Mid-infrared Milk Research Articles

Accuracy of methane emissions predicted from milk mid-infrared spectra and measured by laser methane detectors in Brown Swiss dairy cows

Since heritability of CH4 emissions in ruminants was demonstrated, various attempts to generate large individual animal CH4 data sets have been initiated. Predicting individual CH4 emissions based on equations using milk mid-infrared (MIR) spectra is currently considered promising as a low-cost proxy. However, the CH4 emission predicted by MIR in individuals still has to be confirmed by measurements. In addition, it remains unclear how low CH4 emitting cows differ in intake, digestion, and efficiency from high CH4 emitters. In the current study, putatively low and putatively high CH4 emitting Brown Swiss cows were selected from the entire Swiss herdbook population (176,611 cows), using an MIR-based prediction equation. Eventually, 15 low and 15 high CH4 emitters from 29 different farms were chosen for a respiration chamber (RC) experiment in which all cows were fed the same forage-based diet. Several traits related to intake, digestion, and efficiency were quantified over 8 d, and CH4 emission was measured in 4 open circuit RC. Daily CH4 emissions were also estimated using data from 2 laser CH4 detectors (LMD). The MIR-predicted CH4 production (g/d) was quite constant in low and high emission categories, in individuals across sites (home farm, experimental station), and within equations (first available and refined versions). The variation of the MIR-predicted values was substantially lower using the refined equation. However, the predicted low and high emitting cows (n = 28) did not differ on average in daily CH4 emissions measured either with RC or estimated using LMD, and no correlation was found between CH4 predictions (MIR) and CH4 emissions measured in RC. When individuals were recategorized based on CH4 yield measured in RC, differences between categories of 10 low and 10 high CH4 emitters were about 20%. Low CH4 emitting cows had a higher feed intake, milk yield, and residual feed intake, but they differed only weakly in eating pattern and digesta mean retention times. Low CH4 emitters were characterized by lower acetate and higher propionate proportions of total ruminal volatile fatty acids. We concluded that the current MIR-based CH4 predictions are not accurate enough to be implemented in breeding programs for cows fed forage-based diets. In addition, low CH4 emitting cows have to be characterized in more detail using mechanistic studies to clarify in more detail the properties that explain the functional differences found in comparison with other cows.

Mastitis Detection from Milk Mid-Infrared (MIR) Spectroscopy in Dairy Cows

Mid-infrared (MIR) spectroscopy is the method of choice for the standard milk recording system, to determine milk components including fat, protein, lactose and urea. Since milk composition is related to health and metabolic status of a cow, MIR spectra could be potentially used for disease detection. In dairy production, mastitis is one of the most prevalent diseases. The aim of this study was to develop a calibration equation to predict mastitis events from routinely recorded MIR spectra data. A further aim was to evaluate the use of test day somatic cell score (SCS) as covariate on the accuracy of the prediction model. The data for this study is from the Austrian milk recording system and its health monitoring system (GMON). Test day data including MIR spectra data was merged with diagnosis data of Fleckvieh, Brown Swiss and Holstein Friesian cows. As prediction variables, MIR absorbance data after first derivatives and selection of wavenumbers, corrected for days in milk, were used. The data set contained roughly 600,000 records and was split into calibration and validation sets by farm. Calibration sets were made to be balanced (as many healthy as mastitis cases), while the validation set was kept large and realistic. Prediction was done with Partial Least Squares Discriminant Analysis, key indicators of model fit were sensitivity and specificity. Results were extracted for association between spectra and diagnosis with different time windows (days between diagnosis and test days) in validation. The comparison of different sets of predictor variables (MIR, SCS, MIR + SCS) showed an advantage in prediction for MIR + SCS. For this prediction model, specificity was 0.79 and sensitivity was 0.68 in time window -7 to +7 days (calibration and validation). Corresponding values for MIR were 0.71 and 0.61, for SCS they were 0.81 and 0.62. In general, prediction of mastitis performed better with a shorter distance between test day and mastitis event, yet even for time windows of -21 to +21 days, prediction accuracies were still reasonable, with sensitivities ranging from 0.50 to 0.57 and specificities remaining unchanged (0.71 to 0.85). Additional research to further improve prediction equation, and studies on genetic correlations among clinical mastitis, SCS and MIR predicted mastitis are planned.

