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Accuracy and application of milk fatty acid estimation with diffuse reflectance near-infrared spectroscopy

Published online by Cambridge University Press:  22 May 2018

Andreas Melfsen ,
Max Holstermann ,
Angelika Haeussermann ,
Joachim Molkentin ,
Andreas Susenbeth  and
Eberhard Hartung
Andreas Melfsen
Affiliation:
Institute of Agricultural Engineering, Christian-Albrechts-University Kiel, 24098 Kiel, Germany
Max Holstermann
Affiliation:
Institute of Animal Nutrition and Physiology, Christian-Albrechts-University Kiel, 24098 Kiel, Germany
Angelika Haeussermann*
Affiliation:
Institute of Agricultural Engineering, Christian-Albrechts-University Kiel, 24098 Kiel, Germany
Joachim Molkentin
Affiliation:
Department of Safety and Quality of Milk and Fish Products, Max Rubner-Institute, 24103 Kiel, Germany
Andreas Susenbeth
Affiliation:
Institute of Animal Nutrition and Physiology, Christian-Albrechts-University Kiel, 24098 Kiel, Germany
Eberhard Hartung
Affiliation:
Institute of Agricultural Engineering, Christian-Albrechts-University Kiel, 24098 Kiel, Germany
*
*For correspondence; e-mail: [email protected]
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Abstract

Near infrared spectroscopy (NIRS) has the potential to estimate contents of fatty acids (FA) in milk frequently at-farm or during daily milking routine. In this study, a total of 738 raw milk spectra collected from 33 Holstein cows over a period of 30 weeks were recorded. Reference data on FA composition in milk and in milk fat were analysed in laboratory. Calibration models were calculated for single FA and groups of FA in milk and in milk fat. Validation resulted in sufficient Ratio of Prediction to Deviation (RPD) values for some single FA and in higher RPD values for groups of FA when concentrations of FA in milk were predicted. Since the concentrations of most FA in milk are highly correlated with milk fat content, the prediction of FA contents in milk fat is more meaningful when independent predictions are intended. The accuracy of predicting single FA concentrations in milk fat is rather poor for most FA but still comparable to alternative analysing methods such as MIR analysis. The estimation of different groups of FA in milk fat resulted in an improved accuracy based on higher RPD values, which was sufficient to mirror the development in the different lactation phases. The course of cow individual long chain fatty acid (LCFA) concentration in the early lactation stage can be an indicator for body fat mobilisation. The accurate estimation of the extent and duration of body fat mobilisation in cow individuals was rather difficult with NIR predicted LCFA concentrations and would require a higher measuring frequency than applied in this study.

Keywords

NIRSmilk analysisbody fat mobilisationlong chain fatty acids
Type
Research Article
Information
Journal of Dairy Research , Volume 85 , Issue 2 , May 2018 , pp. 212 - 221
Copyright
Copyright © Hannah Dairy Research Foundation 2018 

