Identification of Repeatedly Frozen Meat Based on Near-Infrared Spectroscopy Combined with Self-Organizing Competitive Neural Networks

REFERENCES

• Bertram, H.C.; Andersen, R.H.; Andersen, H.J. Development in myofibrillar water distribution of two pork quality during 10-months freezer storage. Meat Science 2007, 75(1), 128–133.
   PubMed Web of Science ®Google Scholar
• Gibanananda, R.; Sanjay, B.; Nootan, K.S.; Suryanarayan, D.; Vinod, R.; Seetharaman, A.; Syed, A.H. Lipid peroxidation, free radical production, and antioxidant status in breast cancer. Breast Cancer Research and Treatment 2000, 59(2), 163–170.
   PubMed Web of Science ®Google Scholar
• Meyer, F.; Chardonnens, F.; Hübner, P.; Lüthy, J. Polymerase chain reaction (PCR) in the quality and safety assurance of food: Detection of soya in processed meat products. Meat Science 1996, 203(4), 339–344.
   Google Scholar
• Langen, M.; Peters, U.; Körner, U.; Gissel, C.; Stanislawski, D.; Klein, G. Semiquantitative detection of male pork tissue in meat and meat products by PCR. Meat Science 2010, 86(3), 821–824.
   PubMed Web of Science ®Google Scholar
• Asensio, L.; Gonzálezb, I.; Garcíab, T.; Martín, R. Determination of food authenticity by enzyme-linked immunosorbent assay (ELISA). Food Control 2008, 19(1), 1–8.
   Web of Science ®Google Scholar
• Zülal, K.; Ayten, G.; Mustafa, T.Y.; Ahmet, E.Y.; Hasan, Y. Taqman-based duplex real-time polymerase chain reaction approach for the detection and quantification of donkey and pork adulterations in raw and heat-processed meats. International Journal of Food Properties 2014, 17(3), 629–638.
   Web of Science ®Google Scholar
• Montiel-Sosa, J.F.; Ruiz-Pesini, E.; Montoya, J.; Roncalés, P.; López-Pérez, M.J.; Pérez-Martos, A. Direct and highly species-specific detection of pork meat and fat in meat products by PCR amplification of mitochondrial DNA. Journal of Agriculture and Food Chemistry 2000, 48(7), 2829–2832.
   PubMed Web of Science ®Google Scholar
• Ebbehøj, K.F.; Thomsen, P.D. Species differentiation of heated meat products by DNA hybridization. Meat Science 1991, 30(3), 221–234.
   PubMed Web of Science ®Google Scholar
• Woodcock, T.; Downey, G.; O’Donnell, C.P. Better quality food and beverages: The role of near-infrared spectroscopy. Journal of Near-Infrared Spectroscopy 2008, 16(1), 1–29.
   Web of Science ®Google Scholar
• De Marchi, M.; Riovanto, R.; Penasa, M.; Cassandro, M. At-line prediction of fatty acid profile in chicken breast using near-infrared reflectance spectroscopy. Meat Science 2012, 90(3), 653–657.
   PubMed Web of Science ®Google Scholar
• Chen, L.; Wang, J.; Ye, Z.; et al. Classification of Chinese honeys according to their floral origin by near-infrared spectroscopy. Food Chemistry 2012, 135(2), 338–342.
   PubMed Web of Science ®Google Scholar
• Shen, F.; Ying, Y.; Li, B.; Zheng, Y.; Liu, X. Discrimination of blended Chinese rice wine ages based on near-infrared spectroscopy. International Journal of Food Properties 2012, 15(6), 1262–1275.
   Web of Science ®Google Scholar
• Riovanto, R.; De Marchi, M.; Cassandro, M.; Penasa, M. Use of near-infrared transmittance spectroscopy to predict fatty acid composition of chicken meat. Food Chemistry 2012, 134(4), 2459–2464.
   PubMed Web of Science ®Google Scholar
• Olsen, E.F.; Rukke, E.O.; Egelandsdal, B.; Isaksson, T. Determination of omega-6 and omega-3 fatty acids in pork adipose tissue with non-destructive Raman and Fourier transform infrared spectroscopy. Applied Spectroscopy 2008, 62 (9), 968–974.
   PubMed Web of Science ®Google Scholar
• Prieto, N.; Roehe, R.; Lavín, P.; Batten, G.; Andrés, S. Application of near-infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat Science 2009, 83(2), 175–186.
