Research Article Open Access

Effect of Varying Postmortem Deboning Time and Sampling Position on Visible and near Infrared Spectra of Broiler Breast Filets

Authors

CORRESPONDING AUTHOR
S.A. Hawkins
United States Department of Agriculture, Agricultural Research Service, Athens, GA 30605, USA
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H. Zhuang
United States Department of Agriculture, Agricultural Research Service, Athens, GA 30605, USA
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M. Sohn
United States Department of Agriculture, Agricultural Research Service, Athens, GA 30605, USA
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W.R. Windham
United States Department of Agriculture, Agricultural Research Service, Athens, GA 30605, USA
Published on: April 15, 2014
DOI 10.3923/ijps.2014.272.278
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Abstract

Visible-Near Infrared spectroscopy (Vis-NIR) was used to characterize broiler breast filets with varied deboning times and identify how the side and position of the sampling affects the chemometric analysis and prediction capabilities. This study served to identify what differences, if any, exist when collecting spectra from the skin side and the medial side of the breast filets. In addition to the side of the filet, two different positions, anterior and posterior, on the filet were also probed spectroscopically. The comparison of the region and side of sampling of the breast filets has been previously unreported. The breast filets under investigation were subjected to different post-mortem deboning times. The right and left breast filets from each carcass were both used, but were deboned at different times. The results of this study show that the side of the filet has more impact on the spectra than does the position of the sampled area. The data analysis also shows that the spectra from the skin side are more useful for separation of samples by deboning time.

Keywords:

Broiler breast meat, chemometrics, deboning time, sample position, visible-near infrared spectroscopy

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Copyright (c) 2014 Asian Network for Scientific Information

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How to Cite

Hawkins , S., Zhuang, H., Sohn, M., & Windham, W. (2014). Effect of Varying Postmortem Deboning Time and Sampling Position on Visible and near Infrared Spectra of Broiler Breast Filets. International Journal of Poultry Science, 13(5), 272–278. https://doi.org/10.3923/ijps.2014.272.278
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References

