Hostname: page-component-7bb8b95d7b-fmk2r Total loading time: 0 Render date: 2024-09-05T06:58:19.608Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of near infrared reflectance spectroscopy to predict and compare the composition of carcass samples from young steers

Published online by Cambridge University Press:  02 September 2010

R. Sanderson ,
S. J. Lister ,
M. S. Dhanoa ,
R. J. Barnes  and
C. Thomas
R. Sanderson
Affiliation:
Insitute of Grassland and Environmental Research, Plas Gogerddan, Aberystwyth SY23 3EB
S. J. Lister
Affiliation:
Insitute of Grassland and Environmental Research, Plas Gogerddan, Aberystwyth SY23 3EB
M. S. Dhanoa
Affiliation:
Insitute of Grassland and Environmental Research, Plas Gogerddan, Aberystwyth SY23 3EB
R. J. Barnes
Affiliation:
NIRSystems, Perstorp Analytical Ltd, Highfield House, Foundation Park, Roxborough Way, Maidenhead SL6 3UD
C. Thomas
Affiliation:
Scottish Agricultural College, Auchincruive, Ayr KA6 5HW
Get access

Abstract

The aim of the current study was to investigate the effects of level of feeding and level offish-meal supplementation on the carcass composition of young steers and in doing so, to assess the potential for employing near infrared reflectance spectroscopy (NIRS) in such studies. In addition to wet chemical techniques, NIRS was used to examine carcass samples from animals offered silage-based diets at one of four levels of feeding ranging from near maintenance to ad libitum and with one of four levels offish meal (0, 50,100 or 150 g/kg silage dry matter).

Wet chemical data indicated an increase in fat concentration (P < 0·001) and decrease in crude protein concentration (P < 0·05) in the fresh carcass in response to increasing level of feeding but no statistically significant effect of level of fish meal. Ash concentration was not affected significantly by either level of feeding or level of fish-meal supplementation. Ground, freeze-dried samples were scanned in the wavelength range 1100 to 2498 nm. Calibration equations for ash, fat and crude protein concentration (g/kg carcass) were derived using a modified partial least-squares regression technique. Equations were found to be superior for fat compared with those for crude protein and ash. Standard errors of calibration (g/kg carcass) and multiple correlation coefficients of 6·96 and 0·42, 6·61 and 0·95 and 4·36 and 0·61 were obtained for ash, fat and crude protein respectively with corresponding standard errors of cross validation of 7·71, 7·82 and 4·96 g/kg carcass respectively. Qualitative analysis of spectral information using multivariate techniques and difference spectra clearly showed differences in carcass composition resulting from the different levels of feeding and less so the different levels offish-meal supplementation.

It is shown, that NIRS can be used both quantitatively and qualitatively to study the effects of nutrition on carcass composition.

Keywords

carcass compositiondifference spectramultivariate analysisnear infrared reflectance spectroscopy (NIRS)steers
Type
Research Article
Information
Animal Science , Volume 65 , Issue 1 , August 1997 , pp. 45 - 54
Copyright
Copyright © British Society of Animal Science 1997

