Predicting of intramuscular fat content in pork using near infrared spectroscopy and multivariate analysis

References

• Ripoll, G.; Albertí, P.; Panea, B.; Olleta, J. L.; Sañudo, C. Near-Infrared Reflectance Spectroscopy for Predicting Chemical, Instrumental and Sensory Quality of Beef. Meat Science 2008, 80, 697–702. DOI: 10.1016/j.meatsci.2008.03.009.
   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, 175–186. DOI: 10.1016/j.meatsci.2009.04.016.
   PubMed Web of Science ®Google Scholar
• Kutsanedzie, F. Y. H.; Chen, Q.; Hassan, M. M.; Yang, M.; Sun, H.; Rahman, M. H. Near Infrared System Coupled Chemometric Algorithms for Enumeration of Total Fungi Count in Cocoa Beans Neat Solution. Food Chemistry 2018, 240, 231–238. DOI: 10.1016/j.foodchem.2017.07.117.
   PubMed Web of Science ®Google Scholar
• Tejerina, D.; López-Parra, M. M.; García-Torres, S. Potential Used of near Infrared Reflectance Spectroscopy to Predict Meat Physico-Chemical Composition of Guinea Fowl (Numida Meleagris) Reared under Different Production Systems. Food Chemistry 2009, 113, 1290–1296. DOI: 10.1016/j.foodchem.2008.08.044.
   Web of Science ®Google Scholar
• Wang, J. X.; Fan, L. F.; Wang, H. H.; Zhao, P. F.; Li, H.; Wang, Z. Y.; Huang, L. Determination of the Moisture Content of Fresh Meat Using Visible and Near-Infrared Spatially Resolved Reflectance Spectroscopy. Biosystems Engineering 2017, 162, 40–56. DOI: 10.1016/j.biosystemseng.2017.07.004.
   Web of Science ®Google Scholar
• Prieto, N.; Pawluczyk, O.; Dugan, M. E. R.; Aalhus, J. L. A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products. Applied Spectroscopy 2017, 71, 1403–1426. DOI: 10.1177/0003702817709299.
   PubMed Web of Science ®Google Scholar
• Cai, J.; Chen, Q.; Wan, X.; Zhao, J. Determination of Total Volatile Basic Nitrogen (TVB-N) Content and Warner-Bratzler Shear Force (WBSF) in Pork Using Fourier Transform near Infrared (FT-NIR) Spectroscopy. Food Chemistry 2011, 126, 1354–1360. DOI: 10.1016/j.foodchem.2010.11.098.
   Web of Science ®Google Scholar
• Khulal, U.; Zhao, J.; Hu, W.; Chen, Q. Nondestructive Quantifying Total Volatile Basic Nitrogen (TVB-N) Content in Chicken Using Hyperspectral Imaging (HSI) Technique Combined with Different Data Dimension Reduction Algorithms. Food Chemistry 2016, 197, 1191–1199. DOI: 10.1016/j.foodchem.2015.11.084.
   PubMed Web of Science ®Google Scholar
• Prieto, N.; Andrés, S.; Giráldez, F. J.; Mantecón, A. R.; Lavín, P. Discrimination of Adult Steers (Oxen) and Young Cattle Ground Meat Samples by near Infrared Reflectance Spectroscopy (NIRS). Meat Science 2008, 79, 198–201. DOI: 10.1016/j.meatsci.2007.08.001.
   PubMed Web of Science ®Google Scholar
• Shackelford, S. D.; King, D. A.; Wheeler, T. L. Development of a System for Classification of Pork Loins for Tenderness Using Visible and Near-Infrared Reflectance Spectroscopy. Journal of Animal Science 2011, 89, 3803–3808. DOI: 10.2527/jas.
   PubMed Web of Science ®Google Scholar
• Chou, C.-H.; Chyr, C.-Y.-L.; Chen, R.-L.-C.; Shaw, J.-T. Evaluation of Freshness of Ground Pork Tenderloin by Visible/Near Infrared Spectroscopy. Journal Food Drug Analysis 2010, 18, 107–112. DOI: 10.6227/jfda.
   Web of Science ®Google Scholar
• Liao, Y.; Fan, Y.; Cheng, F. On-Line Prediction of PH Values in Fresh Pork Using Visible/Near-Infrared Spectroscopy with Wavelet De-Noising and Variable Selection Methods. Journal of Food Engineering 2012, 109, 668–675. DOI: 10.1016/j.jfoodeng.2011.11.029.
