Multiple Breeds and Countries’ Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry

Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples...

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Published inFoods Vol. 10; no. 9; p. 2235
Main Authors Christophe, Octave S., Grelet, Clément, Bertozzi, Carlo, Veselko, Didier, Lecomte, Christophe, Höeckels, Peter, Werner, Andreas, Auer, Franz-Josef, Gengler, Nicolas, Dehareng, Frédéric, Soyeurt, Hélène
Format Journal Article Web Resource
LanguageEnglish
Published Switzerland MDPI AG 21.09.2021
MDPI
Subjects
Analytical chemistry
Animal production & animal husbandry
Austria
Calibration
dairy herds
Dairy industry
Datasets
Dietary minerals
Emission analysis
Environmental impact
Food Science
Health (social science)
Health Professions (miscellaneous)
herd improvement
Hypertension
Inductively coupled plasma
Infrared spectra
Infrared spectroscopy
Lactation
least squares
Life sciences
Microbiology
mid-infrared
Milk
milk composition
Mineral composition
mineral content
Minerals
Plant Science
Potassium
prediction
Predictions
Productions animales & zootechnie
Sciences des denrées alimentaires
Sciences du vivant
Scientific imaging
Sodium
Spectrometry
spectroscopy
Austria
Belgium
France
United States > US
Luxembourg
Germany
milk
minerals
mid-infrared
Online AccessGet full text
ISSN2304-8158
2304-8158
DOI10.3390/foods10092235

