Are Breed-specific Differences in Beef Fatty-acid Profiles a "Needle in the Haystack"?

Yuvaraj Ranganathan^1, , Irena Hulova¹, Matthias Schreiner² and Jan Macak¹

¹Agriresearch Rapotin Ltd., Vikyrovice, Czech Republic

²Department of Food Science and Technology, University of Natural Resources and Applied Life Sciences (BOKU), Vienna, Austria

Cite this paper:
Yuvaraj Ranganathan, Irena Hulova, Matthias Schreiner and Jan Macak. Are Breed-specific Differences in Beef Fatty-acid Profiles a "Needle in the Haystack"?. American Journal of Food Science and Technology. 2014; 2(6):192-195. doi: 10.12691/ajfst-2-6-4

Abstract

A healthy human nutrition stems from macronutrients that provide energy and also from micronutrients that are indispensable for basic physiological processes. Essential fatty-acids have been proved and commonly agreed in the recent years as important elements of a healthy diet. This gives impetus to look for sources of essential fatty-acids in the diet. Beef is not only considered a complete protein, but also a source of essential fatty-acids such as linoleic and linolenic acids. The levels of these fatty-acids are however variable depending on the diet and breed of cattle. In this work, we analyzed intra-muscular fatty acid profiles of two breeds (Aberdeen Angus and Blonde d'Aquitaine) as a part of long-term complex and comprehensive comparison of breed differences. Using a novel statistical algorithm we found interesting breed-specific fatty-acid profiles. The advantages of this approach over conventional approaches are discussed as well as the specific differences in fatty acid profiles between the breeds.

Keywords:
Aberdeen Angus Blonde d'Aquitaine beef fatty acids multivariate analysis random forests

