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Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy

Published online by Cambridge University Press:  09 March 2007

N. Barlocco ,
A. Vadell ,
F. Ballesteros ,
G. Galietta  and
D. Cozzolino
N. Barlocco
Affiliation:
Cátedra de Suinos, Centro Regional Sur, Facultad de Agronomía, Universidad de la República, UDELAR, Av. Garzón 780, Código Postal 12900, Montevideo, Uruguay
A. Vadell
Affiliation:
Cátedra de Suinos, Centro Regional Sur, Facultad de Agronomía, Universidad de la República, UDELAR, Av. Garzón 780, Código Postal 12900, Montevideo, Uruguay
F. Ballesteros
Affiliation:
Unidad Tecnología de los Alimentos, Facultad de Agronomía, Universidad de la República, UDELAR, Montevideo, Uruguay
G. Galietta
Affiliation:
Unidad Tecnología de los Alimentos, Facultad de Agronomía, Universidad de la República, UDELAR, Montevideo, Uruguay
D. Cozzolino*
Affiliation:
Australian Wine Research Institute, PO Box 197, Glen Osmond, SA 5064, Australia
*
†E-mail: Daniel.cozzolino@awri.com.au
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Abstract

Partial least-squares (PLS) models based on visible (Vis) and near infrared reflectance (NIR) spectroscopy data were explored to predict intramuscular fat (IMF), moisture and Warner Bratzler shear force (WBSF) in pork muscles (m. longissimus thoracis) using two sample presentations, namely intact and homogenized. Samples were scanned using a NIR monochromator instrument (NIRSystems 6500, 400 to 2500 nm). Due to the limited number of samples available, calibration models were developed and evaluated using full cross validation. The PLS calibration models developed using homogenized samples and raw spectra yielded a coefficient of determination in calibration (R2) and standard error of cross validation (SECV) for IMF (R2=0·87; SECV=1·8 g/kg), for moisture (R2=0·90; SECV=1·1 g/kg) and for WBSF (R2=0·38; SECV=9·0 N/cm). Intact muscle presentation gave poorer PLS calibration models for IMF and moisture (R2<0·70), however moderate good correlation was found for WBSF (R2=0·64; SECV=8·5 N/cm). Although few samples were used, the results showed the potential of Vis-NIR to predict moisture and IMF using homogenized pork muscles and WBSF in intact samples.

Keywords

fatinfrared spectroscopymeat qualitymoisture
Type
Research Article
Information
Animal Science , Volume 82 , Issue 1 , February 2006 , pp. 111 - 116
Copyright
Copyright © British Society of Animal Science 2006

