Skip to main content Accessibility help
×
Home
Hostname: page-component-99c86f546-5rzhg Total loading time: 0.236 Render date: 2021-12-03T05:13:04.991Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

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*
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
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 (R2 CAL) 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.

Type
Growth, development and meat science
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: 772777.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: 6467.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: 925931.CrossRefGoogle Scholar
Cowe, I. A. and McNicol, J. W. 1985. The use of principal components in the analysis of near infrared spectra. Applied Spectroscopy 39: 257265.CrossRefGoogle 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: 649659.CrossRefGoogle 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: 814817.CrossRefGoogle Scholar
Downey, G. 1994. Qualitative analysis in the near infrared region. Analyst 119: 23672375.CrossRefGoogle Scholar
Downey, G. 1996. Authentication of food and food ingredients by near infrared spectroscopy. Journal of Near Infrared Spectroscopy 4: 4761.CrossRefGoogle 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: 353363.CrossRefGoogle 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: 894899.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: 335343.CrossRefGoogle 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. 320326.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: 28492855.CrossRefGoogle 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: 8491.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: 8187.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: 6780.CrossRefGoogle 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: 263269.3.0.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: 471474.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. 107159. 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. 1328. 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: 21152120.CrossRefGoogle 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: 501505.CrossRefGoogle Scholar
Prache, S. and Theriez, M. 1999. Traceability of lamb production systems: carotenoids in plasma and adipose tissue. Animal Science 69: 2936.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: 4554.CrossRefGoogle 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: 34083413.CrossRefGoogle 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: 525532.Google Scholar
55
Cited by

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

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

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

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

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

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

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *