square error (RMSE) of cross-validation (1.81 × 10-3 BCS units and 1.51 × 10-3 BCS units, respectively, using the 120-d and 305-d dataset). Relative to predictions for the entire 305 DIM, the RMSE of cross-validation was 15.4% and 1.5% lower in the first 120 DIM when using PLSR and NN, respectively. Predictions from DIM only were more accurate than those using just MIR spectra data but, irrespective of the dataset and of the prediction model used, combining DIM information with MIR spectral data as prediction variables reduced the RMSE compared with the inclusion of DIM alone, albeit the benefit was small (the RMSE from cross-validation reduced by up to 5.5% when DIM and spectral data were jointly used as model features instead of DIM only). However, when predicting extreme ΔBCS records, the MIR spectral data were more informative than DIM. Model performance when predicting ΔBCS records in future years was similar to that from cross-validation demonstrating the ability of MIR spectra of milk and DIM combined to estimate ΔBCS, particularly in early lactation. This can be used to routinely generate estimates of ΔBCS to aid in day-to-day individual cow management.

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

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

Combined use of milk infrared spectra and genotypes can improve prediction of milk fat composition.

It has been shown that milk infrared (IR) spectroscopy can be used to predict detailed milk fat composition. In addition, polymorphisms with substantial effects on milk fat composition have been identified. In this study, we investigated the combined use of milk IR spectroscopy and genotypes of dairy cows on the accuracy of predicting milk fat composition. Milk fat composition data based on gas chromatography and milk IR spectra were available for 1,456 Dutch Holstein Friesian cows. In addition, genotypes for the diacylglycerol acyltransferase 1 (DGAT1) K232A and stearoyl-CoA desaturase 1 (SCD1) A293V polymorphisms and a SNP located in an intron of the fatty acid synthase (FASN) gene were available. Adding SCD1 genotypes to the milk IR spectra resulted in a considerable improvement of the prediction accuracy for the unsaturated fatty acids C10:1, C12:1, C14:1 cis-9, and C16:1 cis-9 and their corresponding unsaturation indices. Adding DGAT1 genotypes to the milk IR spectra resulted in an improvement of the prediction accuracy for C16:1 cis-9 and C16 index. Adding genotypes of the FASN SNP to the IR spectra did not improve prediction of milk fat composition. This study demonstrated the potential of combining milk IR spectra with genotypic information from 3 polymorphisms to predict milk fat composition. We hypothesize that prediction accuracy of milk fat composition can be further improved by combining milk IR spectra with genomic breeding values.

Using serum and plasma samples to assess failure of transfer of passive immunity in dairy calves.

The objectives of this study were (1) to determine the differences in IgG and total protein (TP) content of serum and plasma samples collected from the same calves; (2) to evaluate the correlation between calf serum and plasma IgG levels, Brix scores, and TP concentrations; (3) to determine whether different cut-off values should be used for plasma and serum to assess failure of transfer of passive immunity (FTPI) in dairy calves; and (4) to evaluate the level of agreement between results obtained from using serum and plasma samples of the same calves to assess FTPI using optimal cut-off values. Blood samples (n = 217) were collected from Holstein calves at 3 to 10 d of age on 30 commercial dairy farms in Nova Scotia and Newfoundland, Canada. Paired serum and plasma samples were analyzed for IgG concentration by the reference radial immunodiffusion assay, transmission infrared (TIR) spectroscopy, digital and optical Brix refractometers, and optical TP refractometer. The IgG concentrations measured by RID and TIR spectroscopy in serum were similar to those in plasma. However, the Brix and TP refractometer readings were significantly higher in plasma than in serum. The prevalence of FTPI in serum and plasma samples based on a RID-IgG concentration <10 g/L was 43.3 and 46.5%, respectively. The RID-IgG concentration was correlated with TIR-IgG (r = 0.92 and 0.89), digital Brix (r = 0.80 and 0.80), optical Brix (r = 0.77 and 0.77), and optical TP (r = 0.75 and 0.77) refractometers in serum and plasma, respectively. The correlations between paired serum and plasma IgG content were 0.85 by TIR spectroscopy, 0.80 by digital Brix, 0.77 by optical Brix, and 0.79 by optical TP refractometer. The optimal cut-off values for TIR spectroscopy, digital Brix, optical Brix, and TP refractometers to assess FTPI using serum were 13.1 g/L, 8.7% Brix, 8.4% Brix and 5.1 g/dL, respectively; and the optimal cut-off values with plasma were 13.4 g/L, 9.4% Brix, 9.3% Brix and 5.8 g/dL, respectively. When using these optimal cut-off values, the level of agreement (88.1%) between results derived from testing serum and plasma by TIR spectroscopy was substantial, with a kappa (κ) value of 0.76. The results derived from testing serum and plasma by digital Brix refractometer showed substantial agreement (83.4%), with a κ value of 0.65, which is higher than the agreement and κ value (74.7% and 0.51) reported for the optical Brix refractometer. Substantial agreement (81.6%) between serum and plasma TP was also obtained when using the optical TP refractometer, with a κ value of 0.63. In conclusion, serum or plasma samples can be used interchangeably for measuring IgG concentrations and assessing FTPI in dairy calves. However, different cut-offs must be used to assess FTPI depending on the sample matrix. Furthermore, results obtained from serum samples showed higher agreement with the reference RID assay than those obtained from plasma samples.

