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Research Article

Use of Magnetic Resonance Imaging to Analyse Meat and Meat Products Non-destructively

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References

  • Muriel, E.; Ruiz, J.; Petrón, M. J.; Martín, D.; Antequera, T. Physico-chemical and Sensory Characteristics of Dry-cured Iberian Loin from Different Iberian Pig Lines. Food Sci. Technol. Int. 2004, 10(2), 117–125. DOI: 10.1177/1082013204043766.
  • Muriel, E.; Andrés, A. I.; Petrón, M. J.; Antequera, T.; Ruiz, J. Lipolytic and Oxidative Changes in Iberian Dry-cured Loin. Meat Sci. 2007, 75(2), 325–333. DOI: 10.1016/j.meatsci.2006.07.017.
  • Pérez-Palacios, T.; Ruiz, J.; Martín, D.; Muriel, E.; Antequera, T. Comparison of Different Methods for Total Lipid Quantification in Meat and Meat Products. Food Chem. 2008, 110(4), 1025–1029. DOI: 10.1016/j.foodchem.2008.03.026.
  • Pérez-Palacios, T.; Ruiz, J.; Martín, D.; Barat, J. M.; Antequera, T. Pre-cure Freezing Effect on Physicochemical, Texture and Sensory Characteristics of Iberian Ham. Food Sci. Technol. Int. 2011, 17(2), 127–133. DOI: 10.1177/1082013210381435.
  • Cava, R.; Ventanas, J.; Ruiz, J.; Andrés, A. I.; Antequera, T. Sensory Characteristics of Iberian Ham: Influence of Rearing System and Muscle Location. Food Sci. Technol. Int. 2000, 6(3), 235–242. DOI: 10.1177/108201320000600306.
  • Gomes, C. L.; Pflanzer, S. B.; Cruz, A. G.; De Felicio, P. E.; Bolini, H. M. A. Sensory Descriptive Profiling and Consumer Preferences of Beef Strip Loin Steaks. Food Res. Int. 2014, 59, 76–84. DOI: 10.1016/j.foodres.2014.01.061.
  • Pérez-Palacios, T.; Antequera, T.; Durán, M. L.; Caro, A.; Rodríguez, P. G.; Ruiz, J. MRI-based Analysis, Lipid Composition and Sensory Traits for Studying Iberian Dry-cured Hams from Pigs Fed with Different Diets. Food Res. Int. 2010, 43(1), 248–254. DOI: 10.1016/j.foodres.2009.09.020.
  • Pérez-Palacios, T.; Caballero, D.; Caro, A.; Rodríguez, P. G.; Antequera, T. Applying Data Mining and Computer Vision Techniques to MRI to Estimate Quality Traits in Iberian Hams. J. Food Eng. 2014, 131, 82–88. DOI: 10.1016/j.jfoodeng.2014.01.015.
  • Basset, O.; Buquet, B.; Abouelkaram, S.; Delachartre, P.; Culioli, J. Application of Texture Image Analysis for the Classification of Bovine Meat. Food Chem. 2000, 69(4), 437–445. DOI: 10.1016/S0308-8146(00)00057-1.
  • Chandraratne, M. R.; Kulasiri, D.; Frampton, C.; Samarasinghe, S.; Bickerstaffe, R. Prediction of Lamb Carcass Grades Using Features Extracted from Lamb Chop Images. J. Food Eng. 2006, 74(1), 116–124. DOI: 10.1016/j.jfoodeng.2005.02.012.
  • Valous, N.; Mendoza, F.; Sun, D. W.; Allen, P. Texture Appearance Characterization of Pre-sliced Pork Ham Images Using Fractal Metrics: Fourier Analysis Dimension and Lacunarity. Food Res. Int. 2007, 42(3), 353–362. DOI: 10.1016/j.foodres.2008.12.012.
  • Cross, H. R.; Belk, K. E. Objective Measurements of Carcass and Meat Quality. Meat Sci. 1994, 36(1–2), 191–202. DOI: 10.1016/0309-1740(94)90041-8.
  • Kongsro, J.; Roe, M.; Kvaal, K.; Aastveit, A. H.; Egelandsdal, B. Prediction of Fat, Muscle and Value in Norwegian Lamb Carcasses Using EUROP Classification, Carcass Shape and Length Measurements, Visible Light Reflectance and Computer Tomography (CT). Meat Sci. 2009, 81(1), 102–107. DOI: 10.1016/j.meatsci.2008.07.004.
