References of "McParland, Sinead"
     in
Bookmark and Share    
Full Text
See detailA simple method to predict methane emissions based on milk mid infrared spectra
Vanlierde, Amélie ULiege; Dehareng, Frédéric; Froidmont, Eric et al

Poster (2016, October)

Detailed reference viewed: 18 (3 ULiège)
Full Text
Peer Reviewed
See detailMilk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes
Vanlierde, Amélie ULiege; Vanrobays, Marie-Laure ULiege; Gengler, Nicolas ULiege et al

in Animal Production Science (2016), 56(3), 258-264

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 12 (1 ULiège)
Full Text
Peer Reviewed
See detailHot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra
Vanlierde, Amélie ULiege; Vanrobays, Marie-Laure ULiege; Dehareng, Frédéric et al

in Journal of Dairy Science (2015), 98(8), 5740-5747

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 ... [more ▼]

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 63 g/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 448 g/d by ILS and 444, 467, and 471 g/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. [less ▲]

Detailed reference viewed: 138 (26 ULiège)
Full Text
See detailInnovative lactation stage specific prediction of CH4 from milk MIR spectra
Vanlierde, Amélie ULiege; Vanrobays, Marie-Laure ULiege; Dehareng, Frédéric et al

Conference (2014, August 28)

Detailed reference viewed: 85 (12 ULiège)
Full Text
See detailInnovative lactation stage specific prediction of CH4 from milk MIR spectra
Vanlierde, Amélie ULiege; Vanrobays, Marie-Laure ULiege; Dehareng, Frédéric et al

in Book of abstracts of the 65th annual meeting of the European Federation of Animal Science (2014, August 26)

Previous research has shown that CH4 emissions of dairy cows are linked to milk composition and particularly to fatty acids (FA). We showed that mid-infrared (MIR) prediction equations can be used to ... [more ▼]

Previous research has shown that CH4 emissions of dairy cows are linked to milk composition and particularly to fatty acids (FA). We showed that mid-infrared (MIR) prediction equations can be used to obtain individual enteric CH4 emissions from the milk MIR spectra. However body tissue mobilisation alters milk FA and potentially links between CH4 and MIR spectra. Therefore to reflect the expected metabolic status during lactation, a method was developed to consider days in milk (DIM) in the MIR based prediction equation. A total of 446 CH4 reference data were obtained using the SF6 method on 146 Jersey, Holstein and Holstein-Jersey cows. Linear (P1) and quadratic (P2) Legendre polynomials were computed from DIM of CH4 measurements. A first derivative was applied to the MIR spectra. The calibration model was developed using as independent variables first derivative, first derivative × P1, first derivative × P2 and a modified PLS regression. The CH4 emission prediction (g CH4/day) showed a calibration coefficient of determination (R2c) of 0.75, a cross-validation coefficient of determination (R2cv) of 0.67 and the standard error of calibration (SEC) was 63 g/day. In order to check if this new equation showed an expected and biological meaningful behavior, it was applied to the milk MIR spectra database of the Walloon Region of Belgium (1,804,476 records). The resulting trend across lactation was similar to what was expected, with increasing averaged CH4 up to DIM 83 and a slight decrease after. This pattern was a clear improvement when compared to predictions from previous equations. Results indicate that this innovative approach with integration of DIM information could be a good strategy to improve the equation by taking better account of the metabolism of the cows. [less ▲]

Detailed reference viewed: 25 (7 ULiège)
Full Text
See detailMid-infrared predictions of fatty acids in bovine milk : final results of the RobustMilk project
Soyeurt, Hélène ULiege; McParland, Sinead; Berry, Donagh et al

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 ▲]

Detailed reference viewed: 60 (10 ULiège)
Full Text
See detailGenetics and genomics of energy balance measured in milk using mid-infrared spectroscopy
McParland, Sinead; Calus, Mario; Coffey, Mike et al

Poster (2012, August)

Detailed reference viewed: 59 (6 ULiège)
Full Text
See detailGenetics and genomics of energy balance measured in milk using mid-infrared spectroscopy
McParland, Sinead; Calus, Mario; Coffey, Mike et al

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

Detailed reference viewed: 24 (3 ULiège)
Full Text
See detailMid-infrared predictions of fatty acids in bovine milk : final results of the RobustMilk project
Soyeurt, Hélène ULiege; McParland, Sinead; Berry, Donagh et al