Mineral composition of cow milk from multibreed herds.

This study estimated the effect of Holstein-Friesian, Brown Swiss, Jersey, Simmental and Alpine Grey cattle breeds on milk mineral contents (Ca, Mg, P, K, and Na) in multibreed herds using data predicted with mid-infrared spectroscopy. The dataset included 139,821 observations from 16,566 cows and 977 herds. Fixed effects considered in the mixed model were breed, parity, stage of lactation and first-order interactions, and random effects were cow, herd-test-date, and the residual. Multiple comparisons of least squares means were performed for the main effect of breed, parity, and stage of lactation using Bonferroni adjustment. Holstein-Friesian yielded milk with the lowest fat, protein, and casein concentration, and Ca, Mg, and P contents, whereas Jersey cows produced milk with the greatest fat, protein, and casein concentration, and Ca and Mg contents. Results of this study suggest that mixing milk from different breeds could enhance milk composition and technological ability, and therefore contribute to improve dairy industry efficiency.

Application of transmission infrared spectroscopy and partial least squares regression to predict immunoglobulin G concentration in dairy and beef cow colostrum.

The objective of this study was to explore the potential of transmission infrared (TIR) spectroscopy in combination with partial least squares regression (PLSR) for quantification of dairy and beef cow colostral immunoglobulin G (IgG) concentration and assessment of colostrum quality. A total of 430 colostrum samples were collected from dairy (n = 235) and beef (n = 195) cows and tested by a radial immunodiffusion (RID) assay and TIR spectroscopy. Colostral IgG concentrations obtained by the RID assay were linked to the preprocessed spectra and divided into combined and prediction data sets. Three PLSR calibration models were built: one for the dairy cow colostrum only, the second for beef cow colostrum only, and the third for the merged dairy and beef cow colostrum. The predictive performance of each model was evaluated separately using the independent prediction data set. The Pearson correlation coefficients between IgG concentrations as determined by the TIR-based assay and the RID assay were 0.84 for dairy cow colostrum, 0.88 for beef cow colostrum, and 0.92 for the merged set of dairy and beef cow colostrum. The average of the differences between colostral IgG concentrations obtained by the RID- and TIR-based assays were -3.5, 2.7, and 1.4 g/L for dairy, beef, and merged colostrum samples, respectively. Further, the average relative error of the colostral IgG predicted by the TIR spectroscopy from the RID assay was 5% for dairy cow, 1.2% for beef cow, and 0.8% for the merged data set. The average intra-assay CV% of the IgG concentration predicted by the TIR-based method were 3.2%, 2.5%, and 6.9% for dairy cow, beef cow, and merged data set, respectively.The utility of TIR method for assessment of colostrum quality was evaluated using the entire data set and showed that TIR spectroscopy accurately identified the quality status of 91% of dairy cow colostrum, 95% of beef cow colostrum, and 89% and 93% of the merged dairy and beef cow colostrum samples, respectively. The results showed that TIR spectroscopy demonstrates potential as a simple, rapid, and cost-efficient method for use as an estimate of IgG concentration in dairy and beef cow colostrum samples and assessment of colostrum quality. The results also showed that merging the dairy and beef cow colostrum sample data sets improved the predictive ability of the TIR spectroscopy.

Effect of Heat-treatment on Accuracy of Infrared Spectroscopy and Digital and Optical Brix Refractometers for Measuring Immunoglobulin G Concentration in Bovine Colostrum.