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Abstract Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents. Models were built from records coming from four countries (n = 1181) and validated using records from the fifth country, Austria (n = 100). The importance of including local samples was tested by integrating 30 Austrian samples in the model while validating with the remaining 70 samples. The best performances were achieved using this second set of models, confirming the need to cover the spectral variability of a country before making a prediction. Validation root mean square errors were 54.56, 63.60, 7.30, 59.87, and 152.89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals’ variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons.
AbstractList Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents. Models were built from records coming from four countries ( = 1181) and validated using records from the fifth country, Austria ( = 100). The importance of including local samples was tested by integrating 30 Austrian samples in the model while validating with the remaining 70 samples. The best performances were achieved using this second set of models, confirming the need to cover the spectral variability of a country before making a prediction. Validation root mean square errors were 54.56, 63.60, 7.30, 59.87, and 152.89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals' variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons.
Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents. Models were built from records coming from four countries (n = 1181) and validated using records from the fifth country, Austria (n = 100). The importance of including local samples was tested by integrating 30 Austrian samples in the model while validating with the remaining 70 samples. The best performances were achieved using this second set of models, confirming the need to cover the spectral variability of a country before making a prediction. Validation root mean square errors were 54.56, 63.60, 7.30, 59.87, and 152.89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals’ variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons.
Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents. Models were built from records coming from four countries ( n = 1181) and validated using records from the fifth country, Austria ( n = 100). The importance of including local samples was tested by integrating 30 Austrian samples in the model while validating with the remaining 70 samples. The best performances were achieved using this second set of models, confirming the need to cover the spectral variability of a country before making a prediction. Validation root mean square errors were 54.56, 63.60, 7.30, 59.87, and 152.89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals’ variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons.
Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents. Models were built from records coming from four countries (n = 1181) and validated using records from the fifth country, Austria (n = 100). The importance of including local samples was tested by integrating 30 Austrian samples in the model while validating with the remaining 70 samples. The best performances were achieved using this second set of models, confirming the need to cover the spectral variability of a country before making a prediction. Validation root mean square errors were 54.56, 63.60, 7.30, 59.87, and 152.89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals' variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons.Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents. Models were built from records coming from four countries (n = 1181) and validated using records from the fifth country, Austria (n = 100). The importance of including local samples was tested by integrating 30 Austrian samples in the model while validating with the remaining 70 samples. The best performances were achieved using this second set of models, confirming the need to cover the spectral variability of a country before making a prediction. Validation root mean square errors were 54.56, 63.60, 7.30, 59.87, and 152.89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals' variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons.
Author Dehareng, Frédéric
Bertozzi, Carlo
Lecomte, Christophe
Grelet, Clément
Werner, Andreas
Veselko, Didier
Christophe, Octave S.
Soyeurt, Hélène
Gengler, Nicolas
Auer, Franz-Josef
Höeckels, Peter
AuthorAffiliation 1 Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium; o.christophe@cra.wallonie.be (O.S.C.); c.grelet@cra.wallonie.be (C.G.)
5 Landeskontrollverband Nordrhein-Westfalen e.V., Bischofstraße 85, 47809 Krefeld, Germany; hoeckels@lkv-nrw.de
2 Elevéo Asbl, AWE Group, 4, Rue des Champs Elysées, 5590 Ciney, Belgium; cbertozzi@awenet.be
4 France Conseil Elevage, Maison du Lait, 42 Rue de Chateaudun, 75009 Paris, France; christophe.lecomte@france-conseil-elevage.fr
6 LKV Baden Württemberg, Heinrich-Baumann Str. 1-3, 70190 Stuttgart, Germany; awerner@lkvbw.de
8 Gembloux Agro-Bio Tech, TERRA Teaching and Research Centre, University of Liège, 5030 Gembloux, Belgium; nicolas.gengler@uliege.be (N.G.); hsoyeurt@uliege.be (H.S.)
3 Comité du Lait de Battice Route de Herve 104, 4651 Battice, Belgium; didier.veselko@comitedulait.be
7 LKV Austria Gemeinnützige GmbH, Dresdnerstr. 89/B1/18, 1200 Wien, Austria; Franz-Josef.Auer@lkv-austria.at
AuthorAffiliation_xml – name: 1 Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium; o.christophe@cra.wallonie.be (O.S.C.); c.grelet@cra.wallonie.be (C.G.)
– name: 2 Elevéo Asbl, AWE Group, 4, Rue des Champs Elysées, 5590 Ciney, Belgium; cbertozzi@awenet.be
– name: 4 France Conseil Elevage, Maison du Lait, 42 Rue de Chateaudun, 75009 Paris, France; christophe.lecomte@france-conseil-elevage.fr
– name: 5 Landeskontrollverband Nordrhein-Westfalen e.V., Bischofstraße 85, 47809 Krefeld, Germany; hoeckels@lkv-nrw.de
– name: 3 Comité du Lait de Battice Route de Herve 104, 4651 Battice, Belgium; didier.veselko@comitedulait.be
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– name: 8 Gembloux Agro-Bio Tech, TERRA Teaching and Research Centre, University of Liège, 5030 Gembloux, Belgium; nicolas.gengler@uliege.be (N.G.); hsoyeurt@uliege.be (H.S.)
– name: 6 LKV Baden Württemberg, Heinrich-Baumann Str. 1-3, 70190 Stuttgart, Germany; awerner@lkvbw.de
Author_xml – sequence: 1
  givenname: Octave S.
  orcidid: 0000-0002-9037-8079
  surname: Christophe
  fullname: Christophe, Octave S.
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  surname: Soyeurt
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/34574345$$D View this record in MEDLINE/PubMed
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Issue 9
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minerals
mid-infrared
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Snippet Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and...
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SubjectTerms Analytical chemistry
Animal production & animal husbandry
Austria
Calibration
dairy herds
Dairy industry
Datasets
Dietary minerals
Emission analysis
Environmental impact
Food Science
Health (social science)
Health Professions (miscellaneous)
herd improvement
Hypertension
Inductively coupled plasma
Infrared spectra
Infrared spectroscopy
Lactation
least squares
Life sciences
Microbiology
mid-infrared
Milk
milk composition
Mineral composition
mineral content
Minerals
Plant Science
Potassium
prediction
Predictions
Productions animales & zootechnie
Sciences des denrées alimentaires
Sciences du vivant
Scientific imaging
Sodium
Spectrometry
spectroscopy
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Title Multiple Breeds and Countries’ Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry
URI https://www.ncbi.nlm.nih.gov/pubmed/34574345
https://www.proquest.com/docview/2576407537
https://www.proquest.com/docview/2577452706
https://www.proquest.com/docview/2636417603
http://orbi.ulg.ac.be/handle/2268/290181
https://pubmed.ncbi.nlm.nih.gov/PMC8470342
https://doaj.org/article/97af59b754f147e080d23a798e64a25e
Volume 10
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