Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-23T06:55:09.149Z Has data issue: false hasContentIssue false

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

Published online by Cambridge University Press:  23 August 2010

N. Prieto*
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
D. W. Ross
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
E. A. Navajas
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
R. I. Richardson
Affiliation:
Division of Farm Animal Science, University of Bristol, Langford, Bristol, BS40 5DU, UK
J. J. Hyslop
Affiliation:
Select Services, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
G. Simm
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
R. Roehe
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
Get access

Abstract

The objective of this study was to examine the online use of near infrared reflectance (NIR) spectroscopy to estimate the concentration of individual and groups of fatty acids (FA) as well as intramuscular fat (IMF) in crossbred Aberdeen Angus (AA×) and Limousin (LIM×) cattle. This was achieved by direct application of a fibre-optic probe to the muscle immediately after exposing the meat surface in the abattoir at 48 h post mortem. Samples of M. longissimus thoracis from 88 AA× and 106 LIM× were scanned over the NIR spectral range from 350 to 1800 nm and samples of the M. longissimus lumborum were analysed for IMF content and FA composition. Statistically significant differences (P < 0.001) were observed in most FA between the two breeds studied, with FA concentration being higher in AA× meat mainly. NIR calibrations, tested by cross-validation, showed moderate to high predictability in LIM× meat samples for C16:0, C16:1, C18:0, trans11 C18:1, C18:1, C18:2 n-6, C20:1, cis9, trans11 C18:2, SFA (saturated FA), MUFA (monounsaturated FA), PUFA (polyunsaturated FA) and IMF content with R2 (SECV, mg/100 g muscle) of 0.69 (146), 0.69 (28), 0.71 (62), 0.70 (8.1), 0.76 (192), 0.65 (13), 0.71 (0.9), 0.71 (2.9), 0.68 (235), 0.75 (240), 0.64 (17) and 0.75 (477), respectively. FA such as C14:0, C18:3 n-3, C20:4 n-6, C20:5 n-3, C22:6 n-3, n-6 and n-3 were more difficult to predict by NIR in these LIM× samples (R2 = 0.12 to 0.62; SECV = 0.5 to 26 mg/100 g muscle). In contrast, NIR showed low predictability for FA in AA× beef samples. In particular for LIM×, the correlations of NIR measurements and several FA in the range from 0.81 to 0.87 indicated that the NIR spectroscopy is a useful online technique for the early, fast and relatively inexpensive estimation of FA composition in the abattoir.