   PubMed Web of Science ®Google Scholar
• Prevolnik, M.; Čandek-Potokar, M.; Škorjanc, D. Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods. Journal of Food Engineering 2010, 98(3), 347–352.
   Web of Science ®Google Scholar
• Grau, R.; Sánchez, A.J.; Girón, J.; Iborra, E.; Fuentes, A.; Barat, J.M. Non-destructive assessment of freshness in packaged sliced chicken breasts using SW-NIR spectroscopy. Food Research International 2011, 44(1), 331–337.
   Web of Science ®Google Scholar
• Monroy, M.; Prasher, S.; Ngadi, M.O.; Wang, N.; Karimi, Y. Pork meat quality classification using visible/near-infrared spectroscopic data. Biosystems Engineering 2010, 107(3), 271–276.
   Web of Science ®Google Scholar
• del Morala, F.G.; Guillénb, A.; del Moralc, L.G.; O’Vallea, F.; Martínezd, L.; del Moral, R.G. Duroc and Iberian pork neural network classification by visible and near-infrared reflectance spectroscopy. Journal of Food Engineering 2009, 90(4), 540–547.
   Web of Science ®Google Scholar
• Zamora-Rojas, E.; Pérez-Marín, D.; De Pedro-Sanz, E.; Guerrero-Ginel, J.E.; Garrido-Varo, A. In-situ Iberian pig carcass classification using a micro-electro-mechanical system (MEMS)-based near-infrared (NIR) spectrometer. Meat Science 2012, 90(3), 636–642.
   PubMed Web of Science ®Google Scholar
• Downey, G.; Beauchne, D. Discrimination between fresh and frozen-then-thawed beef mlongissimus dorsi by combined visible-near infrared reflectance spectroscopy: A feasibility study. Meat Science 1997, 45 (3), 353–363.
   PubMed Web of Science ®Google Scholar
• Anhong, Z.; Fang, C. Identification of fresh shrimp and frozen-thawed shrimp by vis/NIR spectroscopy. 2013 2nd International Conference on Nutrition and Food Sciences 2013, 53(4), 60–65.
   Google Scholar
• Shin, E.C.; Craft, B.D.; Pegg, R.B.; Phillips, R.D.; Eitenmiller, R.R. Chemometric approach to fatty acid profiles in Runner-type peanut cultivars by principal component analysis (PCA). Food Chemistry 2010, 119(3), 1262–1270.
   Web of Science ®Google Scholar
• Qu, N.; Zhu, M.; Mi, H.; Dou, Y.; Ren, Y. Non-destructive determination of compound amoxicillin powder by NIR spectroscopy with the aid of chemometrics. Spectrochimica Acta Part A 2008, 70(5), 1146–1151.
   PubMed Web of Science ®Google Scholar
• Yang, L.P.; Gu, X.H.; Ye, H.W. Sample locality preserving discriminant analysis for classification. Optics and Precision Engineering 2011, 19(9), 2205–2213.
   Google Scholar
• Alishahi, A.; Farahmand, H.; Prieto, N.; Cozzollino, D. Identification of transgenic foods using NIR spectroscopy: A review. Spectrochimica Acta Part A 2010, 75(1), 1–7.
   PubMed Web of Science ®Google Scholar
• Yuan, Y.F.; Tao, Z.H.; Liu, J.X.; Tian, C.H.; Wang, G.W.; Li, Y.Q. Identification of Cortex Phellodendri by Fourier-transform infrared spectroscopy and principal component analysis. Spectroscopy and Spectral Analysis 2011, 31 (5), 1258–1263.
   Web of Science ®Google Scholar
• Federico, M. Artificial neural networks in foodstuff analyses: Trends and perspectives A review. Analytica Chimica Acta 2009, 635(2), 121–131.
   PubMed Web of Science ®Google Scholar
• Wang, Y.H.; Li, Y.; Yang, S.L.; Yang, L. Classification of substrates and inhibitors of p-glycoprotein using unsupervised machine learning approach. Journal of Chemical Information and Modeling 2005, 45(3), 750–757.
   PubMed Web of Science ®Google Scholar
• Huang, Y.; Kangas, L.J.; Rasco, B.A. Applications of artificial neural networks (ANNs) in food science. Food Science and Nutrition 2007, 47(2), 113–126.
   PubMed Web of Science ®Google Scholar