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.

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.

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.

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.

Longitudinal Analysis of Macronutrient Composition in Preterm and Term Human Milk: A Prospective Cohort Study

Background: Mother’s own milk is the optimal source of nutrients and provides numerous health advantages for mothers and infants. As they have supplementary nutritional needs, very preterm infants may require fortification of human milk (HM). Addressing HM composition and variations is essential to optimize HM fortification strategies for these vulnerable infants. Aims: To analyze and compare macronutrient composition in HM of mothers lactating very preterm (PT) (28 0/7 to 32 6/7 weeks of gestational age, GA) and term (T) infants (37 0/7 to 41 6/7 weeks of GA) over time, both at similar postnatal and postmenstrual ages, and to investigate other potential factors of variations. Methods: Milk samples from 27 mothers of the PT infants and 34 mothers of the T infants were collected longitudinally at 12 points in time during four months for the PT HM and eight points in time during two months for the T HM. Macronutrient composition (proteins, fat, and lactose) and energy were measured using a mid-infrared milk analyzer, corrected by bicinchoninic acid (BCA) assay for total protein content. Results: Analysis of 500 HM samples revealed large inter- and intra-subject variations in both groups. Proteins decreased from birth to four months in the PT and the T HM without significant differences at any postnatal time point, while it was lower around term equivalent age in PT HM. Lactose content remained stable and comparable over time. The PT HM contained significantly more fat and tended to be more caloric in the first two weeks of lactation, while the T HM revealed higher fat and higher energy content later during lactation (three to eight weeks). In both groups, male gender was associated with more fat and energy content. The gender association was stronger in the PT group, and it remained significant after adjustments. Conclusion: Longitudinal measurements of macronutrients compositions of the PT and the T HM showed only small differences at similar postnatal stages in our population. However, numerous differences exist at similar postmenstrual ages. Male gender seems to be associated with a higher content in fat, especially in the PT HM. This study provides original information on macronutrient composition and variations of HM, which is important to consider for the optimization of nutrition and growth of PT infants.

Phenotypic investigation of fine milk components in bovine milk and their prediction using mid-infrared spectroscopy

This study aimed to examine the phenotypic variation observed in fine milk components, the use of mid-infrared (MIR) spectroscopy to predict these components, and the correlations with other milk production traits. A total of 4116 milk samples were collected from 414 dairy cows from 44 Canadian herds. Overall 3117 samples were analyzed for casein micelle size, 986 and 937 for total and soluble calcium, respectively, and 2054 for lactoferrin. Individual milk MIR spectra were obtained. Linear mixed models with repeated records were used to test the effects of herd nested within breed, breed, days in milk class, parity, and season on milk components. Lactoferrin content in milk increased with stage of lactation and parity number. Prediction equations were developed by partial least squares regression to predict each milk component using MIR spectroscopy, with the greatest coefficient of determination achieved for lactoferrin (0.55). Pearson correlation coefficients for lactoferrin with somatic cell score and protein percentage were 0.49% and 0.40%, respectively. The estimated correlation increased to 0.53% for predicted lactoferrin and protein percentage. The examined fine milk components exhibited variation, but low accuracies for their MIR prediction may limit the utility of this technology for their phenotyping.

