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Søren Østergaard

Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach

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Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach. / Grelet, C.; Vanlierde, A.; Hostens, M.; Foldager, L.; Salavati, M.; Ingvartsen, Klaus Lønne; Crowe, M.; Sorensen, Martin Tang; Froidmont, E.; Ferris, C. P.; Marchitelli, C.; Becker, F.; Larsen, T.; Carter, F.; GplusE Consortium; Dehareng, F.

I: Animal, Bind 13, Nr. 3, 03.2019, s. 649-658.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Grelet, C, Vanlierde, A, Hostens, M, Foldager, L, Salavati, M, Ingvartsen, KL, Crowe, M, Sorensen, MT, Froidmont, E, Ferris, CP, Marchitelli, C, Becker, F, Larsen, T, Carter, F, GplusE Consortium & Dehareng, F 2019, 'Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach', Animal, bind 13, nr. 3, s. 649-658. https://doi.org/10.1017/S1751731118001751

APA

Grelet, C., Vanlierde, A., Hostens, M., Foldager, L., Salavati, M., Ingvartsen, K. L., Crowe, M., Sorensen, M. T., Froidmont, E., Ferris, C. P., Marchitelli, C., Becker, F., Larsen, T., Carter, F., GplusE Consortium, & Dehareng, F. (2019). Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach. Animal, 13(3), 649-658. https://doi.org/10.1017/S1751731118001751

CBE

Grelet C, Vanlierde A, Hostens M, Foldager L, Salavati M, Ingvartsen KL, Crowe M, Sorensen MT, Froidmont E, Ferris CP, Marchitelli C, Becker F, Larsen T, Carter F, GplusE Consortium, Dehareng F. 2019. Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach. Animal. 13(3):649-658. https://doi.org/10.1017/S1751731118001751

MLA

Vancouver

Author

Grelet, C. ; Vanlierde, A. ; Hostens, M. ; Foldager, L. ; Salavati, M. ; Ingvartsen, Klaus Lønne ; Crowe, M. ; Sorensen, Martin Tang ; Froidmont, E. ; Ferris, C. P. ; Marchitelli, C. ; Becker, F. ; Larsen, T. ; Carter, F. ; GplusE Consortium ; Dehareng, F. / Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach. I: Animal. 2019 ; Bind 13, Nr. 3. s. 649-658.

Bibtex

@article{4bbcca1486d54c07aca65385f7280328,
title = "Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach",
abstract = "Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.",
keywords = "biomarker, dairy cattle, Fourier transform mid-IR spectrometry, metabolic clustering, prediction, BETA-HYDROXYBUTYRATE, NEGATIVE-ENERGY BALANCE, MIDINFRARED SPECTRA, HOLSTEIN, SUBCLINICAL KETOSIS, DISEASES, GENE-EXPRESSION, UTERINE HEALTH, PHYSIOLOGICAL IMBALANCE, DAIRY-CATTLE, Fatty Acids, Nonesterified/blood, Insulin-Like Growth Factor I/metabolism, Spectroscopy, Fourier Transform Infrared/methods, Cattle, Female, Milk, Blood Glucose/metabolism, Animal Husbandry/methods, Animals, Energy Metabolism, Blood Chemical Analysis/veterinary, Cluster Analysis",
author = "C. Grelet and A. Vanlierde and M. Hostens and L. Foldager and M. Salavati and Ingvartsen, {Klaus L{\o}nne} and M. Crowe and Sorensen, {Martin Tang} and E. Froidmont and Ferris, {C. P.} and C. Marchitelli and F. Becker and T. Larsen and F. Carter and {GplusE Consortium} and F. Dehareng and Krogh, {Mogens Agerbo} and Tine Rousing and Jehan Ettema and S{\o}ren {\O}stergaard",
year = "2019",
month = mar,
doi = "10.1017/S1751731118001751",
language = "English",
volume = "13",
pages = "649--658",
journal = "Animal",
issn = "1751-7311",
publisher = "Cambridge University Press",
number = "3",

}

RIS

TY - JOUR

T1 - Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach

AU - Grelet, C.

AU - Vanlierde, A.

AU - Hostens, M.

AU - Foldager, L.

AU - Salavati, M.

AU - Ingvartsen, Klaus Lønne

AU - Crowe, M.

AU - Sorensen, Martin Tang

AU - Froidmont, E.

AU - Ferris, C. P.

AU - Marchitelli, C.

AU - Becker, F.

AU - Larsen, T.

AU - Carter, F.

AU - GplusE Consortium

AU - Dehareng, F.

AU - Krogh, Mogens Agerbo

AU - Rousing, Tine

AU - Ettema, Jehan

AU - Østergaard, Søren

PY - 2019/3

Y1 - 2019/3

N2 - Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

AB - Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

KW - biomarker

KW - dairy cattle

KW - Fourier transform mid-IR spectrometry

KW - metabolic clustering

KW - prediction

KW - BETA-HYDROXYBUTYRATE

KW - NEGATIVE-ENERGY BALANCE

KW - MIDINFRARED SPECTRA

KW - HOLSTEIN

KW - SUBCLINICAL KETOSIS

KW - DISEASES

KW - GENE-EXPRESSION

KW - UTERINE HEALTH

KW - PHYSIOLOGICAL IMBALANCE

KW - DAIRY-CATTLE

KW - Fatty Acids, Nonesterified/blood

KW - Insulin-Like Growth Factor I/metabolism

KW - Spectroscopy, Fourier Transform Infrared/methods

KW - Cattle

KW - Female

KW - Milk

KW - Blood Glucose/metabolism

KW - Animal Husbandry/methods

KW - Animals

KW - Energy Metabolism

KW - Blood Chemical Analysis/veterinary

KW - Cluster Analysis

UR - http://www.scopus.com/inward/record.url?scp=85049846553&partnerID=8YFLogxK

U2 - 10.1017/S1751731118001751

DO - 10.1017/S1751731118001751

M3 - Journal article

C2 - 29987991

AN - SCOPUS:85049846553

VL - 13

SP - 649

EP - 658

JO - Animal

JF - Animal

SN - 1751-7311

IS - 3

ER -