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See detailNovel innovative possibilities for the dairy industry opened by common format of FT-MIR instruments
Grelet, Clément ULiege; Fernandez Pierna, Juan Antonio; Dardenne, Pierre et al

Poster (2016, October 17)

FT-MIR technology is worldwide used for fast and cost effective determination of major milk components. However, due to the different individual response of each instrument the potential of this ... [more ▼]

FT-MIR technology is worldwide used for fast and cost effective determination of major milk components. However, due to the different individual response of each instrument the potential of this technology is currently underexploited as new tools cannot be easily ported to other instruments. Recently a standardization method was developed in order to harmonize the spectral response format between instruments of different brands and models but also across time for each spectrometer. The method matches monthly the infrared response of all spectrometers on the response of a reference instrument, making all machines talking a common language. The objective is to allow the creation and the use of common, new and innovative concepts by pooling resources and sharing data. Using this method, new tools for analysis of milk quality and milk technological properties have been created and shared within the network, as fatty acids and minerals predictions or milk coagulation properties. New concepts requiring a common spectral format have been developed like the untargeted detection of milk contaminant and abnormal milk or the determination of milk geographic origin. Models in relation with the status of the dairy cow were also created and shared as to predict ketosis, negative energy balance or methane emissions. Therefore models can be developed at one place and deployed within the entire network, in which 90 instruments are currently monthly standardized. [less ▲]

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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)

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See detailOptiMIR: Use of MIR spectra to predict multiple cow status as advisory tools for dairy farms
Grelet, Clément ULiege; Gengler, Nicolas ULiege; Bastin, Catherine et al

in Book of Abstracts of the 67th Annual Meeting of the European Federation of Animal Science (2016, August)

Considering the current increasing of herd size, there is a need for precise and rapid information on individual cow state. Mid infrared (MIR) technology is already used worldwide for milk analysis; it ... [more ▼]

Considering the current increasing of herd size, there is a need for precise and rapid information on individual cow state. Mid infrared (MIR) technology is already used worldwide for milk analysis; it allows rapid and cost effective determination of milk composition. The objective of OptiMIR project was to optimize the use of MIR spectra in order to produce indications on cow status thereby providing advisory tools to dairy farmers. Hence phenotypes of interest were collected in several countries and linked to MIR spectra. Since the OptiMIR network comprised 65 MIR instruments in 6 countries, standardisation of MIR data was necessary, allowing the collation of spectral databases and the use by all milk recording organizations (MRO) of the models developed. Using chemometric tools (like PLS regression), predictive models were developed to provide indicators on fine milk composition, on milk biomarkers of physiological imbalance, and directly on status of the cows. Equations predicting fine milk composition such as fatty acids and minerals were consolidated through the OptiMIR network, providing indirectly information on technological properties of milk and cow status. As biomarker of early physiological imbalance, an equation predicting citrate in milk was developed with good accuracy (R²cv=0.86); and as milk biomarkers of ketosis, BHB and acetone were calibrated with fair results (R²cv=0.63 and 0.67 respectively). Direct classification of spectra regarding low vs high risk of ketosis was also performed (84.5% sensitivity and 84.2% specificity). Direct regressions were realized for various negative energy balance criteria (r from 0.43 to 0.57) and enteric methane (R²cv=0.7). All equations are available to be used by MRO on field and converted into advisory tools for the dairy sector. [less ▲]

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See detailCapitalizing on fine milk composition for breeding and management of dairy cows
Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege; Dehareng, Fréderic et al

in Journal of Dairy Science (2016), 99(5), 4071-4079

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of ... [more ▼]

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, and their health, behavior, and well-being as well as their environmental impact and the quality of their products (i.e., milk and subsequently dairy products). Milk composition has been identified as an important source of information because it could reflect, at least partially, all these elements. Major conventional milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other nonconventional milk components, potentially predicted by MIR, might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust, and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using techniques based on classical prediction equations. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate) that can be then useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., methane, energy balance, ketosis) are predicted directly from MIR spectra. In a second approach, research is ongoing to detect and exploit patterns in an innovative manner, by comparing observed with expected MIR spectra directly (e.g., pregnancy). All of these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality, and mitigate environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlations with other traits of interest. [less ▲]

