Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-26T12:37:52.420Z Has data issue: false hasContentIssue false
  • English
  • Français

Visible/near infrared reflectance spectroscopy for predicting composition and tracing system of production of beef muscle

Published online by Cambridge University Press:  18 August 2016

D. Cozzolino ,
D. De Mattos  and
D. Vaz Martins
D. Cozzolino*
Affiliation:
Instituto Nacional de Investigacion Agropecuaria (INIA), La Estanzuela, Ruta 50, km 11, Colonia, Uruguay
D. De Mattos
Affiliation:
INIA Tacuarembo, Tacuarembo, Uruguay
D. Vaz Martins
Affiliation:
Instituto Nacional de Investigacion Agropecuaria (INIA), La Estanzuela, Ruta 50, km 11, Colonia, Uruguay
*
E-mail:[email protected]
Get access

Abstract

Muscle chemical analysis and muscle identification both were attempted by using visible and near infrared reflectance spectroscopy (NIRS). Seventy-eight beef muscles (m. longissimus dorsi) from Hereford cattle were used. The samples were scanned in a NIRS monochromator instrument (NIRSystems 6500, Silver Spring, MD, USA) in reflectance mode (log 1/R). Both intact and minced muscle presentation to the instrument were explored. Predictive equations were made using ISI software (Infrasoft International, Port Matilda, PA, USA) and muscle identification was performed by Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA). The coefficient of determination in calibration (R2CAL) and standard error in cross validation (SECV) for the intact sample presentation were 009 (SECV: 15·6), 0·89 (SECV: 46·9), 0·48 (SECV: 23·9) for moisture (M), fat and crude protein (CP) on g/kg fresh weight basis respectively. R2CAL and SECV for minced sample presentation were 0·41 (SECV: 161), 0·92 (SECV: 43·4), 0·71 (SECV: 20·5) for M, fat and CP on g/kg fresh weight basis respectively. Qualitative analysis of optical information through PCA and SIMCA analysis showed differences in muscles resulting from two different feeding systems.

Keywords

beef qualitynear infrared reflectance spectroscopy (NIRS)principal component analysissteerstracer techniques
Type
Growth, development and meat science
Information
Animal Science , Volume 74 , Issue 3 , June 2002 , pp. 477 - 484
Copyright
Copyright © British Society of Animal Science 2002