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References

Association of Official Analytical Chemists. 1990. Official methods of analysis of the Association of Official Analytical Chemists, 15th edition. Association of Official Analytical Chemists, Inc., Arlington, VA.Google Scholar
Ben-Gera, I. and Norris, K. H. 1968. Direct spectrophotometric determination of fat and moisture in meat products. Journal of Food Science 33: 64–67.CrossRefGoogle Scholar
Brøndum, J., Munck, L., Henckel, P., Karlsson, A., Tornberg, E. and Engelsen, S. B. 2000. Prediction of water holding capacity and composition of porcine meat by comparative spectroscopy. Meat Science 55: 177–185.CrossRefGoogle ScholarPubMed
Byrne, C. E., Downey, G., Troy, D. J. and Buckley, D. J. 1998. Non-destructive prediction of selected attributes of beef by near-infrared reflectance spectroscopy between 750 and 1098 nm. Meat Science 49: 399–409.CrossRefGoogle ScholarPubMed
Chrystall, B. B., Culioli, J., Demeyer, D., Honikel, K. O., Møller, O. J., Purslow, P., Schwagele, F., Shorthose, R. and Uytterhae-gen, J. 1994. Recommendation of reference methods for assessment of meat tenderness. Proceedings of 40th international congress of meat science and technology. P. S–V06. The Hague, The Netherlands.Google Scholar
Clark, D. H. and Short, R. E. 1994. Comparison of AOAC and light spectroscopy analysis of uncooked ground beef. Journal of Animal Science 72: 925–931.CrossRefGoogle ScholarPubMed
Cozzolino, D., Brito, G. and San Julian, R. 2003. The use of near infrared reflectance spectroscopy to assess tenderness, colour and pH in longissimus muscle. Proceedings of the 48th international congress of meat science and technology, Rome, pp. 798–799.Google Scholar
Cozzolino, D. and 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: 37–44.CrossRefGoogle Scholar
Cozzolino, D., Murray, I., Scaife, J. R. and Paterson, R. 2000. Study of dissected lamb muscles by visible and near infrared reflectance spectroscopy for composition assessment. Animal Science 70: 417–423.CrossRefGoogle Scholar
Davis, C. E., Birth, G. S. and Townsend, W. E. 1978. Analysis of spectral reflectance for measuring pork quality. Journal of Animal Science 46: 634–638.CrossRefGoogle Scholar
Eggert, J. M., Depreux, F. F. S., Schinckel, A. P., Grant, A. L. and Gerrard, D. E. 2002. Myosin heavy chain isomorphs account for variation in pork quality. Meat Science 61: 117–126.CrossRefGoogle Scholar
Fernández, X., Monin, G., Talmant, A., Mourot, J. and Lebret, B. 1999. Influence of intramuscular fat 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
Folch, J., Less, M. and Stanley, G. H. S. 1957. A simple method for the isolation and purification of total lipids in animal tissue. Journal of Biological Chemistry 226: 497–501.CrossRefGoogle Scholar
Geesink, G. H., Schreutelkamp, F. H., Frankhuizen, R., Vedder, H. W., Faber, N. M., Kranen, R. W. and Gerritzen, M. A. 2003. Prediction of pork quality attributes from near infrared reflectance spectra. Meat Science 65: 661–668.CrossRefGoogle ScholarPubMed
Hildrum, K. I., Isaksson, T., Naes, T., Nilsen, B. N., Rødbotten, M. and Lea, P. 1995. Near infrared reflectance spectroscopy in the prediction of sensory properties of beef. Journal of Near Infrared Spectroscopy 3: 81–87.CrossRefGoogle Scholar
Hildrum, K. I., Nilsen, B. N., Mielnik, M. and Naes, T. 1994. Prediction of sensory characteristics of beef by near infrared spectroscopy. Meat Science 38: 67–80.CrossRefGoogle ScholarPubMed
Honikel, K. O. 1998. Reference methods for the assessment of physical characteristic of meat. Meat Science 49: 447–457.CrossRefGoogle ScholarPubMed
Lanza, E. 1983. Determination of moisture, protein, fat and calories in raw pork, and beef by near infrared spectroscopy. Journal of Food Science 48: 471–474.CrossRefGoogle Scholar
Lawrie, R. A. 1985. Meat science, fourth edition. Pergamon Press, Oxford.Google Scholar
Leroy, B., Lambotte, S., Dotreppe, O., Lecocq, H., Istasse, L. and 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.CrossRefGoogle Scholar
McCaig, T. N. 2002. Extending the use of visible/near infrared reflectance spectrophotometers to measure colour of food and agricultural products. Food Research International 35: 731–736.CrossRefGoogle Scholar
Martens, H. and Dardenne, P. 1998. Validation and verification of regression in small data sets. Chemometrics and Intelligent Laboratory Systems 44: 99–106.CrossRefGoogle Scholar
Martens, H. and Martens, M. 2000. Multivariate analysis of quality: an introduction. John Wiley and Sons, Chichester.Google Scholar
Martens, H. and Naes, T. 1996. Multivariate calibration. John Wiley and Sons Ltd, New York.Google Scholar
Mitsumoto, M., Maeda, S., Mitsuhashi, T. and Ozawa, S. 1991. Near infrared spectroscopy determination of physical and chemical characteristics in beef cuts. Journal of Food Science 56: 1493–1496.CrossRefGoogle Scholar
Monin, G. 1998. Recent methods for predicting quality in whole meat Meat Science 49: S231–S243CrossRefGoogle Scholar
Mörlein, D., Rosner, F., Brand, S., Jenderka, K.-V. and Wicke, M. 2005. Non-destructive estimation of intramuscular fat content of the longissimus muscle of pork by means of spectral analysis of ultrasound echo signals. Meat Science 69: 187–199.CrossRefGoogle Scholar
Murray, I. 1986. The NIR spectra of homologous series of organic compounds. In NIR/NIT conference (ed. Hollo, J., Kaffka, K. J. and Gonczy, J. L.), pp. 13–28. Akademiai Kiado, Budapest.Google Scholar
Naes, T., Isaksson, T., Fearn, T. and Davies, T. 2002. A user-friendly guide to multivariate calibration and classification. NIR Publications, Chichester, UK.Google Scholar
Oeckel, M. J., van, Warnants, N., Boucque Ch., V. 1999. Pork tenderness estimation by taste panel, Warner-Bratzler shear force and on-line methods. Meat Science 53: 259–267.CrossRefGoogle ScholarPubMed
Rødbotten, R., Nilsen, B. N. and Hildrum, K. I. 2000. Prediction of beef quality attributes from early post mortem near infrared reflectance spectra. Food Chemistry 69: 42–436.CrossRefGoogle Scholar
Park, B., Chen, Y. R., Hruschka, W. R., Shackelford, S. D. and Koohmaraie, M. 1998. Near infrared reflectance analysis for predicting beef longissimus tenderness. Journal of Animal Science 76: 2115–2120.CrossRefGoogle ScholarPubMed
Strayer, L. 1995. Biochemistry, fourth edition. W.H. Freeman and Co., New York.Google Scholar
Swatland, H. J. 1995. On line evaluation of meat. Technomic Publishing Co., Lancaster, USA.Google Scholar
Swatland, H. J. 1986a. Post-mortem spectrophotometry of color intensity of pork and beef using quartz optical fibres. Meat Science 17: 97–106.CrossRefGoogle Scholar
Swatland, H. J. 1986b. Color measurements on pork and veal carcasses by fiber optic spectrophotometry. Canadian Institute of Food Science and Technology 19: 170–173.CrossRefGoogle Scholar
The Unscrambler. 1996. User's guide, version 6.0. CAMO AS, Trondheim, Norway.Google Scholar
Vedder, H. W., Merks, J. W. M., Klein, W. J. H., de Reimert, H. G. M., Frankhuizen, R., Broek, W. H. A. M., van den Lambooij, E. E. 2005. Perspective of NIRS measurement early post-mortem for prediction of pork quality. Meat Science 69: 417–423.Google Scholar
Venel, C., Mullen, A. M., Downey, G. and Troy, D. 2001. Prediction of tenderness and other quality attributes of beef by near infrared reflectance spectroscopy between 750 and 1100 nm: further studies. Journal of Near Infrared Spectroscopy 9: 185–198.CrossRefGoogle Scholar
Williams, P. C. 2001. Implementation of near infrared technology. In New infrared technology in the agricultural and food industries (ed. Williams, P. C. and Norris, K. H.), pp. 145–171. American Association of Cereal Chemists, St Paul, MN.Google Scholar