BACKGROUND: Heat-treatment of colostrum is a method developed to reduce calf exposure to pathogens. Infrared (IR) spectroscopy and Brix refractometers can be used for measuring colostral IgG concentration and assessing colostrum quality. OBJECTIVES: To determine the impact of heat-treatment on accuracy of IR spectroscopy and Brix refractometers for measuring colostral IgG concentration and assessing colostrum quality before and after heat-treatment. ANIMALS: A total of 60 Holstein dairy cows on 8 commercial dairy farms. METHODS: A cross-sectional study was designed to determine the effect of heat-treatment at 60°C and 63°C each for 30 and 60 minutes duration on colostral IgG concentration measured by the reference radial immunodiffusion (RID) assay, IR spectroscopy, and digital and optical refractometers. RESULTS: Colostrum IgG concentration significantly decreased after heat-treatment at 63°C for 30 or 60 minutes as measured by RID, but the IgG values remained unchanged when measured by IR spectroscopy and refractometers. The lowest correlation coefficient found between IR spectroscopy (r = 0.70) and RID results was in colostrum heat-treated at 63°C for 60 minutes. For digital (r = 0.48) and optical (r = 0.50) refractometers, the lowest correlation coefficient was at 63°C for 30 minutes when compared to RID. The accuracy of the IR spectroscopy, digital and optical Brix refractometers was decreased from 91.7 to 80%, 81.7 to 45%, and 80 to 45%, respectively, when colostrum heat-treated at 63°C for 60 minutes. CONCLUSIONS AND CLINICAL IMPORTANCE: Radial immunodiffusion, IR spectroscopy, and Brix refractometers exhibit utility for measuring IgG concentration when colostrum heat-treated at 60°C but does not detect decrease IgG concentrations when heat-treated at 63°C.

Colour measurement of colostrum for estimation of colostral IgG and colostrum composition in dairy cows.

Instruments for on-farm determination of colostrum quality such as refractometers and densimeters are increasingly used in dairy farms. The colour of colostrum is also supposed to reflect its quality. A paler or mature milk-like colour is associated with a lower colostrum value in terms of its general composition compared with a more yellowish and darker colour. The objective of this study was to investigate the relationships between colour measurement of colostrum using the CIELAB colour space (CIE L*=from white to black, a*=from red to green, b*=from yellow to blue, chroma value G=visual perceived colourfulness) and its composition. Dairy cow colostrum samples (n=117) obtained at 4·7±1·5 h after parturition were analysed for immunoglobulin G (IgG) by ELISA and for fat, protein and lactose by infrared spectroscopy. For colour measurements, a calibrated spectrophotometer was used. At a cut-off value of 50 mg IgG/ml, colour measurement had a sensitivity of 50·0%, a specificity of 49·5%, and a negative predictive value of 87·9%. Colostral IgG concentration was not correlated with the chroma value G, but with relative lightness L*. While milk fat content showed a relationship to the parameters L*, a*, b* and G from the colour measurement, milk protein content was not correlated with a*, but with L*, b*, and G. Lactose concentration in colostrum showed only a relationship with b* and G. In conclusion, parameters of the colour measurement showed clear relationships to colostral IgG, fat, protein and lactose concentration in dairy cows. Implementation of colour measuring devices in automatic milking systems and milking parlours might be a potential instrument to access colostrum quality as well as detecting abnormal milk.

Genetic correlations of days open with production traits and contents in milk of major fatty acids predicted by mid-infrared spectrometry.

The objective of this study was to estimate the genetic relationships between days open (DO) and both milk production traits and fatty acid (FA) content in milk predicted by mid-infrared spectrometry. The edited data set included 143,332 FA and production test-day records and 29,792 DO records from 29,792 cows in 1,170 herds. (Co)variances were estimated using a series of 2-trait models that included a random regression for milk production and FA traits. In contrast to the genetic correlations with fat content, those between DO and FA content in milk changed considerably over the lactation. The genetic correlations with DO for unsaturated FA, monounsaturated FA, long-chain FA, C18:0, and C18:1 cis-9 were positive in early lactation but negative after 100 d in milk. For the other FA, genetic correlations with DO were negative across the whole lactation. At 5 d in milk, the genetic correlation between DO and C18:1 cis-9 was 0.39, whereas the genetic correlations between DO and C6:0 to C16:0 FA ranged from -0.37 to -0.23. These results substantiated the known relationship between fertility and energy balance status, explained by the release of long-chain FA in early lactation, from the mobilization of body fat reserves, and the consequent inhibition of de novo FA synthesis in the mammary gland. At 200 d in milk, the genetic correlations between DO and FA content ranged from -0.38 for C18:1 cis-9 to -0.03 for C6:0. This research indicates an opportunity to use FA content in milk as an indicator trait to supplement the prediction of genetic merit for fertility.