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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 ▲]

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See detailChemometrics and vibrational spectroscopy for the detection of melamine levels in milk
Fernandez Pierna, Juan; Vincke, Damien; Baeten, Vincent et al

Poster (2016, January 19)

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See detailApplication of a standardisation procedure on an international network of MIR instruments for milk analysis
Grelet, Clément ULiege; Fernandez Pierna, Juan A.; Dardenne, Pierre et al

Poster (2016, January)

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See detailUse of a multivariate moving window PCA for the untargeted detection of contaminants in agro-food products, as exemplified by the detection of melamine levels in milk using vibrational spectroscopy.
Fernandez Pierna, Juan A; Vincke, Damien; Baeten, Vincent et al

in Chemometrics and Intelligent Laboratory Systems (2016)

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See detail5. Froment 2015: Enfin une récolte sereine
Sinnaeve, Georges; Gofflot, S.; Chandelier, Anne et al

in Bodson, Bernard; Watillon, Bernard (Eds.) Livre Blanc Céréales (2015, September 10)

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See detailLivre Blanc Céréales
Sinnaeve, Georges; Gofflot, S.; Chandelier, anne et al

in Bodson, Bernard; Watillon, Bernard (Eds.) Livre Blanc Céréales (2015, September 10)

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See detailMilk biomarkers to detect ketosis and negative energy balance using MIR spectrometry
Grelet, Clément ULiege; Bastin, Catherine ULiege; Gelé, Marine et al

in Book of Abstracts of the 66th Annual Meeting of the European Federation of Animal Science (2015, September 02)

In order to manage negative energy balance and ketosis in dairy farms, rapid and cost-effective detection is needed. Among the milk biomarkers, citrate was recently identified as an early indicator of ... [more ▼]

In order to manage negative energy balance and ketosis in dairy farms, rapid and cost-effective detection is needed. Among the milk biomarkers, citrate was recently identified as an early indicator of negative energy balance and acetone and BHB are of particular interest regarding ketosis. The objective of this study was to evaluate the ability of Mid-Infrared (MIR) spectrometry to predict these biomarkers as this technology can routinely provide rapid and cost-effective predictions. A total of 566 milk samples were collected in commercial and experimental farms in Luxembourg, France and Germany. Acetone, BHB, and citrate contents were determined by flow injection analysis. Milk MIR spectra were recorded and standardized for all samples. Acetone content ranged from 20 to 3,355 μmol/l with an average of 103 μmol/l; BHB content ranged from 21.3 to 1,595.6 μmol/l with an average of 215.4 μmol/l; and citrate content ranged from 4.5 to 15.5 mmol/l with an average of 8.9 mmol/l. Acetone and BHB contents were log-transformed to approach a normal distribution. Prediction equations were developed using PLS. The R2 of calibration was 0.73 for acetone, 0.75 for BHB and 0.90 for citrate with RMSE (root mean square error) of 87.7 μmol/l, 86.5 μmol/l and 0.75 mmol/l respectively. An external validation was performed and RMSE of validation was 45.2 μmol/l for acetone, 65.33 μmol/l for BHB and 0.80 mmol/l for citrates. Although the practical usefulness of the equations developed should be verified with field data, results from this study demonstrated the potential of MIR spectrometry to predict citrate content with good accuracy and to supply indicative contents of BHB and acetone in milk, providing consequently detection tools of ketosis and negative energy balance in dairy farms. [less ▲]

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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 ▲]

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See detailGenetic variability of MIR predicted milk technological properties in Walloon dairy cattle
Colinet, Frédéric ULiege; Troch, Thibault ULiege; Baeten, Vincent et al

in Book of Abstracts of the 66th Annual Meeting of the European Federation of Animal Science (2015, August)

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See detailPotential of visible-near infrared spectroscopy for the characterization of butter properties
Troch, Thibault ULiege; Baeten, Vincent; Dehareng, Frédéric et al

in Book of Abstracts of the 66th Annual Meeting of the European Federation of Animal Science (2015, August)