Type
Full Paper
Copyright
Copyright © The Animal Consortium 2010

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

Alomar, D, Gallo, C, Castañeda, M, Fuchslocher, R 2003. Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). Meat Science 63, 441450.Google Scholar
Alzón, M, Mendizabal, JA, Arana, A, Albertí, P, Purroy, A 2007. Adipocyte cellularity in different adipose depots in bulls of seven Spanish breeds slaughtered at two body weights. Animal 1, 261267.CrossRefGoogle ScholarPubMed
Andrés, S, Murray, I, Navajas, EA, Fisher, AV, Lambe, NR, Bünger, L 2007. Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy. Meat Science 76, 509516.CrossRefGoogle ScholarPubMed
Cozzolino, D, Murray, I 2002. Effect of sample presentation and animal muscle species on the analysis of meat by near infrared reflectance spectroscopy. Journal of Near Infrared Spectroscopy 10, 3744.CrossRefGoogle Scholar
Dannenberger, D, Nuernberg, G, Scollan, N, Ender, K, Nuernberg, K 2007. Diet alters the fatty acid composition of individual phospholipid classes in beef muscles. Journal of Agricultural and Food Chemistry 55, 452460.CrossRefGoogle Scholar
Davies, AMC, Grant, A 1987. Near infra-red analysis of food. International Journal of Food Science and Technology 22, 191207.CrossRefGoogle Scholar
De Pedro, EJ, Garrido, A, Bares, I, Casillas, M, Murray, I 1992. Application of near infrared spectroscopy for quality control of Iberian pork industry. InNear infrared spectroscopy bridging the gap between data analysis and NIR applications (ed. KI Hildrum, T Isaksson, T Naes and AD Tandberg), pp. 345348. Ellis Horwood, Chichester, UK.Google Scholar
Department of Health, UK 1994. Nutritional aspects of cardiovascular disease. In Reports on Health and Social Subjects (Her Majesty’s Stationery Office, London, UK) vol. 46, 1–186.Google Scholar
Dhanoa, MS, Lister, SJ, Sanderson, R, Barnes, RJ 1994. The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. Journal of Near Infrared Spectroscopy 2, 4347.CrossRefGoogle Scholar
Downey, G, Hildrum, KI 2004. Analysis of meats. In Near-infrared spectroscopy in agriculture (ed. L Al-Amoodi, R Craig, J Workman and J Reeves III), pp. 599632. American Society of Agronomy Inc., Crop Science Society of America Inc., Soil Science Society of America Inc., Madison, Wisconsin, USA.Google Scholar
Elmore, JS, Mottram, DS, Enser, M, Wood, JD 1999. Effect of the polyunsaturated fatty acid composition of beef muscle on the profile of aroma volatiles. Journal of Agricultural and Food Chemistry 47, 16191625.CrossRefGoogle ScholarPubMed
Enser, M 2001. The role of fats in human nutrition. In Oils and fats, vol 2. Animal carcass fats (ed. B Rossell), pp. 77122. Leatherhead Publishing, Leatherhead, Surrey, UK.Google Scholar
Enser, M, Hallett, KG, Hewitt, B, Fursey, GAJ, Wood, JD 1996. Fatty acid content and composition of English beef, lamb and pork at retail. Meat Science 42, 443456.Google Scholar
Enser, M, Hallett, KG, Hewett, B, Fursey, GAJ, Wood, JD, Harrington, G 1998. Fatty acid content and composition of UK beef and lamb muscle in relation to production system and implications for human nutrition. Meat Science 49, 329341.CrossRefGoogle ScholarPubMed
García-Olmo, J, Garrido-Varo, A, De Pedro, E 2001. The transfer of fatty acid calibration equations using four sets of unsealed liquid standardisation samples. Journal of Near Infrared Spectroscopy 9, 4962.CrossRefGoogle Scholar
Gatellier, P, Mercier, Y, Juin, H, Renerre, M 2005. Effect of finishing mode (pasture- or mixed-diet) on lipid composition, colour stability and lipid oxidation in meat from Charolais cattle. Meat Science 69, 175186.CrossRefGoogle ScholarPubMed
González-Martín, I, González-Pérez, C, Hernández-Méndez, J, Álvarez-García, N 2003. Determination of fatty acids in the subcutaneous fat of Iberian breed swine by near infrared spectroscopy (NIRS) with a fibre-optic probe. Meat Science 65, 713719.CrossRefGoogle ScholarPubMed
González-Martín, I, González-Pérez, C, Alvarez-García, N, Gónzalez-Cabrera, JM 2005. On-line determination of fatty acid composition in intramuscular fat of Iberian pork loin by NIRs with a remote reflectance fibre optic probe. Meat Science 69, 243248.CrossRefGoogle ScholarPubMed
