Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-24T01:29:17.439Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparison of accuracy of intramuscular fat prediction in live pigs using five different ultrasound intensity levels

Published online by Cambridge University Press:  01 August 2009

I. Bahelka ,
M. Oravcová ,
D. Peškovičová ,
J. Tomka ,
E. Hanusová ,
R. Lahučký  and
P. Demo
I. Bahelka*
Affiliation:
Slovak Agricultural Research Centre – Research Institute for Animal Production, 949 92 Nitra, Slovak Republic
M. Oravcová
Affiliation:
Slovak Agricultural Research Centre – Research Institute for Animal Production, 949 92 Nitra, Slovak Republic
D. Peškovičová
Affiliation:
Slovak Agricultural Research Centre – Research Institute for Animal Production, 949 92 Nitra, Slovak Republic
J. Tomka
Affiliation:
Slovak Agricultural Research Centre – Research Institute for Animal Production, 949 92 Nitra, Slovak Republic
E. Hanusová
Affiliation:
Slovak Agricultural Research Centre – Research Institute for Animal Production, 949 92 Nitra, Slovak Republic
R. Lahučký
Affiliation:
Slovak Agricultural Research Centre – Research Institute for Animal Production, 949 92 Nitra, Slovak Republic
P. Demo
Affiliation:
Slovak Agricultural Research Centre – Research Institute for Animal Production, 949 92 Nitra, Slovak Republic
*
†E-mail: [email protected]
Get access

Abstract

The objective of this study was to evaluate the possibility of prediction of intramuscular fat (IMF) in live pigs using ultrasound method. Moreover, the accuracy of prediction at five different ultrasound intensity levels was investigated. Cross-sectional images of longissimus dorsi muscle (LD) at right last rib area, from hybrid pigs, were taken. Each pig was scanned at the same frequency (3.5 MHz) and at the five different ultrasound intensity levels 70%, 75%, 80%, 85% and 90% of total amplifying of sonograph, using the device ALOKA SSD-500. The video image analysis was used to predict IMF content (ultrasound intramuscular fat (UIMF) 70 to UIMF90). The second day after slaughter, the dissection of right half carcass was done. A sample of LD at the last rib was taken for laboratory analysis of IMF content (LAIMF). Scatter plots with UIMF on the x-axis and LAIMF on the y-axis were constructed to account for individual variability within and between intensity levels. Correlations between LAIMF and UIMF were significantly different from zero (r = 0.40–0.52), except for correlation between LAIMF and UIMF90 (r = 0.14). Statistical model with LAIMF (the dependent variable), UIMF (the same model for each intensity level), live weight (the covariates) and sex (the fixed effect) was developed. Coefficients of determination (R2) were 0.33, 0.38, 0.34, 0.25 and 0.17 with UIMF at the intensity level 70%, 75%, 80%, 85% and 90%. Root mean square errors ranged from 0.516% to 0.639%. Standard errors of individual prediction ranged from 0.523% to 0.649%. Goodness-of-fit of the model was also justified by testing the residuals for normality. Although the results are not quite unequivocal in favour of the one intensity level, it seems that intensity levels 75% and 80% are the most suitable to predict IMF in live pigs. Further research is needed, mainly to increase accuracy of collecting, processing and evaluating the sonograms using video image analysis.

Keywords

pigintramuscular fatpredictionultrasound intensity levelcorrelations
Type
Full Paper
Information
animal , Volume 3 , Issue 8 , August 2009 , pp. 1205 - 1211
Copyright
Copyright © The Animal Consortium 2009