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

Barnes, R. J., Dhanoa, M. S. and Lister, S. J. 1989. Standard normal variate transformation and de-trending 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
Bertholet, M. P. 1859. Violet d'aniline. Repertoire de Chemie Applique 1: 284.Google Scholar
Boever, J. L. de, Cottyn, B.G., Fiems, L. O. and Boucque, Ch. V. 1992. Determination of chemical composition of beef meat by NIRS. In Near infra-red spectroscopy: bridging the gap between data analysis and NIR applications (ed. Hildrum, K. J., Isaksson, T., Njes, T. and Tandberg, A.), pp. 339–344. Ellis Horwood Ltd., Chichester, England.Google Scholar
Clark, D. H. and Short, R. E. 1994. Comparison of AOAC and light spectroscopy analyses of uncooked, ground beef. Journal of Animal Science 72: 925–931.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Cowe, I. A., McNicol, J. W. and Cuthbertson, D. C. 1985. A designed experiment for the examination of techniques used in the analysis of near infrared spectra. 1. Analysis of spectral structure. Analyst 110: 1227–1232.CrossRefGoogle Scholar
Crowther, A. B. and Large, R. S. 1956. Improved conditions for the sodium phenoxide-sodium hypochlorite method for the determination of ammonia. Analyst 81: 64–65.CrossRefGoogle Scholar
Curcio, J. A. and Petty, C. C. 1951. The near infrared absorption spectrum of liquid water. Journal of the Optical Society of America 41:302–304.CrossRefGoogle Scholar
Dawson, J. M., Buttery, P. J., Lammiman, M. J., Soar, J. B., Essex, C. P., Gill, M. and Beever, D. E. 1991. Nutritional and endocrinological manipulation of lean deposition in forage-fed steers. British Journal of Nutrition 66:171–185.CrossRefGoogle ScholarPubMed
Devaux, M. F., Bertrand, D., Robert, P. and Qannari, M. 1988. Application of multidimensional analyses to the extraction of discriminant spectral patterns from NIR spectra. Applied Spectroscopy 42:1015–1019.CrossRefGoogle Scholar
Dhanoa, M. S., Lister, S. J. and Barnes, R. J. 1994a. On the reduction of multicoUinearity in near-infrared spectroscopic data. In Proceedings of the XVIIth international biometric conference, Hamilton, Ontario, volume 2, p. 241.Google Scholar
Dhanoa, M. S., Lister, S. J. and Barnes, R. J. 1995. On the scales associated with near-infrared reflectance difference spectra. Applied Spectroscopy 49: 765–772.CrossRefGoogle Scholar
Dhanoa, M. S., Lister, S. J., Sanderson, R. and Barnes, R. J. 1994b. The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. Journal of Near Infrared Spectroscopy 2:43–47.CrossRefGoogle Scholar
Docherty, A. C. 1964. Analysis of fertilisers by the autoanalyser. In Automation in analytical chemistry, Technicon international symposium, New York, Technicon, Ardsley, New York.Google Scholar
Ellekjær, 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.CrossRefGoogle Scholar
El Sahat, A. A. and Afify, W. M. 1995. The influence of slaughter weight on sheep carcass lipids. In Proceedings of the 46th annual meeting of the European Association for Animal Production, Prague, p. 236.Google Scholar
Evans, D. G., Scotter, C. N. G., Day, L. Z. and Hall, M. N. 1993. Determination of the authenticity of orange juice by discriminant analysis of near infrared spectra. Journal of Near Infrared Spectroscopy 1:33–44.CrossRefGoogle Scholar
Geladi, P., MacDougall, D. and Martens, H. 1985. Linearization and scatter correction for near-infrared reflectance spectra of meat. Applied Spectroscopy 39:491–500.CrossRefGoogle Scholar
Genstat 5 committee. 1987. Genstat 5 reference manual. Clarendon Press, Oxford.Google Scholar
Gower, J. C. and Ross, G. J. S. 1969. Minimum spanning trees and single linkage cluster analysis. Applied Statistics 18:54–64.CrossRefGoogle Scholar
Hildrum, K. I., Nilsen, B. N., Mielnik, M. and Næs, T. 1994. Prediction of sensory characteristics of beef by near-infrared spectroscopy. Meat Science 38: 67–80.CrossRefGoogle ScholarPubMed
Isaksson, T. and Næs, T. 1988. The effect of multiplicative scatter correction (MSC) and linearity improvement in MR spectroscopy. Applied Spectroscopy 42:1273–1284.CrossRefGoogle Scholar
Isaksson, T. and Næs, T. 1992. Segmented calibration. In Making light work: advances in near infrared spectroscopy (ed. Murray, I. and Cowe, I. A.), pp. 140–146. VCH, Weinheim.Google Scholar
Kruggel, W. G., Field, R. A., Riley, M. L., Radloff, H. D. and Horton, K. M. 1981. Near-infrared reflectance determination of fat, protein, and moisture in fresh meat. Journal of the Association of Official Analytical Chemists 64: 692–696.Google 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
Lister, S. J., Dhanoa, M. S., Mueller-Harvey, I. and Reed, J. D. 1995. The investigation of varietal differences among sorghum crop residues using near infrared reflectance spectroscopy. In Frontiers in analytical spectroscopy (ed. Andrews, D. L. and Davies, A. M. C.), pp. 117–122. The Royal Society of Chemistry, Cambridge.Google Scholar
Mahalanobis, P. C. 1936. On the generalized distance in statistics. Proceedings of the National Institute of Science, India 2:49–55.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
Murray, I. 1987. The NIR spectra of homologous series of organic compounds. In Near infrared diffuse reflectance transmittance spectroscopy (ed. Hollo, J., Kaffka, K. J. and Gonczy, J. L.), pp. 13–28. Akadémiai Kiadó, Budapest.Google Scholar
Murray, I. and Williams, P. C. 1987. Chemical principles of near infra-red technology. In Near infrared technology in the agricultural and food industries (ed. Williams, P. C. and Norris, K. H.), pp. 17–34. American Association of Cereal Chemists, St. Paul, Minnesota, USA.Google Scholar
Osborne, B. G. and Fearn, T. 1986. Near-infrared spectroscopy in food analysis. Longman Scientific and Technical, New York.Google Scholar
Sanderson, R., Thomas, C. and McAllan, A. B. 1992. Fish-meal supplementation of grass silage given to young growing steers: effect on intake, apparent digestibility and live-weight gains. Animal Production 55: 389–396.Google Scholar
Shenk, J. S. and Westerhaus, M. O. 1991. Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Science 31: 469–474.CrossRefGoogle Scholar
Slaughter, D. C., Klueter, H. H., Mitchell, A. D. and Norris, K. H. 1990. Near infrared reflectance of ground hog carcass composition. In The proceedings of the second international near infrared spectroscopy conference (ed. Iwamoto, M. and Kawano, S.), pp. 125–129. Korin Publishing Co., Tokyo.Google Scholar
Steverink, A. T. G. and Steunenberg, H. 1991. Determination of the composition of whole rabbit carcasses by means of near-infrared reflectance spectroscopy (MRS). In Proceedings of the third international conference on near infrared spectroscopy (ed. Biston, R. and Bartiaux-Thill, N.), pp. 637–640. Agricultural Research Publishing, Gembloux, Belgium.Google Scholar
Stone, M. 1974. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, B 36:111–133.Google Scholar
Tetlow, J. A. and Wilson, A. L. 1964. An absorptiometric method for determining ammonia in boiler feed-water. Analyst 89: 453–465.CrossRefGoogle Scholar
Williams, P. C. and Norris, K. H. 1987. Near-infrared technology in the agricultural and food industries American Association of Cereal Chemists, St. Paul, Minnesota, USA.Google Scholar
Woodward, C. J. H., Trayhurn, P. and James, W. P. T. 1976. The rapid determination of carcass fat by the Foss-let specific gravity technique. British Journal of Nutrition 36: 567–570.CrossRefGoogle ScholarPubMed