Determination of fat, protein, moisture, and salt content of Cheddar cheese using mid-infrared transmittance spectroscopy

The objective of our work was to develop and evaluate the performance of a rapid method for measuring fat, protein, moisture, and salt content of Cheddar cheese using a combination mid-infrared (MIR) transmittance analysis and an in-line conductivity sensor in an MIR milk analyzer. Cheddar cheese was blended with a dissolving solution containing pentasodium triphosphate and disodium metasilicate to achieve a uniform, particle-free dispersion of cheese, which had a fat and protein content similar to milk and could be analyzed using a MIR transmittance milk analyzer. Annatto-colored Cheddar cheese samples (34) from one cheese factory were analyzed using reference chemistry methods for fat (Mojonnier ether extraction), crude protein (Kjeldahl), moisture (oven-drying total solids), and salt (Volhard silver nitrate titration). The same 34 cheese samples were also dissolved using the cheese dissolver solution, and then run through the MIR and used for calibration. The reference testing for fat and crude protein was done on the cheese after dispersion in the dissolver solution. Validation was done using a total of 36 annatto-colored Cheddar cheese samples from 4 cheese factories. The 36 validation cheese samples were also analyzed using near-infrared spectroscopy for fat, moisture, and the coulometric method for salt in each factory where they were produced. The validation cheeses were also tested using the same chemical reference methods that were used for analysis of the calibration samples. Standard error of prediction (SEP) values for moisture and fat on the near-infrared spectroscopy were 0.30 and 0.45, respectively, whereas the MIR produced SEP values of 0.28 and 0.23 for moisture (mean 36.82%) and fat (mean 34.0%), respectively. The MIR also out-performed the coulometric method for salt determination with SEP values of 0.036 and 0.139 at a mean level of salt of 1.8%, respectively. The MIR had an SEP value of 0.19 for estimation at a mean level of 24.0% crude protein, which suggests that MIR could be an easy and effective way for cheese producers to measure protein to determine protein recovery in cheese making.

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.

Effect of homogenizer performance on accuracy and repeatability of mid-infrared predicted values for major milk components.

Our objective was to determine the effect of mid-infrared (MIR) homogenizer efficiency on accuracy and repeatability of Fourier transform MIR predicted fat, true protein, and anhydrous lactose determination given by traditional filter and partial least squares (PLS) prediction models. Five homogenizers with different homogenization performance based on laser light-scattering particle size analysis were used. Repeatability and accuracy were determined by conducting 17 sequential readings on milk homogenized externally to the instrument (i.e., control) and unhomogenized milk. Milk component predictions on externally homogenized milks were affected by variation in homogenizer performance, but the magnitude of effect were small (i.e., <0.025%) when milks were pumped through both efficient and inefficient homogenizers within a MIR milk analyzer. Variation in the in-line MIR homogenizer performance on unhomogenized milks had a much larger effect on accuracy of component testing than on repeatability. The increase of particle size distribution [d(0.9)] from 1.35 to 3.03μm (i.e., fat globule diameter above which 10% of the volume of fat is contained) due to poor homogenization affected fat tests the most; traditional filter based fat B (carbon hydrogen stretch; -0.165%), traditional filter-based fat A (carbonyl stretch; -0.074%), and fat PLS (-0.078%) at a d(0.9) of 3.03μm. Variation in homogenization efficiency also affected traditional filter-based true protein test (+0.012%), true protein PLS prediction (-0.107%), and traditional filter-based anhydrous lactose test (+0.027%) at a d(0.9) of 3.03μm. Effects of variation in homogenization on anhydrous lactose PLS predictions were small. The accuracy of both traditional filter models and PLS models were influenced by poor homogenization. The value of 1.7µm for a d(0.9) used by the USDA Federal Milk Market laboratories as a criterion to make the decision to replace the homogenizer in a MIR milk analyzer appears to be a reasonable limit, given the magnitude of effect on the accuracy of fat tests. In the future, as new PLS models are developed to measure other components in milk, the sensitivity of the accuracy of the predictions of these models to factors such as variation of homogenizer performance should be determined as part of the ruggedness testing during PLS model development.

Prediction of fat globule particle size in homogenized milk using Fourier transform mid-infrared spectra.

