Validation of fatty acid predictions in milk using mid-infrared spectrometry across cattle breeds.

in Animal (2013), 7

Relationship between milk composition estimated from mid infrared and methane emissions in dairy cows

Scientific conference (2012, December 03)

N/A

Mid-infrared predictions of fatty acids in bovine milk : final results of the RobustMilk project

Poster (2012, August 28)

The development of mid-infrared equations to predict the milk fatty acid (FA) content of milk allows prompt analysis of large numbers of samples and was one of the aims of the RobustMilk project. Data on ... [more ▼]

The development of mid-infrared equations to predict the milk fatty acid (FA) content of milk allows prompt analysis of large numbers of samples and was one of the aims of the RobustMilk project. Data on MIR spectra and FA from multiple countries, production systems, and breeds were used to develop equations to predict milk FA. The calibration set contained 1,776 spectrally different English, Irish, and Belgian milk samples collected for over 6 years. FA were quantified by gas chromatography (GC). Equations were built using partial least squares regression after a first derivative pretreatment applied to the spectral data. The robustness of the developed equations was assessed by cross-validation (CV) using 50 groups from the calibration set. The coefficient of determination (R²) obtained after CV ranged between 0.7101 for the total content of C18:2 and 0.9993 for the saturated FA group. The standard error of CV ranged between 0.0028 and 0.0998 g/dl of milk. Generally, the group or individual FA having the highest content in milk had the highest R²cv. The results obtained in this study confirmed the usefulness of MIR spectra to robustly quantify the FA content of milk permitting the use of these equations by milk laboratories in UK, Belgium or Ireland. Therefore, these equations could be used to develop selection or management tools for dairy farmers in order to improve the nutritional and environmental quality of milk based on the knowledge of the FA composition of their milk. [less ▲]

Mid-infrared prediction of milk titratable acidity and its genetic variability in first-parity cows

Conference (2012, August 27)

Coagulation of milkhas a direct effect on cheese yield. Among several parameters, titratable acidity of milk (TA) influences all the phases of milk coagulation. In order to study the genetic variability ... [more ▼]

Coagulation of milkhas a direct effect on cheese yield. Among several parameters, titratable acidity of milk (TA) influences all the phases of milk coagulation. In order to study the genetic variability of this trait on a large scale, mid-infrared (MIR) chemometric methods were used to predict TA. A total of 507 milk samples collected in the Walloon Region of Belgium from individual cows were analyzed using a MIR spectrometer. TA was recorded as Dornic degree. An equation to predict TA from milk MIR spectrum was developed using partial least squared regression after a first derivative pre-treatment applied to the spectra to correct the baseline drift. During the calibration process, 45 outliers were detected and removed from the calibration set. The TA mean of the final calibration set was 16.62 (standard deviation (SD) = 1.80). The coefficient of determination (R²) was 0.82 for the calibration with a standard error (SE) of 0.76. A cross-validation (cv) was performed (R²cv = 0.81 with SEcv = 0.80). This equation was then applied on the spectral database generated during the Walloon routine milk recording. The variances components were estimated by REML using single-trait random regression animal test-day model. The dataset used included 33,717 records from 9,191 Holstein first-parity cows; the TA mean was 17.05 (SD = 1.35) and TA ranged from 12.83 to 20.87. Estimated daily heritabilities ranged from 0.43 at 5th day in milk to 0.59 at 215th day in milk indicating potential of selection. Further research will study phenotypic and genetic correlations between TA and milk production traits. [less ▲]

Genetics and genomics of energy balance measured in milk using mid-infrared spectroscopy

Poster (2012, August)

Differences in bovine milk fat composition among dairy breeds in The Netherlands

Conference (2012, August)

Mid-infrared predictions of fatty acids in bovine milk : final results of the RobustMilk project

in Book of Abstracts of the 63rd Annual Meeting of the European Federation of Animal Science (2012, August)

The development of mid-infrared equations to predict the milk fatty acid (FA) content of milk allows prompt analysis of large numbers of samples and was one of the aims of the RobustMilk project. Data on ... [more ▼]

