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Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis

Published online by Cambridge University Press:  27 April 2012

H. Soyeurt ,
C. Bastin ,
F. G. Colinet ,
V. M.-R. Arnould ,
D. P. Berry ,
E. Wall ,
F. Dehareng ,
H. N. Nguyen ,
P. Dardenne  and
J. Schefers
...Show all authors
H. Soyeurt*
Affiliation:
Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium National Fund for Scientific Research, B-1000 Brussels, Belgium
C. Bastin
Affiliation:
Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
F. G. Colinet
Affiliation:
Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
V. M.-R. Arnould
Affiliation:
Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium Convis Herdbuch, Zone artisanale et commerciale no 4, 9085 Ettelbruck, Luxembourg
D. P. Berry
Affiliation:
Animal and Grassland Research and Innovation Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland
E. Wall
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, Easter Bush Campus, Midlothian EH25 9RG, UK
F. Dehareng
Affiliation:
Valorisation of Agricultural Products Department, Walloon Research Centre, 5030 Gembloux, Belgium
H. N. Nguyen
Affiliation:
Valorisation of Agricultural Products Department, Walloon Research Centre, 5030 Gembloux, Belgium
P. Dardenne
Affiliation:
Valorisation of Agricultural Products Department, Walloon Research Centre, 5030 Gembloux, Belgium
J. Schefers
Affiliation:
Department of Dairy Science, University of Wisconsin, 1675 Observatory Drive, Madison, WI 53706, USA
J. Vandenplas
Affiliation:
Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium National Fund for Scientific Research, B-1000 Brussels, Belgium
K. Weigel
Affiliation:
Department of Dairy Science, University of Wisconsin, 1675 Observatory Drive, Madison, WI 53706, USA
M. Coffey
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, Easter Bush Campus, Midlothian EH25 9RG, UK
L. Théron
Affiliation:
Clinical Department for Production Animals – Ruminants Clinic, Faculty of Veterinary Medicine, University of Liège, 4000 Liège, Belgium
J. Detilleux
Affiliation:
Clinical Department for Production Animals – Ruminants Clinic, Faculty of Veterinary Medicine, University of Liège, 4000 Liège, Belgium
E. Reding
Affiliation:
Walloon Breeding Association, 5590 Ciney, Belgium
N. Gengler
Affiliation:
Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium National Fund for Scientific Research, B-1000 Brussels, Belgium
S. McParland
Affiliation:
Animal and Grassland Research and Innovation Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland
*
†E-mail: [email protected]
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Abstract

Lactoferrin (LTF) is a milk glycoprotein favorably associated with the immune system of dairy cows. Somatic cell count is often used as an indicator of mastitis in dairy cows, but knowledge on the milk LTF content could aid in mastitis detection. An inexpensive, rapid and robust method to predict milk LTF is required. The aim of this study was to develop an equation to quantify the LTF content in bovine milk using mid-infrared (MIR) spectrometry. LTF was quantified by enzyme-linked immunosorbent assay (ELISA), and all milk samples were analyzed by MIR. After discarding samples with a coefficient of variation between 2 ELISA measurements of more than 5% and the spectral outliers, the calibration set consisted of 2499 samples from Belgium (n = 110), Ireland (n = 1658) and Scotland (n = 731). Six statistical methods were evaluated to develop the LTF equation. The best method yielded a cross-validation coefficient of determination for LTF of 0.71 and a cross-validation standard error of 50.55 mg/l of milk. An external validation was undertaken using an additional dataset containing 274 Walloon samples. The validation coefficient of determination was 0.60. To assess the usefulness of the MIR predicted LTF, four logistic regressions using somatic cell score (SCS) and MIR LTF were developed to predict the presence of mastitis. The dataset used to build the logistic regressions consisted of 275 mastitis records and 13 507 MIR data collected in 18 Walloon herds. The LTF and the interaction SCS × LTF effects were significant (P < 0.001 and P = 0.02, respectively). When only the predicted LTF was included in the model, the prediction of the presence of mastitis was not accurate despite a moderate correlation between SCS and LTF (r = 0.54). The specificity and the sensitivity of models were assessed using Walloon data (i.e. internal validation) and data collected from a research herd at the University of Wisconsin – Madison (i.e. 5886 Wisconsin MIR records related to 93 mastistis events – external validation). Model specificity was better when LTF was included in the regression along with SCS when compared with SCS alone. Correct classification of non-mastitis records was 95.44% and 92.05% from Wisconsin and Walloon data, respectively. The same conclusion was formulated from the Hosmer and Lemeshow test. In conclusion, this study confirms the possibility to quantify an LTF indicator from milk MIR spectra. It suggests the usefulness of this indicator associated to SCS to detect the presence of mastitis. Moreover, the knowledge of milk LTF could also improve the milk nutritional quality.