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References

Aernouts, B, van Beers, R, Watte, R, Huybrechts, T, Lammertyn, J & Saeys, W 2015 Visible and near-infrared bulk optical properties of raw milk. Journal of Dairy Science 98 6727–6738Google Scholar
Aulrich, K & Molkentin, J 2009 Potential of near infrared spectroscopy for differentiation of organically and conventionally produced milk. Landbauforschung Volkenrode 59 301–307Google Scholar
Barnes, RJ, Dhanoa, MS & Lister, SJ 1989 Standard normal variate transformation and de-trending of near infrared diffuse reflectance spectra. Applied Spectroscopy 43 772–777Google Scholar
Chen, JY, Iyo, C, Terada, F & Kawano, S 2002 Effect of multiplicative scatter correction on wavelength selection for near infrared calibration to determine fat content in raw milk. Journal of near Infrared Spectroscopy 10 301–307Google Scholar
Coppa, M, Ferlay, A, Leroux, C, Jestin, M, Chilliard, Y, Martin, B & Andueza, D 2010 Prediction of milk fatty acid composition by near infrared reflectance spectroscopy. International Dairy Journal 20 182–189Google Scholar
Creamer, LK & MacGibbon, AKH 1996 Some recent advances in the basic chemistry of milk proteins and lipids. International Dairy Journal 6 539–568Google Scholar
Gross, J, van Dorland, HA, Bruckmaier, RM & Schwarz, FJ 2011 Milk fatty acid profile related to energy balance in dairy cows. Journal of Dairy Research 78 479–488CrossRefGoogle ScholarPubMed
Grummer, RR 1991 Effect of feed on the composition of milk Fat. Journal of Dairy Science 74 3244–3257Google Scholar
Holstermann, M 2012 Änderungen des Fettsäuremusters und der delta13C-Werte im Milchfett als Indikatoren für die Körperfettmobilisierung bei Kühen [Changes of fatty acid pattern and delta13C values in milk fat as indicators for mobilisation of body fat in dairy cows]. PhD thesis. Aachen, Germany, Kiel University, ed Verlag, ShakerGoogle Scholar
IDF Standard 1D 1996 Milk – Determination of fat Content (Röse Gottlieb Reference Method). Brussels, Belgium: International Dairy Federation (IDF)Google Scholar
Iggman, D & Risérus, U 2011 Role of different dietary saturated fatty acids for cardiometabolic risk. Clinical Lipidology 6 209–223CrossRefGoogle Scholar
ISO 15885 2002 Milk fat – Determination of the Fatty Acid Composition by gas-Liquid Chromatography. Geneva, Switzerland: International Organization for Standardization (ISO)Google Scholar
Jensen, RG 2002 The composition of bovine milk lipids: January 1995 to December 2000. Journal of Dairy Science 85 295–350CrossRefGoogle ScholarPubMed
Kawasaki, M, Kawamura, S, Tsukahara, M, Morita, S, Komiya, M & Natsuga, M 2008 Near-infrared spectroscopic sensing system for on-line milk quality assessment in a milking robot. Computers and Electronics in Agriculture 63 22–27CrossRefGoogle Scholar
MacGibbon, AKH & Taylor, MW 2006 Composition and structure of bovine milk lipids. In: Advanced Dairy Chemistry, Volume 2 – Lipids, 3rd Edition, pp. 1–42 (Eds. Fox, PF & McSweeney, PLH). New York, USA: Springer Science + Business Media Inc.Google Scholar
Manley, M 2014 Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. Chemical Society Reviews 43 8200–8214Google Scholar
Mansson, HL 2008 Fatty acids in bovine milk fat. Food & Nutrition Research 52Google Scholar
Melfsen, A, Hartung, E & Haeussermann, A 2012a Accuracy of milk composition analysis with near infrared spectroscopy in diffuse reflection mode. Biosystems Engineering 112 210–217Google Scholar
Melfsen, A, Hartung, E & Haeussermann, A 2012b Potential of individual cow scatter correction for an improved accuracy of near infrared milk composition analysis. Journal of near Infrared Spectroscopy 20 477–482Google Scholar
Melfsen, A, Hartung, E & Haeussermann, A 2012c Accuracy of in-line milk composition analysis with diffuse reflectance near-infrared spectroscopy. Journal of Dairy Science 95 6465–6476Google Scholar
Melfsen, A, Hartung, E & Haeussermann, A 2013 Robustness of near-infrared calibration models for the prediction of milk constituents during the milking process. Journal of Dairy Research 80 103–112CrossRefGoogle ScholarPubMed
Micha, R & Mozaffarian, D 2010 Saturated fat and cardiometabolicrisk factors, coronary heart disease, stroke, and diabetes: a fresh look at the evidence. Lipids 45 893–905Google Scholar
Molkentin, J 2009 Authentication of organic milk using δ13C and the α-linolenic acid content of milk fat. Journal of Agricultural and Food Chemistry 57 785–790Google Scholar
Nguyen, HN, Dehareng, F, Hammida, M, Baeten, V, Froidmont, E, Soyeurt, H, Niemoeller, A & Dardenne, P 2011 Potential of near infrared spectroscopy for on-line analysis at the milking parlour using a fibre-optic probe presentation. NIR News 22 11–13Google Scholar
Palmquist, DL 2006 Milk fat: origin of fatty acids and influence of nutritional factors. In Advanced Dairy Chemistry, Volume 2 – Lipids, 3rd Edition, pp. 43–92 (Eds Fox, PF & McSweeney, PLH) New York, USA: Springer Science + Business Media IncGoogle Scholar
Parodi, PW 2006 Nutritional significance of milk lipids. In Advanced Dairy Chemistry, Volume 2 – Lipids, 3rd Edition, pp. 601–633 (Eds Fox, PF & McSweeney, PLH). New York, USA, Springer Science + Business Media IncGoogle Scholar
Rico, DE, Marshall, ER, Choi, J, Kaylegian, KE, Dechow, CD & Harvatine, KJ 2014 Within-milking variation in milk composition and fatty acid profile of Holstein dairy cows. Journal of Dairy Science 97 4259–4268Google Scholar
Robbins, KR, Saxton, AM & Southern, LL 2006 Estimation of nutrient requirements using broken-line regression analysis. Journal of Animal Science 84 155–165CrossRefGoogle ScholarPubMed
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 6202–6209Google Scholar
Saranwong, S & Kawano, S 2008 System design for non-destructive near infrared analyses of chemical components and total aerobic bacteria count of raw milk. Journal of near Infrared Spectroscopy 16 389–398Google Scholar
Schmilovitch, Z, Shmulevich, I, Notea, A & Maltz, E 2000 Near infrared spectrometry of milk in its heterogeneous state. Computers and Electronics in Agriculture 29 195–207Google 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 3690–3695Google 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 1657–1667CrossRefGoogle ScholarPubMed
Stoop, WM, Bovenhuis, H, Heck, JML & van Arendonk, JAM 2009 Effect of lactation stage and energy status on milk fat composition of Holstein-Frisian cows. Journal of Dairy Science 92 1469–1478CrossRefGoogle Scholar
Tsenkova, R, Atanassova, S, Toyoda, K, Ozaki, Y, Itoh, K & Fearn, T 1999 Near-infrared spectroscopy for dairy management: measurement of unhomogenized milk composition. Journal of Dairy Science 82 2344–2351Google Scholar
Tsenkova, R, Atanassova, S, Ozaki, Y, Toyoda, K & Itoh, K 2001 Near-infrared spectroscopy for biomonitoring: influence of somatic cell count on cow's milk composition analysis. International Dairy Journal 11 779–783Google Scholar
Tsenkova, R, Atanassova, S, Morita, H, Ikuta, K, Toyoda, K, Iordanova, IK & Hakogi, E 2006 Near infrared spectra of cows’ milk for milk quality evaluation: disease diagnosis and pathogen identification. Journal of near Infrared Spectroscopy 14 363–370Google Scholar
Williams, PC 2001 Implementation of near-infrared technology. In Near-Infrared Technology in the Agricultural and Food Industries, pp. 145–170 (Ed. Williams, P & Norris, KH). St. Paul Minnesota: American Association of Cereal ChemistsGoogle Scholar