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 ▲]

Detailed reference viewed: 98 (8 ULiège)
Full Text
Peer Reviewed
See detailMid-Infrared prediction of lactoferrin content in bovine milk: Potential indicator of mastitis
Soyeurt, Hélène ULiege; Bastin, Catherine; Colinet, Frédéric ULiege et al

in Animal (2012), 6

Detailed reference viewed: 167 (59 ULiège)
Full Text
Peer Reviewed
See detailMid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries
Soyeurt, Hélène ULiege; Dehareng, F.; Gengler, Nicolas ULiege et al

in Journal of Dairy Science (2011), 94(4), 1657-1667

Increasing consumer concern exists over the relationship between food composition and human health. Because of the known effects of fatty acids on human health, the development of a quick, inexpensive ... [more ▼]

Increasing consumer concern exists over the relationship between food composition and human health. Because of the known effects of fatty acids on human health, the development of a quick, inexpensive, and accurate method to directly quantify the fatty acid (FA) composition in milk would be valuable for milk processors to develop a payment system for milk pertinent to their customer requirements and for farmers to adapt their feeding systems and breeding strategies accordingly. The aim of this study was (1) to confirm the ability of mid-infrared spectrometry (MIR) to quantify individual FA content in milk by using an innovative procedure of sampling (i.e., samples were collected from cows belonging to different breeds, different countries, and in different production systems); (2) to compare 6 mathematical methods to develop robust calibration equations for predicting the contents of individual FA in milk; and (3) to test interest in using the FA equations developed in milk as basis to predict FA content in fat without corrections for the slope and the bias of the developed equations. In total, 517 samples selected based on their spectral variability in 3 countries (Belgium, Ireland, and United Kingdom) from various breeds, cows, and production systems were analyzed by gas chromatography (GC). The samples presenting the largest spectral variability were used to calibrate the prediction of FA by MIR. The remaining samples were used to externally validate the 28 FA equations developed. The 6 methods were (1) partial least squares regression (PLS); (2) PLS + repeatability file (REP); (3) first derivative of spectral data + PLS; (4) first derivative + REP + PLS; (5) second derivative of spectral data + PLS; and (6) second derivative + REP + PLS. Methods were compared on the basis of the crossvalidation coefficient of determination (R2cv), the ratio of standard deviation of GC values to the standard error of cross-validation (RPD), and the validation coefficient of determination (R2v). The third and fourth methods had, on average, the highest R2cv, RPD, and R2v. The final equations were built using all GC and the best accuracy was observed for the infrared predictions of C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C16:0, C18:0, C18:1 trans, C18:1 cis-9, C18:1 cis, and for some groups of FA studied in milk (saturated, monounsaturated, unsaturated, short-chain, medium-chain, and long-chain FA). These equations showed R2cv greater than 0.95. With R2cv equal to 0.85, the MIR prediction of polyunsaturated FA could be used to screen the cow population. As previously published, infrared predictions of FA in fat are less accurate than those developed from FA content in milk (g/dL of milk) and no better results were obtained by using milk FA predictions if no corrections for bias and slope based on reference milk samples with known contents of FA were used. These results indicate the usefulness of equations with R2cv greater than 95% in milk payment systems and the usefulness of equations with R2cv greater than 75% for animal breeding purposes. [less ▲]

Detailed reference viewed: 104 (43 ULiège)
Full Text
See detailMid-infrared predictions of lactoferrin content in bovine milk
Soyeurt, Hélène ULiege; Bastin, Catherine ULiege; Colinet, Frédéric ULiege et al

in Journal of Dairy Science (2011), 94(E-suppl.1), 714

Lactoferrin (LF) is a glycoprotein present in milk and active in the immune system of cows and humans. Therefore, an inexpensive and rapid analysis to quantify this protein is desirable. A previous study ... [more ▼]