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

Association of Official Analytical Chemists. 1990. Official methods of analysis of the Association of Official Analytical Chemists, 15th edition (ed. Helrich, K.). AOAC, Inc., Arlington, VA.Google Scholar
Barnes, R. J., Dhanoa, M. S. and Lister, S. J. 1989. Standard normal variate transformation and detrending of near infrared diffuse reflectance spectra. Applied Spectroscopy 43: 772–777.CrossRefGoogle 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
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.Google Scholar
Cowe, I. A. and McNicol, J. W. 1985. The use of principal components in the analysis of near infrared spectra. Applied Spectroscopy 39: 257–265.Google Scholar
Delpy, D. T. and Cope, M. 1997. Quantification in tissue near–infrared spectroscopy. Philosophical Transactions of the Royal Society of London, Series B 352: 649–659.Google Scholar
Ding, H. B. and Xu, R. J. 1999. Differentiation of beef and kangaroo meat by visible and near infrared reflectance spectroscopy. Journal of Food Science 64: 814–817.CrossRefGoogle Scholar
Downey, G. 1994. Qualitative analysis in the near infrared region. Analyst 119: 2367–2375.Google Scholar
Downey, G. 1996. Authentication of food and food ingredients by near infrared spectroscopy. Journal of Near Infrared Spectroscopy 4: 47–61.Google Scholar
Downey, G. and Beauchene, D. 1997. Discrimination between fresh and frozen then thawed beef M longissimus dorsi by combined visible and near infrared reflectance spectroscopy: a feasibility study. Meat Science 45: 353–363.Google Scholar
Downey, G., McElhinney, J. and Fearn, T. 2000. Species identification in selected raw homogenised meats by reflectance spectroscopy in the mid-infrared, near-infrared and visible ranges. Applied Spectroscopy 54: 894–899.CrossRefGoogle Scholar
Ellekjaer, M. R. and Isaksson, T. 1992. Assessment of maximum cooking temperatures in previously heat treated beef. 1. Near infrared spectroscopy. Journal of the Science of Food and Agriculture 59: 335–343.Google Scholar
Ellekjaer, M. R., Naes, T., Isaksson, T. and Solheim, R. 1992. Identification of sausages with fat-substitutes using near infrared spectroscopy. In Near infrared spectroscopy: bridging the gap between data analysis and NIR applications (ed. Hildrum, K. I. T.Isaksson, Naes, T. and Tandberg, A.), pp. 320–326.Google Scholar
French, P., Stanton, C., Lawless, F., O´Riordan, E.G., Monahan, F. J., Caffrey, P. J. and Moloney, A. P. 2000. Fatty acid composition, including conjugated linoleic acid, of intramuscular fat from steers offered grazed grass, grass silage or concentrate-based diets. Journal of Animal Science 78: 2849–2855.Google Scholar
Griebenow, R. L., Martz, F. A. and Morrow, R. E. 1997. Forage based beef finishing systems: a review. Journal of Production in Agriculture 9: 84–91.CrossRefGoogle Scholar
Hildrum, K. I., Isaksson, T., Naes, T., Nilsen, B.N, Rodbotten, 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.Google Scholar
Holland, J. K., Kemsley, E. K. and Wilson, R. H. 1998. Use of Fourier transform infrared spectroscopy and partial least squares regression for the detection of adulteration of strawberry purees. Journal of the Science of Food and Agriculture 76: 263–269.3.0.CO;2-F>CrossRefGoogle Scholar
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
Mark, H. 1992. Data analysis: multilinear regression and principal component analysis. In Handbook of near infrared analysis (ed. Burns, D. A. and Ciurczak, E. W.), pp. 107–159. Practical Spectroscopy Series no. 13. Marcel Dekker, Inc.Google Scholar
Martens, H. and Naes, T. 1996. Multivariate calibration. John Wiley and Sons Ltd, New York.Google 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
NIRS 2. 1995. Routine operation and calibration software for near infrared instruments. Perstorp Analytical, Silver Spring, MD, ISI International.Google Scholar
Osborne, B. G., Fearn, T. and Hindle, P. H. 1993. Near infrared spectroscopy in food analysis, second edition. Longman Scientific and Technical.Google 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.Google Scholar
Patterson, R. L. S. and Jones, S. J. 1990. Review of current techniques for the verification of the species origin of meat. Analyst 115: 501–505.CrossRefGoogle ScholarPubMed
Prache, S. and Theriez, M. 1999. Traceability of lamb production systems: carotenoids in plasma and adipose tissue. Animal Science 69: 29–36.CrossRefGoogle Scholar
Sanderson, R., Lister, S. J., Dhanoa, M. S., Barnes, R. J. and Thomas, C. 1997. Use of near infrared reflectance spectroscopy to predict and compare the composition of carcass samples from young steers. Animal Science 65: 45–54.Google Scholar
Sato, T., Kawano, S. and Iwamoto, M. 1990. Detection of foreign fat adulteration of milk by near infrared spectroscopic method. Journal of Dairy Science 73: 3408–3413.Google Scholar
Shenk, J. S. and Westerhaus, M. O. 1993. Analysis of agriculture and food products by near infrared reflectance spectroscopy. Monograph. Infrasoft International, Port Matilda, PA.Google Scholar
Strayer, L. 1995. Biochemistry, fourth edition. Freeman, W. H. and Co., New York.Google Scholar
Thyholdt, K. and Isaksson, T. 1997. Differentiation of frozen and unfrozen beef using near infrared spectroscopy. Journal of the Science of Food and Agriculture 73: 525–532.Google Scholar