Hyperspectral scattering profiles for prediction of beef tenderness

American Society of Agricultural and Biological Engineers Annual International Meeting 2008, ASABE 2008

Volumn 1, Issue , 2008, Pages 8-18

  Peng, Yankun ^a   Wu, Jianhu ^a  

^a CHINA AGRICULTURAL UNIVERSITY   (China)

Author keywords

Beef tenderness; Hyperspectral imaging; Meat; Warner Bratzler shear force, Multi linear regression

Indexed keywords

IMAGE PROCESSING; IMAGING TECHNIQUES; OPTOELECTRONIC DEVICES; REGRESSION ANALYSIS;

BEEF TENDERNESS; CALIBRATION SAMPLES; CONSUMER PERCEPTIONS; CORRELATION COEFFICIENTS; EATING QUALITIES; HYPER-SPECTRAL; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGING; HYPERSPECTRAL IMAGING SYSTEMS; NONDESTRUCTIVE DETERMINATIONS; PREDICTION MODELS; SCATTERING PROFILES; SINGLE WAVELENGTHS; SPECTRAL CHARACTERISTICS; STANDARD ERROR OF PREDICTIONS; WARNER-BRATZLER SHEAR FORCE, MULTI-LINEAR REGRESSION;

MEATS;

EID: 63049085713     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Andrés, S., A. Silva, A.L. Soares-Pereira, C. Martins, A.M. Bruno-Soares, I. Murray. 2007. The use of visible and near infrared reflectance spectroscopy to predict beef M. longissimus thracis et lumborum quality attributes. Meat science, 73(3): 217-224.
2. st international congress of meat science and technology, (pp. 594-595). 20-25 August, San Antonio, Texas, USA.
3. Byrne, C. E., G. Downey, D.J. Troy & D. J. Buckley. 1998. Non-destructive Prediction of selected quality attributes of beef by Near-infrared Reflectance Spectroscopy between 750 and 1098nm. Meat Science, 49(4): 399-409.
4. Cozzolino, D., Murray. 2004. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. Lebensm. -Wiss. u.-Technol. 37: 447-452.
5. Gowen, C. P. ODonnell, P.J. Cullen, G. Downey and J.M. Frias. 2007. Hyperspectral imaging- an emerging process analytical tool for food quality and safety control. Trends in food Science & Technology, 18: 590-598.
6. Hildrum, K.I., B.N. Nilson, M. Mielnik, and T. Naes. 1994. Prediction of sensory characteristic of beef by near-infrared spectroscopy. Meat science, 38: 67-80.
7. Janz, J.A.M., J.L. Aalhus, M.E.R. Dugan, M.A. Price. 2006. A mapping method for the description of Warner-Bratzler shear force gradients in beef Longissimus thoracis et lumborum and Semitendinosus. Meat Science, 72: 79-90.
8. Kim, I., kirn, M. S. Chen, Y. R., &Kong, S. G. 2004. Detection of skin tumors on chicken carcass using hyperspectral flourescence imaging. Transaction of the ASAE, 47(5): 1785-1792.
9. Liu, Y. I., B.G. Lyon, W. R. Windham, C. E. Realinib, T. D. D. Pringle, S. Duckett. 2003. Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy, A feasibility study. Meat Science, 65: 1107-1115.
10. Lu, R., Y. Peng. 2006. Hyperspectral Scattering for assessing Peach Fruit Firmness. Biosystems Engineering, 93 (2): 161-171.
11. Miller, M. F., Carr, M. A., Ramsey, C. B., Crockett, K. L., & Hoover, L.C. 2001. Consumer thresholds for establishing the value of beef tenderness. Journal of Animal Science, 79: 3062-3068.
12. Park, B., Y.R. Chen, W.R. Hruschka, S.D. Shackelford, M Koohmaraie. 1998. Near-infrared reflectance analysis for predicting beef longissimus tenderness. Journal of Animal Science, 76: 2115-2120.
13. Peng, Y., R. Lu. 2006. An LCTF-Based multispectral image syetem for estimation of apple fruit firmness: Part II. Selection of optimal wavelength and development of prediction models. Transactions of the ASABE, 49(1): 269-275.
14. Peng, Y., R. Lu. 2008. Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 48: 52-62.
15. Platter, W. J., Tatum, J. D., Belk, K. E., Chapman, P. L., Scanga, J. A., &Smith, G. C. 2003. Relationship of consumer sensory ratings, marbling score, and shear force value to consumer acceptance of beef strip loin steaks. Journal of Animal Science, 81: 2741-2750.
16. Prieto, N., S. Andrés, F.J. Gira'ldez, A.R. Mantecon, P.Lav'n. 2006. Potential use of near infrared reflectance spectroscopy (NIR) for the estimation of chemical composition of oxen meat samples. Meat science, 74: 487-496.
17. Prieto, N., S. Andrés, F.J. Giráldez, A.R. Mantecón, P. Lavín. 2007. Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples, Meat Science, Available online 7 November 2007.
18. Qiao, J.,N. Wang, M.O. Ngadi, A. Gunenc, M. Monroy, C. Gariepy, S.O. Prasher. 2007. Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. Meat Science, I 76: 1-8.
19. Rùdbotten, B.N. Nilsen, K.I. Hildrum. 2000. Prediction of beef quality attributes from early post mortem near infrared reflectance spectra. Food Chemistry, 69: 427-436.
20. Shackelford, T.L. Wheeler, M. Koohmaraie. 1999. Tenderness classification of beef: II. Design and analysis of a system to measure beef longissimus shear force undering commercial processing conditions. Journal of Animal Science, 77: 6-12.
21. Shackford S.D., T.L. Wheeler, M. Koohmarie. 2005. On-line classification of US select beef carcasses for longissimus tenderness using visible and near-infrared reflectance spectroscopy. Meat Science, 69(3): 409-415.
22. Zhao, J. W., Jianmei Zhao, Muhua liu. 2006. The Determination of Beef Tenderness Using Near-Infrared Spectroscopy. Spectroscopy and Spectral Analysis, 26:640-642.