Lactoferrin (LF) is a glycoprotein present in milk and active in the immune system of cows and humans. Therefore, an inexpensive and rapid analysis to quantify this protein is desirable. A previous study reported the potential to quantify LF from the mid-infrared (MIR) spectrometry from 69 milk samples. Through the European RobustMilk project (www.robustmilk.eu), 3,606 milk samples were collected in Belgium, Ireland, and Scotland from individual cows and analyzed using a MIR MilkoScanFT6000 spectrometer. Milk LF content was quantified using ELISA in duplicate. Average ELISA data with a CV lower than 5% were used. After the detection of spectral and ELISA outliers, the calibration set contained 2,499 samples. An equation to predict LF content from MIR was developed using partial least squared regression. A first derivative pre-treatment of spectra was used to correct the baseline drift. To improve the repeatability of the spectral data, a file which contained the spectra of samples analyzed on 5 spectrometers was used during the calibration. The lactoferrin mean was 159.28 mg/l of milk with a SD of 97.21 mg/l of milk. The calibration (C) coefficient of determination (R2) was equal to 0.73 with a standard error (SE) of calibration of 50.54 mg/l of milk. A cross-validation (CV) was used to assess the robustness of the equation. R2 CV was 0.72 with a SE-CV of 51.16 mg/l of milk. An external validation (V) was conducted on 150 milk samples collected in Belgium. The SE of prediction (SEP) was 59.17 mg/L of milk. The similarity between R2 C and R2CV as well as between SE-C and SE-CV and between SE-CV and SEP confirms the equations developed are robust. The correlation between predicted and measured LF values was 0.71. This lower value compared with the one obtained from the calibration set (0.85) could be explained by the low ELISA reproducibility (16.24% ± 25.51%). If the developed equation is used to clean the validation data set, a total of 16 samples can be deleted. The validation coefficient for these 134 samples increased to 0.82. From these results, the developed equation could be used for screening the dairy cow population for breeding purposes. [less ▲]

Detailed reference viewed: 88 (21 ULiège)
Full Text
Peer Reviewed
See detailThe use of mid-infrared spectrometry to predict body energy status of Holstein cows
McParland, Sinead; Banos, Giorgios; Wall, Eileen et al

in Journal of Dairy Science (2011), 94

Detailed reference viewed: 39 (3 ULiège)
Full Text
Peer Reviewed
See detailImprovements and validation of mid-infrared predictions of milk fatty acid
Soyeurt, Hélène ULiege; McParland, Sinead; Donagh, Berry et al

Conference (2010, July)

The development of mid-infrared equations to predict the milk fatty acid (FA) content of milk allows prompt analysis of large numbers of samples. The first aim was to improve these predictions by ... [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. The first aim was to improve these predictions by comparing 6 statistical approaches. The second one was to validate the new equations using an independent sample set. The calibration set contained 239 spectrally different Belgian milk samples collected for over 2 years from several cows and breeds. FA were quantified by gas chromatography (GC). Statistical approaches tested were 1) partial least squares regression (PLS), 2) PLS and first derivative, 3) PLS and repeatability file (RF), 4) PLS, first derivative and RF, 5) PLS, second derivative, and 6) PLS, second derivative and RF. This last file contained spectra obtained from the same samples using 5 spectrometers. Cross-validation (CV) used 20 groups from the calibration set. Methods were compared using the ratio of the standard deviation of GC values to the standard error of CV (RPD). An external validation permitted a second comparison and was done using 362 samples collected for one year from multiple breeds and cows in Belgium, Ireland, and Scotland. Different RPD values were obtained by the 6 methods. Generally the equations developed using method 4 gave better results suggesting the adaptation of the methodology to the studied FA. It confirms by the obtained validation coefficients of determination. Highest values were observed for the equations with the highest RPD values except for C18:0. The ability to predict FA using method 4 gave superior results to those shown in previous publications. [less ▲]

Detailed reference viewed: 78 (9 ULiège)
Peer Reviewed
See detailThe influence of genetic selection on the milk fatty acid profile of spring calving dairy cows
McParland, Sinead; Berry, Donagh; Coleman, J. et al

in Proceedings of the British Society of Animal Science and the Agricultural Research Forum (2010)

Detailed reference viewed: 16 (1 ULiège)
Full Text
Peer Reviewed
See detailImprovement and validation of milk fatty acid predictions using mid-infrared spectrometry
Soyeurt, Hélène ULiege; McParland, Sinead; Berry, Donagh et al

in Proceedings of the Bristish Society of Animal Science and the Agricultural Research Forum (2010)

Detailed reference viewed: 37 (6 ULiège)