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See detailOverview of possibilities and challenges of the use of infrared spectrometry in cattle breeding
Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege; Dehareng, Frédéric et al

in Book of Abstracts of the 66th Annual Meeting of the European Federation of Animal Science (2015, August)

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See detailComparison of 3 different variable selection strategies to improve the predictions of fatty acid profile in bovine milk by mid-infrared spectrometry
Soyeurt, Hélène ULiege; Brostaux, Yves ULiege; Dehareng, Frédéric et al

in Journal of Animal Science (2015, July 15), 93/98(Suppl. s3/ Suppl. 2), 804

Mid-infrared (MIR) spectrometry is used to provide phenotypes related to the milk composition. Foss spectrum contains 1,060 datapoints. The number of reference values required to build a calibration ... [more ▼]

Mid-infrared (MIR) spectrometry is used to provide phenotypes related to the milk composition. Foss spectrum contains 1,060 datapoints. The number of reference values required to build a calibration equation is often lower than the spectral variables mainly due to the cost of chemical analysis. Problems of collinearity and overfitting appear when this high dimensional data set is used. This research will study the interest of using variable selection (VS) approach before the use of partial least square regression (PLS). The data set included 1,236 milk spectra related to their fatty acid (FA) contents. Saturated (SFA), monounsaturated (MUFA), polyunsaturated (PUFA), short chain (SCFA), medium chain (MCFA), and long chain FA (LCFA) were studied. The data set was randomly divided in 3 groups which were used to create 3 calibration and validation data sets. Three different VS methods were compared. The first strategy was based on the part of trait variability explained by each considered variables (R2VS). The second method was based on the regression coefficient estimated after PLS procedure divided by the standard deviation of the considered spectral variable (BSVS). The third strategy permitted to underline the uninformative variables which were the ones having the lowest ratio of average regression coefficient to their corresponding standard deviation estimated after a leave-one out cross-validation (UVEVS). For UVEVS and BSVS, the cutoff was determined from the known uninformative region of MIR milk spectrum. The cutoff for R2VS was determined by testing different thresholds ranged between 5 and 40%. The most interesting cutoff for R2VS was 25%. The worst results in terms of validation root mean square error of prediction (RMSEPv) were obtained using a full PLS (i.e., without VS). The maximum difference (g/dl of milk) of RMSEPv obtained from the full PLS and from the PLS using selected variables were 0.156 for SFA, 0.139 for MUFA, 0.011 for PUFA, 0.025 for SCFA, 0.164 for MCFA, and 0.188 for LCFA. R2VS gave the best results for all studied traits followed by UVEVS and then BSVS. In conclusion, the use of VS improved significantly the performance of FA MIR equations. [less ▲]

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See detailCapitalizing on fine milk composition for breeding and management of dairy cows
Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege; Dehareng, Frédéric et al

in Journal of Animal Science (2015, July 12), 93/ 98(Suppl. s3/ Suppl. 2), 4

Management and breeding of dairy cows face the challenge of permanently adapting to changing production circumstances under socioeconomic constraints. If management and breeding addresses different ... [more ▼]

Management and breeding of dairy cows face the challenge of permanently adapting to changing production circumstances under socioeconomic constraints. If management and breeding addresses different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, their products (i.e., milk and subsequently dairy products), their behavior and their environmental impact. Milk composition has been identified as an important source of information since it could reflect, at least partially, all these elements. Major milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other components might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using classical prediction equation based techniques. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate), that can then be useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., ketosis) are predicted directly from MIR spectra. In a second approach, in an innovative manner, patterns detected by comparing observed from expected MIR spectra can be used directly. All these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality and limit environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlation with other traits of interest. [less ▲]

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See detailQuality Assurance for new analytical parameters, Optimir standardisation of MIR instruments
Grelet, Clément ULiege; Fernandez Pierna, Juan; Dardenne, Pierre et al

Conference (2015, June 10)

Detailed reference viewed: 16 (2 ULiège)