Our objective was to develop partial least square models using data from Fourier transform mid-infrared (MIR) spectra to predict the particle size distributions d(0.5) and d(0.9), surface volume mean diameter D[3,2], and volume moment mean diameter D[4,3] of milk fat globules and validate the models. The goal of the study was to produce a method built into the MIR milk analyzer that could be used to warn the instrument operator that the homogenizer is near failure and needs to be replaced to ensure quality of results. Five homogenizers with different homogenization efficiency were used to homogenize pasteurized modified unhomogenized milks and farm raw bulk milks. Homogenized milks were collected from the homogenizer outlet and then run through an MIR milk analyzer without an in-line homogenizer to collect a MIR spectrum. A separate portion of each homogenized milk was analyzed with a laser light-scattering particle size analyzer to obtain reference values. The study was replicated 3 times with 3 independent sets of modified milks and bulk tank farm milks. Validation of the models was done with a set of 34 milks that were not used in the model development. Partial least square regression models were developed and validated for predicting the following milk fat globule particle size distribution parameters from MIR spectra: d(0.5) and d(0.9), surface volume mean diameter D[3,2], and volume moment mean diameter D[4,3]. The basis for the ability to model particle size distribution of milk fat emulsions was hypothesized to be the result of the partial least square modeling detecting absorbance shifts in MIR spectra of milk fat due to the Christiansen effect. The independent sample validation of particle size prediction methods found more variation in d(0.9) and D[4,3] predictions than the d(0.5) and D[3,2] predictions relative to laser light-scattering reference values, and this may be due to variation in particle size among different pump strokes. The accuracy of the d(0.9) prediction for routine quality assurance, to determine if a homogenizer within an MIR milk analyzer was near the failure level [i.e., d(0.9) >1.7µm] and needed to be replaced, is fit-for-purpose. The daily average particle size performance [i.e., d(0.9)] of a homogenizer based on the mean for the day could be used for monitoring homogenizer performance.

Prediction of fatty acid chain length and unsaturation of milk fat by mid-infrared milk analysis.

Our objective was to develop partial least squares (PLS) models to predict fatty acid chain length and total unsaturation of milk fat directly from a mid-infrared (MIR) spectra of milk at 40°C and then determine the feasibility of using those measures as correction factors to improve the accuracy of milk fat determination. A set of 268 milks (modified milks, farm bulk tank milks, and individual cow) were analyzed for fat, true protein, and anhydrous lactose with chemical reference methods, and in addition a MIR absorption spectra was collected for each milk. Fat was extracted from another portion of each milk, the fat was saponified to produce free fatty acids, and the free fatty acids were converted to methyl esters and quantified using gas-liquid chromatography. The PLS models for predicting the average chain length (carbons per fatty acid) and unsaturation (double bonds per fatty acid) of fatty acids in the fat portion of a milk sample from a MIR milk spectra were developed and validated. The validation performance of the prediction model for chain length and unsaturation had a relative standard deviation of 0.43 and 3.3%, respectively. These measures are unique in that they are fat concentration independent characteristics of fat structure that were predicted directly with transmission MIR analysis of milk. Next, the real-time data output from the MIR spectrophotometer for fatty acid chain length and unsaturation of milk were used to correct the fat A (C=O stretch) and fat B (C-H stretch) measures to improve accuracy of fat prediction. The accuracy validation was done over a period of 5 mo with 12 sets of 10 individual farm milks that were not a part of the PLS modeling population. The correction of a traditional fat B virtual filter result (C-H stretch) for sample-to-sample variation in unsaturation reduced the Euclidean distance for predicted fat from 0.034 to 0.025. The correction of a traditional fat A virtual filter result (C=O stretch) modified with additional information on sample-to-sample variation of chain length and unsaturation gave the largest improvement (reduced Euclidean distance from 0.072 to 0.016) and the best validation accuracy (i.e., lowest Euclidean distance) of all the fat prediction methods.

Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes

Mitigating the proportion of energy intake lost as methane could improve the sustainability and profitability of dairy production. As widespread measurement of methane emissions is precluded by current in vivo methods, the development of an easily measured proxy is desirable. An equation has been developed to predict methane from the mid-infrared (MIR) spectra of milk within routine milk-recording programs. The main goals of this study were to improve the prediction equation for methane emissions from milk MIR spectra and to illustrate its already available usefulness as a high throughput phenotypic screening tool. A total of 532 methane measurements considered as reference data (430 ± 129 g of methane/day) linked with milk MIR spectra were obtained from 165 cows using the SF6 technique. A first derivative was applied to the MIR spectra. Constant (P0), linear (P1) and quadratic (P2) modified Legendre polynomials were computed from each cows stage of lactation (days in milk), at the day of SF6 methane measurement. The calibration model was developed using a modified partial least-squares regression on first derivative MIR data points × P0, first derivative MIR data points × P1, and first derivative MIR data points × P2 as variables. The MIR-predicted methane emissions (g/day) showed a calibration coefficient of determination of 0.74, a cross-validation coefficient of determination of 0.70 and a standard error of calibration of 66 g/day. When applied to milk MIR spectra recorded in the Walloon Region of Belgium (≈2 000 000 records), this equation was useful to study lactational, annual, seasonal, and regional methane emissions. We conclude that milk MIR spectra has potential to be used to conduct high throughput screening of lactating dairy cattle for methane emissions. The data generated enable monitoring of methane emissions and production characteristics across and within herds. Milk MIR spectra could now be used for widespread screening of dairy herds in order to develop management and genetic selection tools to reduce methane emissions.

Machine learning algorithms for the prediction of conception success to a given insemination in lactating dairy cows

The ability to accurately predict the conception outcome for a future mating would be of considerable benefit for producers in deciding what mating plan (i.e., expensive semen or less expensive semen) to implement for a given cow. The objective of the present study was to use herd- and cow-level factors to predict the likelihood of conception success to a given insemination (i.e., conception outcome not including embryo loss); of particular interest in the present study was the usefulness of milk mid-infrared (MIR) spectral data in augmenting the accuracy of the prediction model. A total of 4,341 insemination records with conception outcome information from 2,874 lactations on 1,789 cows from 7 research herds for the years 2009 to 2014 were available. The data set was separated into a calibration data set and a validation data set using either of 2 approaches: (1) the calibration data set contained records from all 7 farms for the years 2009 to 2011, inclusive, and the validation data set included data from the 7 farms for the years 2012 to 2014, inclusive, or (2) the calibration data set contained records from 5 farms for all 6 yr and the validation data set contained information from the other 2 farms for all 6 yr. The prediction models were developed with 8 different machine learning algorithms in the calibration data set using standard 10-times 10-fold cross-validation and also by evaluating in the validation data set. The area under curve (AUC) for the receiver operating curve varied from 0.487 to 0.675 across the different algorithms and scenarios investigated. Logistic regression was generally the best-performing algorithm. The AUC was generally inferior for the external validation data sets compared with the calibration data sets. The inclusion of milk MIR in the prediction model generally did not improve the accuracy of prediction. Despite the fair AUC for predicting conception outcome under the different scenarios investigated, the model provided a reasonable prediction of the likelihood of conception success when the high predicted probability instances were considered; a conception rate of 85% was evident in the top 10% of inseminations ranked on predicted probability of conception success in the validation data set.

Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra

The main goal of this study was to develop, apply, and validate a new method to predict an indicator for CH4 eructed by dairy cows using milk mid-infrared (MIR) spectra. A novel feature of this model was the consideration of lactation stage to reflect changes in the metabolic status of the cow. A total of 446 daily CH4 measurements were obtained using the SF6 method on 142 Jersey, Holstein, and Holstein-Jersey cows. The corresponding milk samples were collected during these CH4 measurements and were analyzed using MIR spectroscopy. A first derivative was applied to the milk MIR spectra. To validate the novel calibration equation incorporating days in milk (DIM), 2 calibration processes were developed: the first was based only on CH4 measurements and milk MIR spectra (independent of lactation stage; ILS); the second included milk MIR spectra and DIM information (dependent on lactation stage; DLS) by using linear and quadratic modified Legendre polynomials. The coefficients of determination of ILS and DLS equations were 0.77 and 0.75, respectively, with standard error of calibration of 63g/d of CH4 for both calibration equations. These equations were applied to 1,674,763 milk MIR spectra from Holstein cows in the first 3 parities and between 5 and 365 DIM. The average CH4 indicators were 428, 444, and 448g/d by ILS and 444, 467, and 471g/d by DLS for cows in first, second, and third lactation, respectively. Behavior of the DLS indicator throughout the lactations was in agreement with the literature with values increasing between 0 and 100 DIM and decreasing thereafter. Conversely, the ILS indicator of CH4 emission decreased at the beginning of the lactation and increased until the end of the lactation, which differs from the literature. Therefore, the DLS indicator seems to better reflect biological processes that drive CH4 emissions than the ILS indicator. The ILS and DLS equations were applied to an independent data set, which included 59 respiration chamber measurements of CH4 obtained from animals of a different breed across a different production system. Results indicated that the DLS equation was much more robust than the ILS equation allowing development of indicators of CH4 emissions by dairy cows. Integration of DIM information into the prediction equation was found to be a good strategy to obtain biologically meaningful CH4 values from lactating cows by accounting for biological changes that occur throughout the lactation.

Standardization of milk mid-infrared spectra from a European dairy network

The goal of this study was to find a procedure to standardize dairy milk mid-infrared spectra from different Fourier transform mid-infrared spectrophotometers (different brands or models) inside a European dairy network to create new farm-management indicators (e.g., fertility, health, feed, environmental impact) based on milk infrared spectra. This step is necessary to create common spectral databases, allowing the building of statistical tools, to be used by all instruments of the network. The method used was piecewise direct standardization (PDS), which matches slave-instrument spectra on master-instrument spectra. To evaluate the possibility of using common equations on different instruments, the PDS method was tested on a set of milk samples measured on each machine, and an equation predicting fat content of milk is applied on all. Regressions were performed between master and slaves fat predictions, before and after PDS. Bias and root mean square error between predictions were decreased after PDS, respectively, from 0.3781 to 0.0000 and from 0.4609 to 0.0156 (g of fat/100mL of milk). The stability over time of these results was confirmed by an application of the coefficients created by PDS 1 mo later on the slave spectra. These preliminary results showed that the PDS method permits a reduction of the inherent spectral variability between instruments, allowing the merging of Fourier transform mid-infrared milk spectra from different instruments into a common database, the creation of new types of dairy farm management indicators, and the use of these common calibrations for all Fourier transform mid-infrared instruments of the European dairy network.

Effect of microbiological testing on subsequent mid-infrared milk component analysis of the same milk sample

