Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-14T16:58:02.241Z Has data issue: false hasContentIssue false
  • English
  • Français

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

Published online by Cambridge University Press:  03 June 2011

E. Gjerlaug-Enger ,
J. Kongsro ,
L. Aass ,
J. Ødegård  and
O. Vangen
E. Gjerlaug-Enger*
Affiliation:
Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, 1432 Ås, Norway Norsvin, PO Box 504, 2304 Hamar, Norway
J. Kongsro
Affiliation:
Norsvin, PO Box 504, 2304 Hamar, Norway
L. Aass
Affiliation:
Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, 1432 Ås, Norway
J. Ødegård
Affiliation:
Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, 1432 Ås, Norway Nofima Marin, PO Box 5010, 1432 Ås, Norway
O. Vangen
Affiliation:
Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, 1432 Ås, Norway
*
†E-mail: eli.gjerlaug@norsvin.no
Get access

Abstract

This study was conducted to evaluate the potential of near-infrared (NIR) spectroscopy (NIRS) technology for prediction of the chemical composition (moisture content and fatty acid composition) of fat from fast-growing, lean slaughter pig samples coming from breeding programmes. NIRS method I: a total of 77 samples of intact subcutaneous fat from pigs were analysed with the FOSS FoodScan NIR spectrophotometer (850 to 1050 nm) and then used to predict the moisture content by using partial least squares (PLS) regression methods. The best equation obtained has a coefficient of determination for cross-validation (CV; R2cv) and a root mean square error of a CV (RMSECV) of 0.88 and 1.18%, respectively. The equation was further validated with (n = 15) providing values of 0.83 and 0.42% for the coefficient of determination for validation (R2val) and root mean square error of prediction (RMSEP), respectively. NIRS method II: in this case, samples of melted subcutaneous fat were analysed in an FOSS XDS NIR rapid content analyser (400 to 2500 nm). Equations based on modified PLS regression methods showed that NIRS technology could predict the fatty acid groups, the main fatty acids and the iodine value accurately with R2cv, RMSECV, R2val and RMSEP of 0.98, 0.38%, 0.95 and 0.49%, respectively (saturated fatty acids), 0.94, 0.45%, 0.97 and 0.65%, respectively (monounsaturated fatty acids), 0.97, 0.28%, 0.99 and 0.34%, respectively (polyunsaturated fatty acids), 0.76, 0.61%, 0.84 and 0.87%, respectively (palmitic acid, C16:0), 0.75, 0.16%, 0.89 and 0.10%, respectively (palmitoleic acid, C16:1n-7), 0.93, 0.41%, 0.96 and 0.64%, respectively (steric acid, C18:0), 0.90, 0.51%, 0.94 and 0.44%, respectively (oleic acid, C18:1n-9), 0.97, 0.25%, 0.98 and 0.29% (linoleic acid, C18:2n-6), 0.68, 0.09%, 0.57 and 0.16% (α-linolenic acid, C18:3n-3) and 0.97, 0.57, 0.97 and 1.22, respectively (iodine value, calculated). The magnitude of this error showed quite good accuracy using these rapid methods in prediction of the moisture and fatty acid composition of fat from pigs involved in breeding schemes.

Keywords

fat moisturefatty acids compositionnear-infrared spectroscopygold reflectorrepeatability file
Type
Full Paper
Information
animal , Volume 5 , Issue 11 , 26 September 2011 , pp. 1829 - 1841
Copyright
Copyright © The Animal Consortium 2011