1.
Cavitt, L.C., J.F.C. Meullenet, R. Xiong and C.M. Owens, 2005. The relationship of razor blade shear, Allo-Kramer shear, Warner-Bratzler shear and sensory tests to changes in tenderness of broiler breast fillets. J. Muscle Foods, 16: 223-242.
2.
Cavitt, L.C., G.W. Youm, J.F. Meullenet, C.M. Owens and R. Xiong, 2004. Prediction of poultry meat tenderness using razor blade shear, Allo-Kramer shear and sarcomere length. J. Food Sci., 69: SNQ11-SNQ15.
3.
Chao, K., Y.R. Chen and D.E. Chan, 2003. Analysis of Vis/NIR spectral variations of wholesome, septicemia and cadaver chicken samples. Applied Eng. Agric., 19: 453-460.
4.
Chao, K., X. Nou, Y. Liu, M.S. Kim and D.E. Chan et al., 2008. Detection of fecal/ingesta contaminants on poultry processing equipment surfaces by visible and near-infrared reflectance spectroscopy. Applied Eng. Agric., 24: 49-55.
5.
Davis, R., J. Irudayaraj, B.L. Reuhs and L.J. Mauer, 2010. Detection of E. coli O157:H7 from ground beef using Fourier Transform Infrared (FT-IR) spectroscopy and chemometrics. J. Food Sci., 75: M340-M346.
6.
De la Haba, M.J., A. Garrido-Varo, D.C. Perez-Marin and J.E. Guerrero, 2007. Near infrared analysis as a first-line screening technique for identifying animal species in rendered animal by-product meals. J. Near Infrared Spectrosc., 15: 237-245.
7.
Ding, F., Y.R. Chen, K. Chao and D.E. Chan, 2006. Two-color mixing for classifying agricultural products for safety and quality. Applied Optics, 45: 668-677.
8.
Fluckiger, M., M.R. Brown, L.R. Ward and N.A. Moltschaniwskyj, 2011. Predicting glycogen concentration in the foot muscle of abalone using Near Infrared Reflectance Spectroscopy (NIRS). Food Chem., 126: 1817-1820.
9.
Fontaine, J., J. Horr and B. Schirmer, 2001. Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products and poultry meal. J. Agric. Food Chem., 49: 57-66.
10.
Givens, D.I., J.L. de Boever and E.R. Deaville, 1997. The principles, practices and some future applications of near infrared spectroscopy for predicting the nutritive value of foods for animals and humans. Nutr. Res. Rev., 10: 83-114.
11.
Gonzalez-Martin, I. and J.M. Hernandez-Hierro, 2008. Detection and quantification of additives (urea, biuret and poultry litter) in alfalfas by nir spectroscopy with fibre-optic probe. Talanta, 76: 1130-1135.
12.
Hawkins, S.A., B. Park, G.H. Poole, T. Gottwald, W.R. Windham and K.C. Lawrence, 2010. Detection of Citrus huanglongbing by Fourier transform infrared-attenuated total reflection spectroscopy. Applied Spectrosc., 64: 100-103.
13.
Hawkins, S.A., B. Park, G.H. Poole, T.R. Gottwald, W.R. Windham, J. Albano and K.C. Lawrence, 2010. Comparison of FTIR spectra between Huanglongbing (citrus greening) and other citrus maladies. J. Agric. Food Chem., 58: 6007-6010.
14.
Lee, Y.S., C.M. Owens and J.F. Meullenet, 2008. The Meullenet-Owens Razor Shear (MORS) for predicting poultry meat tenderness: Its applications and optimization. J. Texture Studi., 39: 655-672.
15.
Li, Q.F., Z.L. Yang and L.J. Han, 2008. Discriminant analysis of near infrared diffuse reflectance spectra to detect adulteration of non-ruminant meat and bone meal. Guang Pu Xue Yu Guang Pu Fen Xi, 28: 572-577.
16.
Liu, Y., B.G. Lyon, W.R. Windham, C.E. Lyon and E.M. Savage, 2004. Prediction of physical, color and sensory characteristics of broiler breasts by visible/near infrared reflectance spectroscopy. Poult. Sci., 83: 1467-1474.
17.
Liu, Y., B.G. Lyon, W.R. Wyndham, C.E. Lyon and E.M. Savage, 2004. Principal component analysis of physical, color and sensory characteristics of chicken breasts deboned at two, four, six and twenty-four hours postmortem. Poult. Sci., 83: 101-108.
18.
Liu, Y. and Y.R. Chen, 2000. Two-dimensional correlation spectroscopy study of visible and near-infrared spectral variations of chicken meats in cold storage. Applied Spectrosc., 54: 1458-1470.
19.
Lyon, B.G. and C.E. Lyon, 1990. Texture profile of broiler Pectoralis major as influenced by post-mortem deboning time and heat method. Poult. Sci., 69: 329-340.
20.
Lyon, B.G. and C.E. Lyon, 2001. Meat Quality: Sensory and Instrumental Evaluations. In: Poultry Meat Processing, Sams, A.R. (Ed.). CRC Press, Boca Raton, FL., pp: 97-120.
21.
Lyon, C.E., D. Hamm and J.E. Thomson, 1985. pH and tenderness of broiler breast meat deboned various times after chilling. Poult. Sci., 64: 307-310.
22.
Meullenet, J.F., E. Jonville, D. Grezes and C.M. Owens, 2004. Prediction of the texture of cooked poultry pectoralis major muscles by near-infrared reflectance analysis of raw meat. J. Texture Stud., 35: 573-585.
23.
Owens, B., M.E.E. McCann, K.J. McCracken and R.S. Park, 2009. Prediction of wheat chemical and physical characteristics and nutritive value by near-infrared reflectance spectroscopy. Br. Poult. Sci., 50: 103-122.
24.
Park, B., W.R. Windham, K.C. Lawrence and D.P. Smith, 2007. Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm. Biosyst. Eng., 96: 323-333.
25.
Prevolnik, M., M. Candek-Potokar, M. Novic and D. Skorjanc, 2009. An attempt to predict pork drip loss from pH and colour measurements or near infrared spectra using artificial neural networks. Meat Sci., 83: 405-411.
26.
Prieto, N., R. Roehe, P. Lavin, G. Batten and S. Andres, 2009. Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat Sci., 83: 175-186.
27.
Prieto, N., D.W. Ross, E.A. Navajas, G.R. Nute and R.I. Richardson et al., 2009. On-line application of visible and near infrared reflectance spectroscopy to predict chemical-physical and sensory characteristics of beef quality. Meat Sci., 83: 96-103.
28.
Sun, D.W., 2009. Infrared Spectroscopy for Food Quality Analysis and Control. 1st Edn., Academic Press, Oxford, USA., ISBN-13: 978-0123741363, Pages: 448.
29.
Valdes, E.V. and S. Leeson, 1994. Determination of metabolizable energy of feed grade fats based on the application of near infrared reflectance to the multilevel test diets. Poult. Sci., 73: 172-178.
30.
Valdes, E.V. and J.D. Summers, 1986. Determination of crude protein and fat in carcass and breast muscle samples of poultry by near infrared reflectance spectroscopy. Poult. Sci., 65: 485-490.
31.
Windham, W.R., K.C. Lawrence and P.W. Feldner, 2003. Prediction of fat content in poultry meat by near-infrared transmission analysis. J. Applied Poult. Res., 12: 69-73.
32.
Windham, W.R., K.C. Lawrence, B. Park and R.J. Buhr, 2003. Visible/NIR spectroscopy for characterizing fecal contamination of chicken carcasses. Trans. Asae, 46: 747-751.
33.
Xiong, R., L.C. Cavitt, J.F. Meullenet and C.M. Owens, 2006. Comparison of Allo-kramer, Warner-bratzler and razor blade shears for predicting sensory tenderness of broiler breast meat. J. Texture Stud., 37: 179-199.
34.
Yancey, J.W.S., J.K. Apple, J.F. Meullenet and J.T. Sawyer, 2010. Consumer responses for tenderness and overall impression can be predicted by visible and near-infrared spectroscopy, Meullenet-owens razor shear and Warner-Bratzler shear force. Meat Sci., 85: 487-492.
35.
Yang, J.S., Q.X. Meng, L.P. Ren, Z.M. Zhou and X.X. Xie, 2010. Rapid evaluation of beef quality by NIRS technology. Spectrosc. Spectral Anal., 30: 685-687.