M. S. Brewer, J. Novakofski, and K. Freise, Instrumental evaluation of pH effects on ability of pork chops to bloom, Meat Science, vol.72, issue.4, pp.596-602, 2006.
DOI : 10.1016/j.meatsci.2005.09.009

B. S. Andrews, S. Hutchison, and J. A. Unruh, INFLUENCE OF pH AT 24???H POSTMORTEM ON QUALITY CHARACTERISTICS OF PORK LOINS AGED 45 DAYS POSTMORTEM, Journal of Muscle Foods, vol.66, issue.4, pp.401-419, 2007.
DOI : 10.1111/j.1365-2621.1998.tb17895.x

R. L. Van-laack, S. G. Stevens, and K. J. Stalder, The influence of ultimate pH and intramuscular fat content on pork tenderness and tenderization., Journal of Animal Science, vol.79, issue.2, pp.79392-397, 2001.
DOI : 10.2527/2001.792392x

B. L. Knox, V. Laack, R. L. Davidson, and P. , Relationships between Ultimate pH and Microbial, Chemical, and Physical Characteristics of Vacuum-Packaged Pork Loins, Journal of Food Science, vol.145, issue.1, pp.73-104, 2008.
DOI : 10.1111/j.1365-2621.1984.tb12832.x

E. A. Bryhni, D. V. Byrne, and M. Rodbotten, Consumer and sensory investigations in relation to physical/chemical aspects of cooked pork in Scandinavia, Meat Science, vol.65, issue.2, pp.65737-748, 2003.
DOI : 10.1016/S0309-1740(02)00276-0

L. Yitao, F. Yuxia, and . Wu-xueqian, Online Determination of pH in Fresh Pork by Visible/Near-Infrared Spectroscopy, Guang Pu Xue Yu Guang Pu Fen Xi, issue.34, pp.30681-684, 2010.

H. J. He, D. Wu, and D. Sun, Application of hyperspectral imaging technique for nondestructive pH prediction in salmon fillets, Proceedings of the 3rd CIGR International Conference of Agricultural Engineering (CIGR-AgEng2012)

G. K. Naganathan, L. M. Grimes, and J. Subbiah, Visible/near-infrared hyperspectral imaging for beef tenderness prediction, Computers and Electronics in Agriculture, vol.64, issue.2, pp.225-233, 2008.
DOI : 10.1016/j.compag.2008.05.020
URL : http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1178&context=biosysengfacpub

G. K. Naganathan, L. M. Grimes, and J. Subbiah, Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction, Sensing & Instrumentation for Food Quality & Safety, pp.178-188, 2008.
DOI : 10.2135/cropsci1991.0011183X003100020049x

G. Elmasry, D. W. Sun, and P. Allen, Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging, Food Research International, vol.44, issue.9, pp.442624-2633, 2011.
DOI : 10.1016/j.foodres.2011.05.001

G. Elmasry, D. W. Sun, and P. Allen, Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef, Journal of Food Engineering, vol.110, issue.1, pp.127-140, 2012.
DOI : 10.1016/j.jfoodeng.2011.11.028

M. Kamruzzaman, G. Elmasry, and D. W. Sun, Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis, Analytica Chimica Acta, vol.714, issue.3, pp.71457-67, 2012.
DOI : 10.1016/j.aca.2011.11.037

J. Qiao, N. Wang, and M. O. Ngadi, Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique, Meat Science, vol.76, issue.1, pp.761-769, 2007.
DOI : 10.1016/j.meatsci.2006.06.031

J. Qiao, M. O. Ngadi, and N. Wang, Pork quality and marbling level assessment using a hyperspectral imaging system, Journal of Food Engineering, vol.83, issue.1, pp.10-16, 2007.
DOI : 10.1016/j.jfoodeng.2007.02.038

Z. Peng-yankun, W. Jing, and . Wei, Potential prediction of the microbial spoilage of beef using spatially resolved hyperspectral scattering profiles, Journal of Food Engineering, vol.102, issue.2, pp.163-169, 2011.
DOI : 10.1016/j.jfoodeng.2010.08.014

T. Feifei, P. Yankun, and L. Yongyu, Simultaneous Determination of Tenderness and Escherichia Coli Contamination of Pork Using Hyperspectral Scattering Technique, Meat Science, issue.3, pp.90851-857, 2012.

C. Dongsheng, L. Yizeng, and X. Qingsong, A New Strategy of Outlier Detection for QSAR/QSPR, Journal of Computational Chemistry, issue.3, pp.31592-602, 2010.

W. Wu, B. Walczak, and D. L. Massart, Artificial neural networks in classification of NIR spectral data: Design of the training set, Chemometrics and Intelligent Laboratory Systems, vol.33, issue.1, pp.3335-3381, 1996.
DOI : 10.1016/0169-7439(95)00077-1

R. Galvao, M. Araujo, and G. E. Jose, A method for calibration and validation subset partitioning, Talanta, vol.67, issue.4, pp.736-740, 2005.
DOI : 10.1016/j.talanta.2005.03.025

L. Hongdong, L. Yizeng, and X. Qingsong, Key Wavelengths Screening Using Competitive Adaptive Reweighted Sampling Method for Multivariate Calibration, Analytica Chimica Acta, vol.648, issue.1, pp.77-84, 2009.