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

Bahelka, I, Demo, P, Tomka, J, Lahučký, R 2006. Prediction of intramuscular fat proportion at pigs in vivo by ultrasound method. Conference Biotechnology 2006, February 15–16, České Budějovice, Czech Republic, pp. 141–143.Google Scholar
Bahelka, I, Tomka, J, Hanusová, E 2007. The effects of probe type and intensity of ultrasound on accuracy of intramuscular fat prediction in longissimus dorsi muscle of pigs. Biotechnology in Animal Husbandry 23, 87–95.CrossRefGoogle Scholar
Barton-Gade, PA 1990. Pork quality in genetic improvement programmes – the Danish experience. Record of Proceedings National Swine Improvement Federation and Annual Meeting. Des Moines, Iowa, USA.Google Scholar
Bejerholm, C, Barton-Gade, PA 1986. Effect of intramuscular fat level on eating quality of pig meat. 32nd European congress of meat research workers, August 24–26, Ghent, Belgium.Google Scholar
Bergsma, R 2004. Added value of carcass measurements in EBV. International Workshop on data management and genetic evaluation in pigs, April 16–17, Domžale, Slovenia.Google Scholar
Cameron, ND 1990. Genetic and phenotypic parameters for carcass traits, meat and eating quality traits in pigs. Livestock Production Science 26, 119–135.Google Scholar
de Vries, AG, van der Wal, PG, Long, T, Eikelenboom, G, Merks, JWM 1994. Genetic parameters of pork quality and production traits in Yorkshire populations. Livestock Production Science 40, 277–289.CrossRefGoogle Scholar
Dion, ND, Pettigrew, AM, Daigle, JP 1996. Efficiency of various live measurements for the prediction of lean yield and marbling. Record of Proceedings National Swine Improvement Federation Meeting. Ontario, Canada.Google Scholar
Eggert, JM, Depreux, FFS, Schinkel, AP, Grant, AL, Gerrard, DE 2002. Myosin heavy chain isomorphs account for variation in pork quality. Meat Science 61, 117–126.CrossRefGoogle Scholar
Eggert, JM, Schinckel, AP 1998. Growth and characterization of individual backfat layer in swine: a new method for the prediction of intramuscular fat in the live animal or carcass. Record of Proceedings National Swine Improvement Federation Conference and Annual Meeting, vol. 23, December 4–5, NSIF. East Lansing Marriot, MI, USA. Retrieved July 25, 2005, from http://www.nsif.com/Conferences/1998/Content/htmGoogle Scholar
Faucitano, L, Huff, P, Teuscher, F, Gariepy, C, Wegner, J 2005. Application of computer image analysis to measure pork marbling characteristics. Meat Science 69, 537–543.CrossRefGoogle ScholarPubMed
Fernandez, X, Monin, G, Talmant, A, Mourot, J, Lebret, B 1999. Influence of IMF content on the quality of pig meat – 1. Composition of the lipid fraction and sensory characteristics of m. longissimus lumborum. Meat Science 53, 59–65.CrossRefGoogle ScholarPubMed
Hodgson, RR, Davis, GW, Smith, GC, Savell, JW, Cross, HR 1991. Relationship between pork loin palatability traits and physical characteristics of cooked chops. Journal of Animal Science 69, 4858–4865.CrossRefGoogle ScholarPubMed
Huff-Lonergan, E, Baas, TJ, Malek, M, Dekkers, JCM, Prusa, K, Rothshild, MF 2002. Correlations among selected pork quality traits. Journal of Animal Science 80, 617–627.CrossRefGoogle ScholarPubMed
Knapp, P, William, A, Sölkner, J 1997. Genetic parameters for lean meat content and meat quality traits in different pig breeds. Livestock Production Science 52, 69–73.CrossRefGoogle Scholar
Laboratory Imaging 2005. LUCIA – User’s guide, System for image processing and analysis. Prague, Czech Republic.Google Scholar
Leaflet, AS, Schwab, CR, Baas, TJ 2006. Development of a model to predict intramuscular fat in live pigs using real-time ultrasound. Iowa State University Animal Industry Report. Retrieved January 10, 2008, from http://www.ans.iastate.edu/report/air/2006pdf/R2050.pdfCrossRefGoogle Scholar
Leroy, B, Lambotte, S, Dotreppe, O, Lecocq, H, Istasse, L, Clinquart, A 2003. Prediction of technological and organoleptic properties of beef longissimus thoracis from near infrared reflectance and transmission spectra. Meat Science 66, 45–54.Google Scholar
Liu, EC, Berger, PJ, Goodwin, RN, Christian, LL 1998. Simultaneous estimation of genetic parameters for 38 traits of growth, carcass, meat quality and eating quality. Joint Meeting Abstracts, 78.Google Scholar