The development of mid-infrared equations to predict the milk fatty acid (FA) content of milk allows prompt analysis of large numbers of samples and was one of the aims of the RobustMilk project. Data on MIR spectra and FA from multiple countries, production systems, and breeds were used to develop equations to predict milk FA. The calibration set contained 1,776 spectrally different English, Irish, and Belgian milk samples collected for over 6 years. FA were quantified by gas chromatography (GC). Equations were built using partial least squares regression after a first derivative pretreatment applied to the spectral data. The robustness of the developed equations was assessed by cross-validation (CV) using 50 groups from the calibration set. The coefficient of determination (R²) obtained after CV ranged between 0.7101 for the total content of C18:2 and 0.9993 for the saturated FA group. The standard error of CV ranged between 0.0028 and 0.0998 g/dl of milk. Generally, the group or individual FA having the highest content in milk had the highest R²cv. The results obtained in this study confirmed the usefulness of MIR spectra to robustly quantify the FA content of milk permitting the use of these equations by milk laboratories in UK, Belgium or Ireland. Therefore, these equations could be used to develop selection or management tools for dairy farmers in order to improve the nutritional and environmental quality of milk based on the knowledge of the FA composition of their milk. [less ▲]

Mid-infrared prediction of milk titratable acidity and its genetic variability in first-parity cows

in Book of Abstracts of the 63rd Annual Meeting of the European Federation of Animal Science (2012, August)

Coagulation of milkhas a direct effect on cheese yield. Among several parameters, titratable acidity of milk (TA) influences all the phases of milk coagulation. In order to study the genetic variability ... [more ▼]

Coagulation of milkhas a direct effect on cheese yield. Among several parameters, titratable acidity of milk (TA) influences all the phases of milk coagulation. In order to study the genetic variability of this trait on a large scale, mid-infrared (MIR) chemometric methods were used to predict TA. A total of 507 milk samples collected in the Walloon Region of Belgium from individual cows were analyzed using a MIR spectrometer. TA was recorded as Dornic degree. An equation to predict TA from milk MIR spectrum was developed using partial least squared regression after a first derivative pre-treatment applied to the spectra to correct the baseline drift. During the calibration process, 45 outliers were detected and removed from the calibration set. The TA mean of the final calibration set was 16.62 (standard deviation (SD) = 1.80). The coefficient of determination (R²) was 0.82 for the calibration with a standard error (SE) of 0.76. A cross-validation (cv) was performed (R²cv = 0.81 with SEcv = 0.80). This equation was then applied on the spectral database generated during the Walloon routine milk recording. The variances components were estimated by REML using single-trait random regression animal test-day model. The dataset used included 33,717 records from 9,191 Holstein first-parity cows; the TA mean was 17.05 (SD = 1.35) and TA ranged from 12.83 to 20.87. Estimated daily heritabilities ranged from 0.43 at 5th day in milk to 0.59 at 215th day in milk indicating potential of selection. Further research will study phenotypic and genetic correlations between TA and milk production traits. [less ▲]

Genetic parameters for methane indicator traits based on milk fatty acids in cows

Conference (2012, July 18)

Effect of milk recording schemes on milk and milk components genetic parameters in Luxembourg

Conference (2012, July 18)

A particularity of the Luxembourg milk recording is the use of different schemes. Two principal schemes are applied: the scheme “S” applied on 69.1% of the total herds (n=712) and consisting in one ... [more ▼]

A particularity of the Luxembourg milk recording is the use of different schemes. Two principal schemes are applied: the scheme “S” applied on 69.1% of the total herds (n=712) and consisting in one proportionate sample of all daily milkings, and the scheme “T” (21.6% of the total herds) which consists in one sample of only one milking (morning or evening milking) (and alternating milking time from month to month). The problematic is that application of different schemes could influence the milk components (protein and fat yield) and the milk fat components (saturated and unsaturated groups of fatty acids) genetic parameters estimation and to prevent all comparisons between dairy population under different milk recording schemes. A total of 47,613 and 44,833 test-day records were obtained, respectively for schemes “S” and “T” from Holstein cows in first lactation in Luxembourg dairy herds. The used model included as fixed effects: herd x date of test, class of age, and month x year. Random effects were permanent environmental, additive genetics, and residual effects. The main objective of this work is to study the effect the choice of milk recording schemes (“S” or “T” schemes) on milk yield and milk components genetic parameters. A solution could be to add a fixed effect taking in account the milking time. The second objective is to study the effect of milking time (morning or evening) on genetic parameters estimated in the case of scheme “T”. According to the results, genetic parameters were statistically different between the schemes “S” and “T” for milk yield (P value < 0.0001). Further, the classifications of bulls according to their breeding values were very different when values were estimated on basis of scheme “S” or “T” (Spearman correlation value of 0.51 for milk yield for example). In conclusion, using several milk recording schemes do not allow any comparison of genetic parameters between dairy cattle’s. [less ▲]