Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-23T17:46:22.719Z Has data issue: false hasContentIssue false

Validation of fatty acid predictions in milk using mid-infrared spectrometry across cattle breeds

Published online by Cambridge University Press:  02 July 2012

M. H. T. Maurice-Van Eijndhoven
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH Wageningen, The Netherlands
H. Soyeurt
Affiliation:
Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium National Fund for Scientific Research, 1000 Brussels, Belgium
F. Dehareng
Affiliation:
Valorisation of Agricultural Products, Walloon Agricultural Research Centre, 5030 Gembloux, Belgium
M. P. L. Calus
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands
Get access

Abstract

The aim of this study was to investigate the accuracy to predict detailed fatty acid (FA) composition of bovine milk by mid-infrared spectrometry, for a cattle population that partly differed in terms of country, breed and methodology used to measure actual FA composition compared with the calibration data set. Calibration equations for predicting FA composition using mid-infrared spectrometry were developed in the European project RobustMilk and based on 1236 milk samples from multiple cattle breeds from Ireland, Scotland and the Walloon Region of Belgium. The validation data set contained 190 milk samples from cows in the Netherlands across four breeds: Dutch Friesian, Meuse-Rhine-Yssel, Groningen White Headed (GWH) and Jersey (JER). The FA measurements were performed using gas–liquid partition chromatography (GC) as the gold standard. Some FAs and groups of FAs were not considered because of differences in definition, as the capillary column of the GC was not the same as used to develop the calibration equations. Differences in performance of the calibration equations between breeds were mainly found by evaluating the standard error of validation and the average prediction error. In general, for the GWH breed the smallest differences were found between predicted and reference GC values and least variation in prediction errors, whereas for JER the largest differences were found between predicted and reference GC values and most variation in prediction errors. For the individual FAs 4:0, 6:0, 8:0, 10:0, 12:0, 14:0 and 16:0 and the groups’ saturated FAs, short-chain FAs and medium-chain FAs, predictions assessed for all breeds together were highly accurate (validation R2 > 0.80) with limited bias. For the individual FAs cis-14:1, cis-16:1 and 18:0, the calibration equations were moderately accurate (R2 in the range of 0.60 to 0.80) and for the individual FA 17:0 predictions were less accurate (R2 < 0.60) with considerable bias. FA concentrations in the validation data set of our study were generally higher than those in the calibration data. This difference in the range of FA concentrations, mainly due to breed differences in our study, can cause lower accuracy. In conclusion, the RobustMilk calibration equations can be used to predict most FAs in milk from the four breeds in the Netherlands with only a minor loss of accuracy.

Type
Product quality, human health and well-being
Copyright
Copyright © The Animal Consortium 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bobe, G, Lindberg, GL, Freeman, AE, Beitz, DC 2007. Short communication: composition of milk protein and milk fatty acids is stable for cows differing in genetic merit for milk production. Journal of Dairy Science 90, 39553960.CrossRefGoogle ScholarPubMed
Christie, WW 1998. Gas chromatography mass spectrometry methods for structural analysis of fatty acids. Lipids 33, 343353.CrossRefGoogle ScholarPubMed
Etzion, Y, Linker, R, Cogan, U, Shmulevich, I 2004. Determination of protein concentration in raw milk by mid-infrared Fourier transform infrared/attenuated total reflectance spectroscopy. Journal of Dairy Science 87, 27792788.Google Scholar
Gander, GW, Sampugna, J, Jensen, RG 1962. Analysis of milk fatty acids by gas–liquid chromatography. Journal of Dairy Science 45, 323328.CrossRefGoogle Scholar
ISO–IDF (International Organization for Standardization–International Dairy Federation) 2002. Milk fat – preparation of fatty acid methyl esters. ISO 15884-IDF 184. International Dairy Federation, Brussels, Belgium.Google Scholar
Maurice-Van Eijndhoven, MHT, Hiemstra, SJ, Calus, MPL 2011. Short communication: milk fat composition of 4 cattle breeds in the Netherlands. Journal of Dairy Science 94, 10211025.Google Scholar
Palmquist, DL 2006. Milk fat: origin of fatty acids and influence of nutritional factors thereon. Springer, New York, NY, USA.Google Scholar
Palmquist, DL, Stelwagen, K, Robinson, PH 2006. Modifying milk composition to increase use of dairy products in healthy diets – preface. Animal Feed Science and Technology 131, 149153.CrossRefGoogle Scholar
Rutten, MJM, Bovenhuis, H, van Arendonk, JAM 2010. The effect of the number of observations used for Fourier transform infrared model calibration for bovine milk fat composition on the estimated genetic parameters of the predicted data. Journal of Dairy Science 93, 48724882.Google Scholar
Rutten, MJM, Bovenhuis, H, Hettinga, KA, van Valenberg, HJF, van Arendonk, JAM 2009. Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer. Journal of Dairy Science 92, 62026209.Google Scholar
Smith, LM 1961. Quantitative fatty acid analysis of milk fat by gas–liquid chromatography. Journal of Dairy Science 44, 607622.Google Scholar
Soyeurt, H, Gengler, N 2008. Genetic variability of fatty acids in bovine milk. Biotechnologie, Agronomie, Société et Environnement 12, 203210.Google Scholar
Soyeurt, H, Dehareng, F, Gengler, N, McParland, S, Wall, E, Berry, DP, Coffey, M, Dardenne, P 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. Journal of Dairy Science 94, 16571667.Google Scholar
Soyeurt, H, Dardenne, P, Dehareng, F, Lognay, G, Veselko, D, Marlier, M, Bertozzi, C, Mayeres, P, Gengler, N 2006. Estimating fatty acid content in cow milk using mid-infrared spectrometry. Journal of Dairy Science 89, 36903695.Google Scholar
Stoop, WM, van Arendonk, JAM, Heck, JML, van Valenberg, HJF, Bovenhuis, H 2008. Genetic parameters for major milk fatty acids and milk production traits of Dutch Holstein–Friesians. Journal of Dairy Science 91, 385394.Google Scholar
Williams, PC, Sobering, DC 1993. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. Journal of Near Infrared Spectroscopy 1, 2532.CrossRefGoogle Scholar
Wilson, RH, Tapp, HS 1999. Mid-infrared spectroscopy for food analysis: recent new applications and relevant developments in sample presentation methods. TrAC Trends in Analytical Chemistry 18, 8593.CrossRefGoogle Scholar