Our objectives were to determine if mixing and sampling of a raw milk sample at 4°C for determination of total bacteria count (TBC) and if incubation at 14°C for 18h and sampling for a preliminary incubation (PI) count influenced the accuracy of subsequent fat, protein, or lactose measurement by mid-infrared (IR) analysis of milk from the same sample container due to either nonrepresentative sampling or the presence of microbial metabolites produced by microbial growth in the milk from the incubation. Milks of 4 fat levels (2.2, 3, 4, and 5%) reflected the range of fat levels encountered in producer milks. If the portion of milk removed from a cold sample was not representative, then the effect on a milk component test would likely be larger as fat content increases. Within the milks at each fat level, 3 treatments were used: (1) 20 vials of the same milk sampled for testing TBC using a BactoScan FC and then used for a milk component test; (2) 20 vials for testing TBC plus PI count followed by component test; and (3) 20 vials to run for IR component test without a prior micro sampling and testing. This was repeated in 3 different weeks using a different batch of milk each week. No large effect on the accuracy of component milk testing [IR fat B (carbon hydrogen stretch) and fat A (carbonyl stretch)] due to the cold milk sample handling and mixing procedures used for TBC was detected, confirming the fact that the physical removal of milk from the vial by the BactoScan FC (Foss Electric, Hillerød, Denmark) was a representative portion of the milk. However, the representativeness of any other sampling procedure (manual or automated) of a cold milk sample before running milk component testing on the same container of milk should be demonstrated and verified periodically as a matter of routine laboratory quality assurance. Running TBC with a BactoScan FC first and then IR milk analysis after had a minimal effect on milk component tests by IR when milk bacteria counts were within pasteurized milk ordinance limits of <100,000 cfu/mL. Running raw milk PI counts (18h of incubation at 13–14°C) with the BactoScan FC before milk component testing by IR milk analysis had an effect on component tests. The effect was largest on fat test results and would decrease the accuracy of milk payment testing on individual producer milks. The effect was most likely due to the absorption of light by bacterial metabolites resulting from microbial growth or other chemical degradation processes occurring in the milk during the PI count incubation, not by the sampling procedure of the BactoScan. The direction of the effect on component test results will vary depending on the bacteria count and the type of bacteria that grew in the milk, and this could be different in every individual producer milk sample.

Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows

Interest is increasing in the feed intake complex of individual dairy cows, both for management and animal breeding. However, energy intake data on an individual-cow basis are not routinely available. The objective of the present study was to quantify the ability of routinely undertaken mid-infrared (MIR) spectroscopy analysis of individual cow milk samples to predict individual cow energy intake and efficiency. Feed efficiency in the present study was described by residual feed intake (RFI), which is the difference between actual energy intake and energy used (e.g., milk production, maintenance, and body tissue anabolism) or supplied from body tissue mobilization. A total of 1,535 records for energy intake, RFI, and milk MIR spectral data were available from an Irish research herd across 36 different test days from 535 lactations on 378 cows. Partial least squares regression analyses were used to relate the milk MIR spectral data to either energy intake or efficiency. The coefficient of correlation (REX) of models to predict RFI across lactation ranged from 0.48 to 0.60 in an external validation data set; the predictive ability was, however, strongest (REX=0.65) in early lactation (<60 d in milk). The inclusion of milk yield as a predictor variable improved the accuracy of predicting energy intake across lactation (REX=0.70). The correlation between measured RFI and measured energy balance across lactation was 0.85, whereas the correlation between RFI and energy balance, both predicted from the MIR spectrum, was 0.65. Milk MIR spectral data are routinely generated for individual cows throughout lactation and, therefore, the prediction equations developed in the present study can be immediately (and retrospectively where MIR spectral data have been stored) applied to predict energy intake and efficiency to aid in management and breeding decisions.

Considering Human Milk Variability in the Nutritional Management of Low-Birth-Weight Infants

Human milk is the preferred feeding for babies but requires fortification to meet the needs of low-birth-weight infants. Human milk is known to vary with length of gestation and state of lactation. We describe the degree of variability both between mothers and within a mother’s own milk supply in the setting of the neonatal intensive care unit. Aliquots from 24-hour supplies of human milk from mothers of infants weighing &lt;2 kg at birth who were between 2 and 6 weeks of life were analyzed for lactose, bioavailable protein, and fat using a mid-infrared milk analyzer. Variability was estimated for each macronutrient by calculating the coefficient of variation (%) and was significantly less within one mother than between mothers ( P &lt; .05). The variability in macronutrient content between mothers supports the use of human milk analysis when establishing a nutritional management plan for low-birth-weight infants. Low variability within a mother’s own milk supply suggests that weekly analysis is sufficient to establish an individualized fortification plan.