   Web of Science ®Google Scholar
• Huang, Q.; Chen, Q.; Li, H.; Huang, G.; Ouyang, Q.; Zhao, J. Non-Destructively Sensing Pork’s Freshness Indicator Using Near Infrared Multispectral Imaging Technique. Journal of Food Engineering 2015, 154, 69–75. DOI: 10.1016/j.jfoodeng.2015.01.006.
   Web of Science ®Google Scholar
• Horváth, K.; Seregély, Z.; Andrássy, É.; Dalmadi, I.; Farkas, J.; Horváth, K.; Seregély, Z.; Andrássy, É.; Farkas, J. A Preliminary Study Using near Infrared Spectroscopy to Evaluate Freshness and Detect Spoilage in Sliced Pork Meat. Acta Alignment Hung 2008, 37, 93–102. DOI: 10.1556/AAlim.37.2008.1.9.
   Web of Science ®Google Scholar
• Andrés, S.; Murray, I.; Navajas, E. A.; Fisher, A. V.; Lambe, N. R.; Bünger, L. Prediction of Sensory Characteristics of Lamb Meat Samples by near Infrared Reflectance Spectroscopy. Meat Science 2007, 76, 509–516. DOI: 10.1016/j.meatsci.2007.01.011.
   PubMed Web of Science ®Google Scholar
• Schmutzler, M.; Beganovic, A.; Böhler, G.; Huck, C. W. Methods for Detection of Pork Adulteration in Veal Product Based on FT-NIR Spectroscopy for Laboratory, Industrial and On-Site Analysis. Food Control 2015, 57, 258–267. DOI: 10.1016/j.foodcont.2015.04.019.
   Web of Science ®Google Scholar
• Barlocco, N.; Vadell, A.; Ballesteros, F.; Galietta, G.; Cozzolino, D. Predicting Intramuscular Fat, Moisture and Warner-Bratzler Shear Force in Pork Muscle Using near Infrared Reflectance Spectroscopy. Animal Science 2006, 82, 111–116. DOI: 10.1079/ASC20055.
   Google Scholar
• Prevolnik, M.; Čandekpotokar, M.; Škorjanc, D.; Velikonjabolta, Š.; Škrlep, M.; Žnidaršič, T.; Babnik, D. Predicting Intramuscular Fat Content in Pork and Beef by near Infrared Spectroscopy. Journal of Near Infrared Spectroscopy 2005, 13, 77–85. DOI: 10.1255/jnirs.460.
   Web of Science ®Google Scholar
• Pullanagari, R. R.; Yule, I. J.; Agnew, M. On-Line Prediction of Lamb Fatty Acid Composition by Visible near Infrared Spectroscopy. Meat Science 2015, 100, 156–163. DOI: 10.1016/j.meatsci.2014.10.008.
   PubMed Web of Science ®Google Scholar
• De La Roza-Delgado, B.; Soldado, A.; Oliveira, A.; Martinez-Fernandez, A.; Argamenteria, A. Assessing the Value of a Portable near Infrared Spectroscopy Sensor for Predicting Pork Meat Quality Traits of “Asturcelta Autochthonous Swine Breed”. Food Analytical Methods 2014, 7, 151–156. DOI: 10.1007/s12161-013-9611-y.
   Web of Science ®Google Scholar
• Yu, H.-Y.; Niu, X.-Y.; Lin, H.-J.; Ying, Y.-B.; Li, -B.-B.; Pan, -X.-X. A Feasibility Study on On-Line Determination of Rice Wine Composition by Vis-NIR Spectroscopy and Least-Squares Support Vector Machines. Food Chemistry 2009, 113, 291–296. DOI: 10.1016/j.foodchem.2008.06.083.
   Web of Science ®Google Scholar
• Suykens, J. A. K.; Gestel, T. V.; Brabanter, J. D.; Moor, B. D.; Vandewalle, J. Least Squares Support Vector Machines World Scientific Press: River Edge, 2002.
   Google Scholar
• Vapnik, V. N.;. The Nature of Statistical Learning Theory. Springer Press: New York, 1995.
   Google Scholar
• Thissen, U.; Ustün, B.; Melssen, W. J.; Buydens, L. M. Multivariate Calibration with Least-Squares Support Vector Machines. Analytical Chemistry 2004, 76, 3099–3105. DOI: 10.1021/ac035522m.
   PubMed Web of Science ®Google Scholar
• Shen, F.; Niu, X.-Y.; Yang, D.-T.; Ying, Y.-B.; Li, -B.-B.; Zhu, G.-Q.; Wu, J. Determination of Amino Acids in Chinese Rice Wine by Fourier Transform near Infrared Spectroscopy. Journal of Agricultural and Food Chemistry 2010, 58, 9809–9816. DOI: 10.1021/jf1017912.