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Ascherio A. and Willett W.C., “Health effects of trans fatty acids,” The American Journal of Clinical Nutrition, 66, 1006S-1010S. 1997.
[2]	Daley , Abbott A., Doyle P.S., Nader G.A. and Larson S., “A review of fatty acid profiles and antioxidant content in grass-fed and grain-fed beef,” Nutrition Journal, 9, 10. 2010.
[3]	Bures D., Barton L., Zahradkova R., Teslik V., and Krejcova M., “Chemical composition, sensory characteristics, and fatty acid profile of muscle from Aberdeen Angus, Charolais, Simmental, and Hereford bulls,” Czech Journal of Animal Science, 51, 279-284. 2006.
[4]	Barton L., Bures D. and Kudrna V., “Meat quality and fatty acid profile of the musculus longissimus lumborum in Czech Fleckvieh, Charolais and Charolais x Czech Fleckvieh bulls fed different types of silages,” Czech Journal of Animal Science, 55, 479-487. 2010.
[5]	Wyness L., Weichselbaum E., O'Connor A., Williams E.B., Benelam B., Riley H. and Stanner S., “Red meat in the diet: an update,” Nutrition Bulletin, 36, 34-77. 2011.
[6]	Brouwer I.A., Wanders A.J. and Katan M.B., “Effect of animal and industrial trans fatty acids on HDL and LDL cholesterol levels in humans – a quantitative review,” PLoS One, 5, e9434. 2010.
[7]	Astrup A., Dyerberg J., Elwood P., Hermansen K., Hu F.B., Jakobsen M.U., Kok F.J., Krauss R.M., Lecerf J.M., LeGrand P., Nestel P., Risérus U., Sanders T., Sinclair A., Stender S., Tholstrup T. and Willett W.C., “The role of reducing intakes of saturated fat in the prevention of cardiovascular disease: where does the evidence stand in 2010?,” The American Journal of Clinical Nutrition, 93, 684-688. 2011.
[8]	, Pierre F.H. and Corpet D.E., “Heme iron from meat and risk of colorectal cancer: a meta-analysis and a review of the mechanisms involved,” Cancer Prevention Research, 4, 177-184. 2011.
[9]	Welch A.A., Shakya-Shrestha S., Lentjes M.A., Wareham N.J. and Khaw K.T., “Dietary intake and status of n−3 polyunsaturated fatty acids in a population of fish-eating and non-fish-eating meat-eaters, vegetarians, and vegans and the precursor-product ratio of α-linolenic acid to long-chain n−3 polyunsaturated fatty acids: results from the EPIC-Norfolk cohort,” The American Journal of Clinical Nutrition, 92, 1040-1051. 2010.
[10]	McGregor E.M., Campbell C.P., Miller S.P., Purslow P.P. and Mandell I.B., “Effect of nutritional regimen including limit feeding and breed on growth performance, carcass characteristics and meat quality in beef cattle,” Canadian Journal of Animal Science, 92, 327-341. 2012.
[11]	Franco D., Crecente S., Vázquez J.A., Gómez M. and Lorenzo J.M., “Effect of cross breeding and amount of finishing diet on growth parameters, carcass and meat composition of foals slaughtered at 15 months of age,” Meat Science, 93 (3), 547-556. 2013.
[12]	Shingfield K.J., Bonnet M. and , “Recent developments in altering the fatty acid composition of ruminant-derived foods,” Animal, 7, 132-162. 2013.
[13]	Cifkova E., Holcapek M., Lisa M., Ovcacikova M., Lycka A., Lynen F. and Sandra P., “Nontargeted quantitation of lipid classes using hydrophilic interaction liquid chromatography–electrospray ionization mass spectrometry with single internal standard and response factor approach,” Analytical Chemistry, 84, 10064-10070. 2012.
[14]	Lisa M. and Holcapek M., “Characterization of triacylglycerol enantiomers using chiral HPLC/APCI-MS and synthesis of enantiomeric triacylglycerols,” Analytical Chemistry, 85, 1852-1859. 2013.
[15]	Svetnik V., Liaw A., Tong C., Culberson J.C., Sheridan R.P. and Feuston B.P., “: A classification and regression tool for compound classification and QSAR modeling,” Journal of Chemical Information and Computer Sciences, 43, 1947-1958. 2003.
[16]	Cannon E.O., Bender A., Palmer D.S. and Mitchell J.B.O., “Chemoinformatics-based classification of prohibited substances employed for doping in sport,” Journal of Chemical Information and Modeling, 46, 2369-2380. 2006.
[17]	Díaz-Uriarte R. and de Andrés , “Gene selection and classification of microarray data using random forest,” BMC Bioinformatics, 7, 3. 2006.
[18]	Granitto P.M., Biasioli F., Aprea E., Mott D., Furlanello C., Märk T.D. and Gasperi F., “Rapid and non-destructive identification of strawberry cultivars by direct PTR-MS headspace analysis and data mining techniques,” Sensors and Actuators B, 121, 379-385. 2007.
[19]	Zhang J., , Peskind E.R., Quinn J.F., Jankovic J., Kenney C., Chung K.A., Millard S.P., Nutt J.G. and Montine T.J., “CSF multianalyte profile distinguishes Alzheimer and Parkinson disease,” American Journal of Clinical Pathology, 129, 526-529. 2008.
[20]	Gao D., Zhang Y.-X. and Zhao Y.-H., “Random forest algorithm for classification of multiwavelength data,” Research in Astronomy and Astrophysics, 9, 220-226. 2009.
[21]	Stevens J., Applied Multivariate Statistics for the Social Sciences, Erlbaum Associates, , 1992.
[22]	Breiman L., “Random forests,” Machine Learning, 45, 5-32. 2001.
[23]	Perdiguero-Alonso D., Montero F.E., Kostadinova A., Raga J.A. and Barrett J., “Random forests, a novel approach for discrimination of fish populations using parasites as biological tags,” International Journal for Parasitology, 38, 1425-1434. 2008.
[24]	R Development Core Team, “R: a language and environment for statistical computing. R Foundation for Statistical Computing,” , http://www.R-project.org. 2009.