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

Anderson, S 2007. Determination of fat, moisture, and protein in meat and meat products by using the FOSS FoodScan (TM) near-infrared spectrophotometer with FOSS artificial neural network calibration model and associated database: collaborative study. Journal of AOAC International 90, 1073–1083.CrossRefGoogle Scholar
Anonymous 1974. Nordic committee on food analysis (NMKL). Method no. 23. NMKL Veterinærinstituttet, Oslo, Norway.Google Scholar
Buning-Pfaue, H 2003. Analysis of water in food by near infrared spectroscopy. Food Chemistry 82, 107–115.CrossRefGoogle Scholar
de Pedro, E, Casillas, M, Miranda, CM 1997. Microwave oven application in the extraction of fat from the subcutaneous tissue of Iberian pig ham. Meat Science 45, 45–51.CrossRefGoogle ScholarPubMed
de Pedro, E, Garrido, A, Bares, I, Casillas, M, Murray, I 1992. Application of near infrared spectroscopy for quality control of Iberian pork industry. In Near infrared spectroscopy bridging the gap between data analysis and NIR applications (ed. KI Hildrum, T Isaksson, T Naes and A Tandberg), pp. 345–348. Ellis Horwood, Chichester, UK.Google Scholar
Duarte, FJ 1995. Tunable laser applications. Marcel Dekker, New York, NY, USA.Google Scholar
Esbensen, KH 2000. Multivariate Data Analysis – in practice. An introduction to multivariate data analysis and experimental design. CAMO Process AS, Oslo, Norway.Google Scholar
Fernandez, A, de Pedro, E, Nunez, N, Silio, L, Garcia-Casco, J, Rodriguez, C 2003. Genetic parameters for meat and fat quality and carcass composition traits in Iberian pigs. Meat Science 64, 405–410.CrossRefGoogle ScholarPubMed
Garcia-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, 49–62.CrossRefGoogle Scholar
Garcia-Olmo, J, Garrido-Varo, A, de Pedro, E 2005. Advantages and disadvantages of multiple linear regression and partial least squares regression equations for the prediction of fatty acids. Retrieved April 7, 2011, from http://www.uco.es/organiza/departamentos/prod-animal/p-animales/cerdo-iberico/Bibliografia/p253.pdfGoogle Scholar
Garcia-Olmo, J, de Pedro, E, Garrido, A, Paredes, A, Sanabria, C, Santolalla, M, Salas, J, Garcia-Hierro, JR, Gonzalez, I, Garcia-Cachan, MD, Guirao, J 2002. Determination of the precision of the fatty acid analysis of Iberian pig fat by gas chromatography. Results of a mini collaborative study. Meat Science 60, 103–109.CrossRefGoogle ScholarPubMed
Garrido-Varo, A, García-Olmo, J, Perez-Marin, D 2004. Applications in fats and oils. In Near-infrared spectroscopy in agriculture (ed. CA Roberts, J Workman and JB Reeves III), pp. 487–558. ASA, CSSA and SSSA, Inc., Madison, WI, USA.Google Scholar
Garrido-Varo, A, Perez-Marin, D, Bautista-Cruz, J, Guerrero-Ginel, JE 2008. Near infrared spectroscopy for quantification of animal-origin fats in fat blends. Journal of Near Infrared Spectroscopy 16, 281–283.CrossRefGoogle Scholar
Gjerlaug-Enger, E, Aass, L, Ødegård, J, Vangen, O 2010. Genetic parameters of meat quality traits in two pig breeds measured by rapid methods. Animal 4, 1832–1843.Google Scholar
Gjerlaug-Enger, E, Aass, L, Ødegård, J, Kongsro, J, Vangen, O 2011. Genetic parameters of fat quality in pigs measured by near-infrared spectroscopy. Animal, (in press). doi:10.1017/S1751731111000528.Google ScholarPubMed
Gonzalez-Martin, I, Gonzalez-Perez, C, Hernandez-Mendez, J, Alvarez-Garcia, N, Lazaro, SM 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, 257–268.CrossRefGoogle Scholar
Isaksson, T, Naes, T 1988. The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Applied Spectroscopy 42, 1273–1284.Google Scholar
Isaksson, T, Miller, CE, Naes, T 1992. Nondestructive Nir and Nit determination of protein, fat, and water in plastic-wrapped, homogenized meat. Applied Spectroscopy 46, 1685–1694.Google Scholar
Martens, H, Naes, T 1989. Multivariate calibration. John Wiley & Sons Ltd, Chichester, UK.Google Scholar
Perez-Marin, D, Garrido-Varo, A, de Pedro, E, Guerrero-Ginel, JE 2007. Chemometric utilities to achieve robustness in liquid NIRS calibrations: application to pig fat analysis. Chemometrics and Intelligent Laboratory Systems 87, 241–246.Google Scholar
Perez-Marin, D, Sanz, ED, Guerrero-Ginel, JE, Garrido-Varo, A 2009. A feasibility study on the use of near-infrared spectroscopy for prediction of the fatty acid profile in live Iberian pigs and carcasses. Meat Science 83, 627–633.CrossRefGoogle ScholarPubMed
Rossel, RAV, McBratney, AB 1998. Laboratory evaluation of a proximal sensing technique for simultaneous measurement of soil clay and water content. Geoderma 85, 19–39.Google Scholar
Sato, T, Kawano, S, Iwamoto, M 1991. Near-infrared spectral patterns of fatty-acid analysis from fats and oils. Journal of the American Oil Chemists Society 68, 827–833.CrossRefGoogle Scholar
Schwörer, D, Lorenz, D, Rebsamen, A 1999. Evaluation of the fat score by NIR spectroscopy. Proceedings of the 50th Annual meeting of the European Association for Animal Production, 22–26 August 1999. Commission on pig production, Session P3.27, Zürich, Switzerland.Google Scholar
Shenk, JS, Westerhaus, MO 1995. Analysis of agricultural and food products by near infrared reflectance spectroscopy – monograph. NIR Systems Inc., Silver Spring, MD, USA.Google Scholar
Shenk, JS, Westerhaus, MO 1996. Calibration the ISI way. In Near infrared spectroscopy: the future waves (ed. AMC Davies and PC Williams), pp. 198–202. NIR Publications, Chichester, UK.Google Scholar
Shenk, JS, Workman, JJ, Westerhaus, MO, Burns, DA, Ciurczak, EW 2001. Application of NIR spectroscopy to agricultural products. Handbook of near infrared analysis. Practical spectroscopy series, vol. 27, 2nd edition. Marcel Decker, New York, NY, USA.Google Scholar
Tillmann, P, Paul, C 1998. The repeatability file – a tool for reducing the sensitivity of near infrared spectroscopy calibrations to moisture variation. Journal of Near Infrared Spectroscopy 6, 61–68.CrossRefGoogle Scholar
Williams, PC, Sobering, D 1996. How do we do it: a brief summary of the methods we use in developing near infrared calibrations. In Near infrared spectroscopy: the future (ed. AMC Waves, Davies and PC Williams), pp. 185–188. NIR Publications, Chichester, UK.Google Scholar
WinISI III Manual 2005. Version 1.60. Infrasoft International, State College, PA, USA.Google Scholar