   PubMed Web of Science ®Google Scholar
• Fu, X.; Ying, Y.; Lu, H.; Xu, H.; Yu, H. FT-NIR Diffuse Reflectance Spectroscopy for Kiwifruit Firmness Detection. Journal of Food Measurement and Characterization 2007, 1, 29–35. DOI: 10.1007/s11694-007-9004-2.
   Google Scholar
• Cozzolino, D.; Murray, I.; Paterson, R.; Scaife, J. R. Visible and near Infrared Reflectance Spectroscopy for the Determination of Moisture, Fat and Protein in Chicken Breast and Thigh Muscle. Journal of Near Infrared Spectroscopy 1996, 4, 108–114. DOI: 10.1255/jnirs.92.
   Google Scholar
• Cozzolino, D.; Murray, I.; Scaife, J. R.; Paterson, R. Study of Dissected Lamb Muscles by Visible and near Infrared Reflectance Spectroscopy for Composition Assessment. Animal Science 2000, 70, 417–423. DOI: 10.1017/S1357729800051766.
   Google Scholar
• Zhang, K.; Tan, Z.; Chen, C.; Sun, X. S.; Wang, D. Rapid Prediction of Oil Content of Camelina Seeds Using Near-Infrared Spectroscopy. Energy Fuel 2017, 31, 5629–5634. DOI: 10.1021/acs.energyfuels.6b02762.
   Web of Science ®Google Scholar
• Liu, F.; He, Y.; Wang, L.; Sun, G. Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration. Food and Bioprocess Technology 2011, 4, 1331–1340. DOI: 10.1007/s11947-009-0240-9.
   Web of Science ®Google Scholar
• Afseth, N.-K.; Segtnan, V.-H.; Marquardt, B.-J.; Wold, J.-P. Raman and Near-Infrared Spectroscopy for Quantification of Fat Composition in a Complex Food Model System. Applied Spectroscopy 2005, 59, 1324–1332. DOI: 10.1366/000370205774783304.
   PubMed Web of Science ®Google Scholar
• Murray, I.; Williams, P. C. Chemical Principals of Near-Infrared Technology American Association of Cereal Chemists Press: Saint Paul, 1987.
   Google Scholar
• Shenk, J. S.; Workman, J. J.; Westerhans, M. O. Application of NIRS to Agricultural Products Dekker Press: New York, 1992.
   Google Scholar
• Prieto, N.; Andrés, S.; Giráldez, F.-J.; Mantecón, A.-R.; Lavín, P. Potential Use of near Infrared Reflectance Spectroscopy (NIRS) for the Estimation of Chemical Composition of Oxen Meat Samples. Meat Science 2006, 74, 487–496. DOI: 10.1016/j.meatsci.2006.04.030.
   PubMed Web of Science ®Google Scholar
• Rødbotten, R.; Nilsen, B.-N.; Hildrum, K.-I. Prediction of Beef Quality Attributes from Early Post Mortem near Infrared Reflectance Spectra. Food Chemistry 2000, 69, 427–436. DOI: 10.1016/j.meatsci.2008.03.009.
   Web of Science ®Google Scholar
• Cozzolino, D.; Barlocco, N.; Vadell, A.; Ballesteros, F.; Gallieta, G. The Use of Visible and Near-Infrared Reflectance Spectroscopy to Predict Colour on Both Intact and Homogenised Pork Muscle. LWT Food Science and Technology 2003, 36, 195–202. DOI: 10.1016/S0023-6438(02)00199-8.
   Web of Science ®Google Scholar
• Cozzolino, D.; Murray, I. Identification of Animal Meat Muscles by Visible and near Infrared Reflectance Spectroscopy. LWT Food Science and Technology 2004, 37, 447–452. DOI: 10.1016/j.lwt.2003.10.013.
   Web of Science ®Google Scholar
• Leroy, B.; Lambotte, S.; Dotreppe, O.; Lecocq, H.; Istasse, L.; Clinquart, A. Prediction of Technological and Organoleptic Properties of Beef Longissimus Thoracis from Mear-Infrared Reflectance and Transmission Spectra. Meat Science 2004, 66, 45–54. DOI: 10.1016/S0309-1740(03)00002-0.
   PubMed Web of Science ®Google Scholar
• Liao, Y.-T.; Fan, Y.-X.; Cheng, F. On-Line Prediction of Fresh Pork Quality Using Visible/Near-Infrared Reflectance Spectroscopy. Meat Science 2010, 86, 901–907. DOI: 10.1016/j.meatsci.2010.07.011.
   PubMed Web of Science ®Google Scholar