Keywords

cowlactoferrinmilkmid-infrared spectrometrymastitis
Type
Behaviour, welfare and health
Information
animal , Volume 6 , Issue 11 , November 2012 , pp. 1830 - 1838
Copyright
Copyright © The Animal Consortium 2012

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References

Baker, HM, Baker, EN 2004. Lactoferrin and Iron: structural and dynamic aspects of binding and release. BioMetals 17, 209–2016.Google Scholar
Baker, EN 2005. Lactoferrin: a multi-tasking protein par excellence. Cellular and Molecular Life Sciences 62, 2529–2530.Google Scholar
Barbano, DM, Dellavalle, ME 1987. Rapid method for determination of milk casein content by infrared analysis. Journal of Dairy Science 70, 1524–1528.CrossRefGoogle ScholarPubMed
Baumrucker, CR 2000. Mammary mechanisms for lactoferrin: interactions with IGFBP-3. Biotechnology Agronomy, Society and Environment 4, 5–12.Google Scholar
Baumrucker, CR, Schanbacher, F, Shang, Y, Green, MH 2005. Lactoferrin interaction with retinoid signaling: cell growth and apoptosis in mammary cells. Domestic Animal Endocrinology 30, 289–303.CrossRefGoogle ScholarPubMed
Baveye, S, Elass, E, Mazurier, J, Spik, G, Legrand, D 1999. Lactoferrin: a multifunctional glycoprotein involved in the modulation of the inflammatory process. Clinical Chemistry and Laboratory Medicine 37, 281–296.Google Scholar
Coffey, MP, Simm, G, Oldham, JD, Hill, WG, Brotherstone, S 2004. Genotype and diet effects on energy balance in the first three lactations of dairy cows. Journal of Dairy Science 87, 4318–4326.Google Scholar
Coleman, J, Pierce, KM, Berry, DP, Brennan, A, Horan, B 2009. The influence of genetic selection and feed system on the reproductive performance of spring-calving dairy cows within future pasture-based production systems. Journal of Dairy Science 92, 5258–5269.CrossRefGoogle ScholarPubMed
Dal Zotto, R, De Marchi, M, Cecchinato, A, Penasa, M, Cdro, M, Carnier, P, Gallo, L, Bittante, G 2008. Reproducibility and repeatability of measures of milk coagulation properties and predictive ability of mid-infrared reflectance spectroscopy. Journal of Dairy Science 91, 4103–4112.CrossRefGoogle ScholarPubMed
Emanuelson, U, Danell, B, Philipsson, J 1988. Genetic parameters for clinical mastitis, somatic cell counts, and milk production estimated by multiple-trait restricted maximum likelihood. Journal of Dairy Science 71, 467–476.CrossRefGoogle ScholarPubMed
Farnaud, S, Evans, RW 2003. Lactoferrin – a multifunctional protein with antimicrobial properties. Molecular Immunology 40, 395–405.Google Scholar
Gaunt, SN, Raffio, N, Kingsbury, ET, Damon, RA, Johnson, WH, Mitchell, BA 1980. Variation of lactoferrin and mastitis and their heritabilities. Journal of Dairy Science 63, 1874–1880.Google Scholar
Hagiwara, S-I, Kawai, K, Anri, A, Nagahata, H 2003. Lactoferrin concentrations in milk from normal and subclinical mastitis cows. Journal of Veterinary Medical Science 65, 319–323.Google Scholar
Harmon, RJ 1994. Physiology of mastitis and factors affecting somatic cell counts. Journal of Dairy Science 77, 2103–2112.Google Scholar
Hruschka, WR 1987. Data analysis: wavelength selection methods. In Near-infrared technology in the agricultural and food industries (ed. P Williams and K Norris), pp. 35–55. American Association of Cereal Chemists, St Paul, MN, USA.Google Scholar
Kutila, T, Suojala, L, Lehtolainen, T, Saloniemi, H, Kaartinen, L, Tähti, M, Seppälä, K, Pyörälä, S 2004. The efficacy of bovine lactoferrin in the treatment of cows with experimentally induced Escherichia coli mastitis. Journal of Veterinary Pharmacology and Therapeutics 27, 197–202.Google Scholar
Lindmark-Mansson, H, Bränning, C, Aldén, G, Paulsson, M 2006. Relationship between somatic cell count, individual leukocyte populations and milk components in bovine udder quarter milk. International Dairy Journal 16, 717–727.Google Scholar
Mead, PE, Tweedie, JW 1990. cDNA and protein sequence of bovine lactoferrin. Nucleic Acids Research 18, 7167.Google Scholar
Pösö, J, Mäntysaari, EA 1996. Relationships between clinical mastitis, somatic cell score, and production for the first three lactations of Finnish Ayrshire. Journal of Dairy Science 79, 1284–1291.Google Scholar
Prendiville, R, Lewis, E, Pierce, KM, Buckley, F 2010. Comparative grazing behavior of lactating Holstein–Friesian, Jersey, and Jersey × Holstein–Friesian dairy cows and its association with intake capacity and production efficiency. Journal of Dairy Science 93, 764–774.Google Scholar
Pugovel, G, Baumrucker, CR, Sauerwein, H, Rühl, R, Ontsouka, E, Hammon, HM, Blum, JM 2005. Effects of an enhanced vitamin A intake during the dry period on retinoids, lactoferrin, IGF system, mammary gland epithelial cell apoptosis, and subsequent lactation in dairy cows. Journal of Dairy Science 88, 1785–1800.Google Scholar
Rutten, MJM, Bovenhuis, H, Hettinga, KA, Van Vanlenberg, HJF, Van Arendonck, JAM 2009. Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer. Journal of Dairy Science 92, 6202–6209.Google Scholar
SAS 1999. SAS/STAT user's guide. Version 8. SAS Institute Inc., Cary, NC, USA. 3884p.Google Scholar
Sinnaeve, G, Dardenne, P, Agneessens, R, Biston, R 1994. The use of near infrared spectroscopy for the analysis of fresh grass silage. Journal of Near Infrared Spectroscopy 2, 79–84.CrossRefGoogle Scholar
Sivakesava, S, Irudayaraj, J 2002. Rapid determination of tetracycline in milk by FT-MIR and FT-NIR Spectroscopy. Journal of Dairy Science 85, 487–493.Google Scholar
Sorensen, LK, Lund, M, Juul, B 2003. Accuracy of Fourier transform infrared spectrometry in determination of casein in dairy cows’ milk. Journal of Dairy Research 70, 445–452.Google Scholar
Soyeurt, H, Dardenne, P, Lognay, G, Veselko, D, Mayeres, P, Gengler, N 2006. Estimating fatty acid content in cow milk using mid-infrared spectrometry. Journal of Dairy Science 89, 3690–3695.CrossRefGoogle ScholarPubMed
Soyeurt, H, Colinet, FG, Arnould, VM-R, Dardenne, P, Bertozzi, C, Renaville, R, Portetelle, D, Gengler, N 2007. Genetic variability of lactoferrin content estimated by mid-infrared spectrometry in bovine milk. Journal of Dairy Science 90, 4443–4450.Google Scholar
Soyeurt, H, Dehareng, F, Gengler, N, McParland, S, Wall, E, Berry, DP, Coffey, M 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. Journal of Dairy Science 94, 1657–1667.Google Scholar
Tsuda, H, Sekine, K, Ushida, Y, Kuhara, T, Takasuka, N, Iigo, M, Seok Han, B, Moore, MA 2000. Milk and dairy products in cancer prevention: focus on bovine lactoferrin. Mutation Research 462, 227–233.Google Scholar
Ward, PP, Paz, A, Conneely, OM 2005. Multifunctional roles of lactoferrin: a critical overview. Cellular and Molecular Life Science 62, 2540–2548.Google Scholar
Westerhaus, MO 1990. Improving repeatability of NIR calibrations across instruments. In Proceedings of the 3rd International Conference on Near Infrared Spectroscopy, Brussels, Belgium, pp. 671–674. Agricultural Research Centre, Gembloux, Belgium.Google Scholar
William, P, Norris, K 2001. Near-infrared technology in the agricultural and food industries. American Association of Cereal Chemists, St Paul, MN.Google Scholar
Williams, P 2007. Near-infrared technology – getting the best out of light. PDK Grain, Nanaimo, Canada.Google Scholar