González-Martín, I, González-Pérez, C, Hernández-Méndez, J, Alvarez-García, N, Merino Lázaro, S 2002. Determination of fatty acids in the subcutaneous fat of Iberian breed swine by Near Infrared Spectroscopy. A comparative study of the methods for obtaining total lipids: solvents and melting with microwaves. Journal of Near Infrared Spectroscopy 10, 257268.CrossRefGoogle Scholar
Kouba, M, Enser, M, Whittington, FM, Nute, GR, Wood, JD 2003. Effect of a high linolenic acid diet on lipogenic enzyme activities, fatty acid composition and meat quality in the growing pig. Journal of Animal Science 81, 19671979.CrossRefGoogle ScholarPubMed
Mendizabal, JA, Albertí, P, Eguinoa, P, Arana, A, Soret, B, Purroy, A 1999. Adipocyte size and lipogenic enzyme activities in different adipose tissue depots in steers of local Spanish breeds. Animal Science 69, 115121.CrossRefGoogle Scholar
Murray, I 1986. The NIR spectra of homologous series of organic compounds. In Proceedings International NIR/NIT Conference (ed. J Hollo, KJ Kaffka, and JL Gonczy), pp. 1328. Akademiai Kiado, Budapest, Hungary.Google Scholar
Murray, I, Williams, PC 1987. Chemical principles of near-infrared technology. In Near infrared technology in the agricultural and food industries (ed. PC Williams and K Norris), pp. 1734. American Association of Cereal Chemists, Inc., St. Paul, Minnesota, USA.Google Scholar
Okeudo, NJ, Moss, BW 2007. Intramuscular lipid and fatty acid profile of sheep comprising four sex-types and seven slaughter weights produced following commercial procedure. Meat Science 76, 195200.Google Scholar
Osborne, BG, Fearn, T, Hindle, PH 1993. Practical NIR spectroscopy with applications in food and beverage analysis, 2nd edition. Longman Scientific and Technical, Harlow, UK.Google Scholar
Park, B, Chen, YR, Hruschka, WR, Shackelford, SD, Koohmaraie, M 1998. Near-infrared reflectance analysis for predicting beef longissimus tenderness. Journal of Animal Science 76, 21152120.CrossRefGoogle ScholarPubMed
Pla, M, Hernández, P, Ariño, B, Ramírez, JA, Díaz, I 2007. Prediction of fatty acid content in rabbit meat and discrimination between conventional and organic production systems by NIRS methodology. Food Chemistry 100, 165170.CrossRefGoogle Scholar
Prieto, N, Andrés, S, Giráldez, FJ, Mantecón, AR, Lavín, P 2006. Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples. Meat Science 74, 487496.Google Scholar
Prieto, N, Andrés, S, Giráldez, FJ, Mantecón, AR, Lavín, P 2008. Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples. Meat Science 79, 692699.CrossRefGoogle Scholar
Prieto, N, Roehe, R, Lavín, P, Batten, G, Andrés, S 2009a. Application of near infrared reflectance spectroscopy to predict meat and meat products quality: a review. Meat Science 83, 175186.CrossRefGoogle ScholarPubMed
Prieto, N, Ross, DW, Navajas, EA, Nute, GR, Richardson, RI, Hyslop, JJ, Simm, G, Roehe, R 2009b. On-line application of visible and near infrared reflectance spectroscopy to predict chemical-physical and sensory characteristics of beef quality. Meat Science 83, 96103.CrossRefGoogle ScholarPubMed
Prieto, N, Ross, DW, Navajas, EA, Nute, GR, Richardson, RI, Hyslop, JJ, Simm, G, Roehe, R 2009c. On-line prediction of the fatty acid content of beef meat by near infrared reflectance spectroscopy. In Advances in Animal Biosciences. Proceedings of the British Society of Animal Science, p. 116. BSAS, UK.Google Scholar
Raes, K, De Smet, S, Demeyer, D 2001. Effect of double-muscling in Belgian Blue young bulls on the intramuscular fatty acid composition with emphasis on conjugated linoleic acid and polyunsaturated fatty acids. Animal Science 73, 253260.CrossRefGoogle Scholar
Rainer, L, Heiss, CJ 2004. Conjugated linoleic acid: health implications and effects on body composition. Journal of the American Dietetic Association 6, 963968.CrossRefGoogle Scholar
Realini, CE, Duckett, SK, Windham, WR 2004. Effect of vitamin C addition to ground beef from grass-fed or grain-fed sources on color and lipid stability, and prediction of fatty acid composition by near-infrared reflectance analysis. Meat Science 68, 3543.Google Scholar
Reinhardt, TC, Paul, C, Röbbelen, G 1992. Quantitative analysis of fatty acids in intact rapeseed by NIRS. In Making light work. Advances in near infrared spectroscopy (ed. I Murray and IA Cowe), pp. 323327. VCH, London, UK.Google Scholar
Sañudo, C, Enser, M, Campo, MM, Nute, GR, Maria, G, Sierra, I, Wood, JD 2000. Fatty acid composition and fatty acid characteristics of lamb carcasses from Britain and Spain. Meat Science 54, 339346.CrossRefGoogle ScholarPubMed
