Prediction of Crude Protein, Extractable Fat, Calcium and Phosphorus Contents of Broiler Chicken Carcasses Using Near-infrared Reflectance Spectroscopy

  • Kadim, I.T. (Department of Animal and Veterinary Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University) ;
  • Mahgoub, O. (Department of Animal and Veterinary Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University) ;
  • Al-Marzooqi, W. (Department of Animal and Veterinary Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University) ;
  • Annamalai, K. (Department of Animal and Veterinary Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University)
  • Received : 2004.09.26
  • Accepted : 2005.02.28
  • Published : 2005.07.01


Near-infrared reflectance spectroscopic (NIRS) calibrations were developed for accurate and fast prediction of whole broiler chicken carcass composition. The Feed and Forage Foss systems Model 5000 Reflectance Transport Model 5000 with near-infrared reflectance spectroscopy (NIRS)-WinISI II windows software was used for this purpose. One equation was developed for the prediction of each carcass component. One hundred and fifty freeze dried broiler whole carcass samples were ground in a Cyclotech 1,093 sample mill and analyzed for dry matter, protein, fat, calcium and phosphate. Samples were divided into two sets: a calibration set from which equations were derived and a prediction set used to validate these equations. The chemical analysis values (mean${\pm}$SD) were calculated based on dry matter basis as follows: dry matter: 33.41${\pm}$2.78 (range: 26.41-43.47), protein: 54.04${\pm}$6.63 (range: 36.20-76.09), fat 35.44${\pm}$8.34 (range: 7.50-55.03), calcium 2.55${\pm}$0.65 (range: 0.99-4.41), phosphorus 1.38${\pm}$0.26 (range: 0.60-2.28). One hundred and three samples were used to calibrate the equations and prediction values. The software used was modified to obtain partial least square regression statistics, as it is the most suitable for natural products analysis. The coefficients of determination ($R^2$) and the standard errors of prediction were 0.82 and 1.83 for the dry matter, 0.96 and 1.98 for protein, 0.99 and 1.07 for fat, 0.90 and 0.30 for calcium and 0.91 and 0.11 for phosphorus, respectively. The present study indicated that NIRS can be calibrated to predict the whole broiler carcass chemical composition, including minerals in a rapid, accurate, and cost effective manner. It neither requires skilled operators nor generates hazardous waste. These findings may have practical importance to improve instrumental procedures for quick evaluation of broiler carcass composition.


Near-infrared Reflectance Spectroscopy;Carcass Composition;Broilers


  1. AOAC. 2000. Official Methods for Analysis, 17th ed. Association of Official Analytical Chemsits, Arlington, VA.
  2. De Boever, J. L., W. Eeckhout and C. V. Boucque. 1994. The possibilities of near infrared reflection spectroscopy to predict total phosphorus, phytate phosphorus and phytase activity in vegetable feedstuff. Neth. J. Agric. Sci. 42:357-361.
  3. Min, B. J. and S. K. Lee. 2004. Surimi quality from mechanically deboned meat as affected by washing cycle, salt concentration, heating temperature and rate. Asian-Aust. J. Anim. Sci. 17:131-136.
  4. Saiga, S., T. Sasaki, K. Nonaka, K. Takahashi, M. Watanabe and K. Watanabe. 1989. Prediction of mineral concentrations of orchardgrass (Datylis glomerata L.) with near infrared reflectance spectroscopy. J. Jpn. Soc. Gra. Sci. 35:228-233.
  5. Valdes, E. V. and J. D. Summers. 1986. Determination of crude protein and fat in carcass and breast muscle samples of poultry by NIR reflectance spectroscopy. Poult. Sci. 65:485-490.
  6. Vasquez de Aldana, B. R. B., B. Garcia, A. Garcia-Criado and M. E. Perez-Corona. 1995. Estimation of mineral content in natural grasslands by near infrared reflectance spectroscopy. Comm. Soil Sci. Plant Anal. 26:1383-1396.
  7. Renden, J. A., S. S. Oates and R. B. Reed. 1986. Determination of body fat and moisture in Dwarf hens with near-infrared reflectance spectroscopy. Poult. Sci. 65:1539-1541.
  8. Abeni, F. and G. Bergoglio. 2001. Characterization of different strains of broiler chicken by carcass measurements, chemical and physical parameters and NIRS on breast muscle. Meat Sci. 57:133-137.
  9. Foley, W. J., A. McIlwee, I. Lawler, L. Aragones, A. P. Woolnough and N. Berdig. 1998. Ecological applications of near infrared reflectance spectroscopy: A tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance. Oecologia. 116:293-305.
  10. Smith, T. N., G. M. Pesti, R. I. Bakalli, J. Kiburn and H. M. Edwards. 2001. The use of near-infrared reflectance spectroscopy to predict the moisture, nitrogen, calcium, total phosphorus, gross energy and phytate phosphorus contents of broiler excreta. Poult. Sci. 80:314-319.
  11. Cozzolino, D., I. Murray and R. Paterson. 1996. Visible and near infrared reflectance spectroscopy for the determination of moisture, fat and protein in chicken breast and thigh muscle. J. Near Infra. Spec. 4:213-223.
  12. Murray, I. and P. C. Williams. 1987. Chemical principles of nearinfrared technology. In: Near-infrared technology in the agricultural and food industries (Ed. P. William and K. Norris). St. Paul, MN: American Association of cereal chemists. pp. 17-34.
  13. Norris, K. H., R. F. Barnes, J. E. Moore and J. S. Shenk. 1976. Predicting forage quality by near-infrared reflectance spectroscopy. J. Anim. Sci. 43:889-897.
  14. Clark, D. H., M. H. Ralphs and R. C. Lamb. 1987. Total alkaloid determinations in larkspur and lupine with near infrared reflectance spectroscopy. Agro. J. 79:481-285.
  15. Chen, H. and B. P. Marks. 1998. Visible/near-infrared spectroscopy for physical characteristics of cooked chicken patties. J. Food Sci. 63:279-282.
  16. Fontaine, J., B. Schirmer and J. Horr. 2002. Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino avid contents. 2. Results for wheat, barely, corn, tricale, wheat bran/middlings, rice bran and sorghum. J. Agric. Food Chem. 50:3902-3911.
  17. Dealdana, B. R., B. G. Criado, A. G. Cuidad and M. E. Corona. 1995. Estimation of mineral-content in natural grasslands by near-infrared reflectance spectroscopy. Comm. Soil Sci. Plant Anal. 26:1238-1396.
  18. Shenk, J. S., J. J. Workman and M. O. Westerhaus. 1992. Application of NIR spectroscopy to agricultural products. In: Handbook of Near-Infrared Analysis. (Ed. A. Burns and E. W. Ciurczak). Marcel dekker, New York, NY. pp. 383-431.
  19. Windham, W. R., K. C. Lawrence and P. W. Feldner. 2003. Prediction of fat content in poultry meat by near-infrared transmission analysis. J. Appl. Poult. Res. 12:69-73.

Cited by

  1. Better Quality Food and Beverages: The Role of near Infrared Spectroscopy vol.16, pp.1, 2008,
  2. Measurement of water-holding capacity in raw and freeze-dried broiler breast meat with visible and near-infrared spectroscopy1 vol.93, pp.7, 2014,
  3. A Review of near Infrared Spectroscopy in Muscle Food Analysis: 2005–2010 vol.19, pp.2, 2011,