Lutjens, A, Kalm, E 1995. Zusammenhang zwischen analytischen und sensorischen Fleischbeschaffenheitsparametern. Fleischwirtschaft 75, 484–491.Google Scholar
Meyer, E 1991. Technologische und sensorische Bewertung der Fleischbeschaffenheit praktischer Mehrfachkreuzungen und Hybriden unter Berücksichtigung des intramuskulären Fettgehaltes. Thesis, Göttingen University.Google Scholar
Monin, G 1998. Recent methods for predicting quality in whole meat. Meat Science 49, S231–S243.CrossRefGoogle Scholar
Mörlein, D, Rosner, F, Brand, S, Jenderka, KV, Wicke, M 2005. Non-destructive estimation of the intramuscular fat content of the longissimus muscle of pigs by means of spectral analysis of ultrasound echo signals. Meat Science 69, 187–199.CrossRefGoogle ScholarPubMed
Newcom, DW, Baas, TJ, Lampe, JF 2002. Prediction of intramuscular fat percentage in live swine using real-time ultrasound. Journal of Animal Science 80, 3046–3052.Google Scholar
Newcom, DW, Baas, TJ, Stalder, KJ 2003. Genetics of pork quality. Retrieved October 17, 2007, from http://www.nsif.com/conferences/2003/pdf%5CGeneticsPorkQuality.pdfGoogle Scholar
Newcom, DW, Baas, TJ, Stalder, KJ, Schwab, CR 2005. Comparison of three models to estimate breeding values for percentage of loin intramuscular fat in Duroc swine. Journal of Animal Science 83, 750–756.Google Scholar
NPPC 1995. Genetic evaluation/Terminal line program results (ed. R Goodwin and S Burroughs). National Pork Production Council, Des Moines, Iowa, USA.Google Scholar
Ragland, KD 1998. Assessment of intramuscular fat, lean growth, and lean composition using real-time ultrasound. PhD, Iowa State University, Ames.Google Scholar
Ragland, K, Brondum, J, Baas, T, Christian, L 1997. Prediction of intramuscular fat in live swine using real-time ultrasound. Record of Proceedings National Swine Improvement Federation Conference and Annual Meeting, vol. 22, December 5–6, Des Moines, Iowa, USA.Google Scholar
Ragland, KD, Christian, LL, Baas, TJ, Amin, VR 2002. Efficacy of using real-time ultrasound to predict intramuscular fat in the longissimus muscle of live swine. Retrieved August 21, 2004, from http://www.ext.vt.edu/news/periodicals/livestock/ap-02_12/aps-171.htmlGoogle Scholar
Rosner, F, von Lengerken, G, Maak, S 2003. The value of pig breeding herds in Germany and progress in improvement of meatiness and pork quality. Animal Science Papers and Reports 21 (suppl. 1), 153–161.Google Scholar
SAS/STAT 2002–2003. SAS Institute Inc., version 9.1.3. SAS, Cary, NC, USA.Google Scholar
Schwab, CR, Baas, TJ, Berry, NL, Mote, BE, Stalder, KJ 2005. Selection for intramuscular fat in Duroc swine – an update. Retrieved April 8, 2007, from http://www.nsif.com/conferences/2005/pdf%5CSelectionIntramuscularFat.pdfGoogle Scholar
Schwörer, DA, Rebsamen, A, Lorenz, D 1995. Selection of intramuscular fat in Swiss pig breeds and the importance of fatty tissue quality. The 2nd Dummerstorf Muscle Workshop on Growth and Meat Quality, May 17–19, Rostock, Germany.Google Scholar
Suzuki, K, Kadowaki, H, Shibata, T, Uchida, H, Nishida, A 2005. Selection for daily gain, loin-eye area, backfat thickness and intramuscular fat based on desired gains over seven generations of Duroc pigs. Livestock Production Science 97, 193–202.Google Scholar
van Oeckel, MJ, Warnants, N, Boucque, CHV 1999. Pork tenderness estimation by taste panel, Warner-Bratzler shear force and on-line methods. Meat Science 53, 259–267.Google Scholar
von Rohr, P, Hofer, A, Künzi, N 1999. Economic values for meat quality traits in pigs. Journal of Animal Science 77, 2633–2640.CrossRefGoogle ScholarPubMed
Walstra, P, Merkus, GSM 1996. Procedure for assessment of the lean meat percentage as a consequence of the new EU reference dissection method in pig carcass classification. Report ID-DLO 96014. DLO – Research Institute for Animal Science and Health, Lelystad, The Netherlands, p. 22.Google Scholar
Whittaker, AD, Park, B, Thane, BR, Miller, RK, Sawell, JW 1992. Principles of ultrasound and measurement of intramuscular fat. Journal of Animal Science 70, 942–952.CrossRefGoogle ScholarPubMed
Wicke, M, Heylen, K, von Lengerken, G 1996. Ergebnisse topographischer Untersuchungen zum intramuskulären Fettgehalt (IMF) des M. longissimus des Schweines. IMF-Kolloquium der Thüringischen Landesanstalt für Landwirtschaft, October 22–23, Wilhelmsthal, Germany.Google Scholar