SAS INST. INC 2003. SAS/STAT®. User’s Guide (Version 9.1). SAS Publishing, Cary, NC, USA.Google Scholar
Scollan, ND, Choi, NJ, Kurt, E, Fisher, AV, Enser, M, Wood, JD 2001. Manipulating the fatty acid composition of muscle and adipose tissue in beef cattle. British Journal of Nutrition 85, 115124.Google Scholar
Scollan, N, Hocquette, JF, Nuernberg, K, Dannenberger, D, Richardson, I, Moloney, A 2006. Innovations in beef production systems that enhance the nutritional and health value of beef lipids and their relationship with meat quality. Meat Science 74, 1733.Google Scholar
Shackelford, SD, Wheeler, TL, Koohmaraie, M 2005. On-line classification of US Select beef carcasses for longissimus tenderness using visible and near-infrared reflectance spectroscopy. Meat Science 69, 409415.CrossRefGoogle ScholarPubMed
Sheard, PR, Enser, M, Wood, JD, Nute, GR, Gill, BP, Richardson, RI 2000. Shelf life and quality of pork and pork products with a raised n-3 PUFA. Meat Science 55, 213221.CrossRefGoogle ScholarPubMed
Shenk, JS, Workman, JJ, Westerhaus, MO 1992. Application of NIR spectroscopy to agricultural products. In Handbook of near infrared analysis, practical spectroscopy series (ed. DA Burns and EW Ciurczak), pp. 383431. Marcel Dekker, New York, USA.Google Scholar
Sierra, V, Aldai, N, Castro, P, Osoro, K, Coto-Montes, A, Oliván, M 2008. Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy. Meat Science 78, 248255.CrossRefGoogle ScholarPubMed
Teye, GA, Sheard, PR, Whittington, FM, Nute, GR, Stewart, A, Wood, JD 2006. Influence of dietary oils and protein level on pork quality. 1. Effects on muscle fatty acid composition, carcass, meat and eating quality. Meat Science 73, 157165.Google Scholar
Tøgersen, G, Isaksson, T, Nilsen, BN, Bakker, EA, Hildrum, KI 1999. On-line NIR analysis of fat, water and protein in industrial scale ground meat batches. Meat Science 51, 97102.CrossRefGoogle ScholarPubMed
Vatansever, L, Kurt, E, Enser, M, Nute, GR, Gill, BP, Richardson, RI 2000. Shelf life and eating quality of beef from cattle of different breeds given diets differing in n – 3 polyunsaturated fatty acid composition. Animal Science 71, 471482.CrossRefGoogle Scholar
Warren, HE, Scollan, ND, Enser, MB, Hughes, SI, Richardson, RI, Wood, JD 2008. Effects of breed and a concentrate or grass silage diet on beef quality in cattle of 3 ages. I. Animal performance, carcass quality and muscle fatty acid composition. Meat Science 78, 256269.CrossRefGoogle ScholarPubMed
Webb, EC, O’Neill, HA 2008. The animal fat paradox and meat quality. Meat Science 80, 2836.CrossRefGoogle ScholarPubMed
Westerhaus, M, Workman, JJ, IIIReeves, JB, Mark, H 2004. Quantitative analysis. In Near-infrared spectroscopy in agriculture (ed. CA Roberts, J Workman and JB Reeves III), pp. 133174. American Society of Agronomy Inc., Madison, USA.Google Scholar
Williams, PC 2001. Implementation of near-infrared technology. In Near-infrared technology in the agricultural and food industries, 2nd edition (ed. PC Williams and K Norris), pp. 145169. American Association of Cereal Chemists, St. Paul, Minnesota, USA.Google Scholar
Williams, PC 2008. Near-infrared technology-getting the best out of the light. A short course in the practical implementation of near infrared spectroscopy for user, 5.3th edition. PDK Projects Inc., Nanaimo, Canada.Google Scholar
Williams, PC, Norris, K 2001. Near-infrared technology in the agricultural and food industries, 2nd edition. American Association of Cereal Chemists Inc., St. Paul, Minnesota, USA.Google Scholar
Windham, WR, Morrison, WH 1998. Prediction of fatty acid content in beef neck lean by near infrared reflectance analysis. Journal of Near Infrared Spectroscopy 6, 229234.Google Scholar
Windham, WR, Mertens, DR, IIBarton, FE 1989. Protocol for NIR calibration; sample selection and equation development and validation. In Near infrared reflectance spectroscopy (NIRS): analysis of forage quality (ed. GC Marten, JS Shenk and FE Barton II), pp. 96103. USDA Handbook no. 643. US Govt. Printing Office, Washington, DC, USA.Google Scholar
Wood, JD, Richardson, IR, Nute, GR, Fisher, AV, Campo, MM, Kasapidou, E, Sheard, PR, Enser, M 2003. Effects of fatty acids on meat quality: a review. Meat Science 66, 2132.Google Scholar