  • Fulladosa, E.; Santos-Garcés, E.; Picouet, P.; Gou, P. Prediction of Salt and Water Content in Dry-cured Hams by Computed Tomography. J. Food Eng. 2010, 96(1), 80–85. DOI: 10.1016/j.jfoodeng.2009.06.044.
  • Santos-Garcés, E.; Gou, P.; García-Gil, N.; Arnau, J.; Fulladosa, E. Non-destructive Analysis of Aw, Salt and Water in Dry-cured Hams during Drying Process by Means of Computed Tomography. J. Food Eng. 2010, 101(2), 187–192. DOI: 10.1016/j.jfoodeng.2010.06.027.
  • Alamprese, C.; Casale, M.; Sinelli, N.; Lanteri, S.; Casiraghi, E. Detection of Minced Beef Adulteration with Turkey Meat by UV-VIS, NIR and MIR Spectroscopy. LWT Food Sci. Technol. 2013, 53(1), 225–232. DOI: 10.1016/j.lwt.2013.01.027.
  • Morán, L.; Andres, S.; Allen, P.; Moloney, A. P. Visible and near Infrared Spectroscopy as an Authentication Tool: Preliminary Investigation of the Prediction of the Ageing Time of Beef Steaks. Meat Sci. 2018, 142, 52–58. DOI: 10.1016/j.meatsci.2018.04.007.
  • Sun, S.; Guo, B.; Wei, Y.; Fan, M. Classification of Geographical Origins and Prediction of 13C and 15N Values of Lamb Meat by near Infrared Reflectance Spectroscopy. Food Chem. 2012, 135(2), 508–514. DOI: 10.1016/j.foodchem.2012.05.004.
  • Monteiro-Balage, J.; Da-luz-e-silva, S.; Abdalla-Gomide, C.; De-nadai-bonin, M.; Figueira, A. C. Predicting Pork Quality Using VIS/NIR Spectroscopy. Meat Sci. 2015, 108, 37–43. DOI: 10.1016/j.meatsci.2015.04.018.
  • Pérez-Palacios, T.; Caballero, D.; González-Mohino, A.; Mir-Bel, J.; Antequera, T. Near Infrared Reflectance Spectroscopy to Analyse Texture Related Characteristics of Sous Vide Pork Loin. J. Food Eng. 2019, 263, 417–423. DOI: 10.1016/j.jfoodeng.2019.07.028.
  • González-Mohino, A.; Antequera, T.; Ventanas, S.; Caballero, D.; Mir-Bel, J.; Pérez-Palacios, T. Near-infrared Spectroscopy-based Analysis to Study Sensory Parameters on Pork Loins as Affected by Cooking Methods and Conditions. J. Sci. Food Agric. 2018, 98(11), 4227–4236. DOI: 10.1002/jsfa.8944.
  • Qiao, T.; Ren, J.; Craigie, C. R.; Zabalza, J.; Maltin, C.; Marshall, S. Singular Spectrum Analysis for Improving Hyperspectral Imaging Based Beef Eating Quality Evaluation. Comput. Electron. Agric. 2015, 115, 21–25. DOI: 10.1016/j.compag.2015.05.007.
  • Craigie, C. R.; Johnson, P. L.; Shorten, P. R.; Charteis, A.; Maclennan, G.; Tate, M. L.; Agnew, M. P.; Taukiri, K. R.; Stuart, A. D.; Reis, M. M. Application of Hyperspectral Imaging to Predict the pH, Intramuscular Fatty Acid Content and Composition of Lamb M. Longissimus Lumborum at 24 H Post Mortem. Meat Sci. 2017, 132, 19–28. DOI: 10.1016/j.meatsci.2017.04.010.
  • Barbin, D.; ElMasry, G.; Sun, D. W.; Allen, P. Near-infrared Hyperspectral Imaging for Grading and Classification of Pork. Meat Sci. 2012, 90(1), 259–268. DOI: 10.1016/j.meatsci.2011.07.011.
  • Pereira, F. M. V.; Colnago, L. A. Determination of the Moisture Content in Beef without Weighing Benchtop Time-domain Nuclear Magnetic Resonance Spectrometer and Chemometrics. Food Anal. Methods. 2012, 5(6), 1349–1353. DOI: 10.1007/s12161-012-9383-9.
  • Warner, R. D.; Jacob, R. H.; Rosenvold, K.; Rochfort, S.; Trenerry, C.; Plozza, T.; McDonagh, M. B. Altered Post-mortem Metabolism Identified in Very Fast Chilled Lamb M. Longissimus Thoracis Et Lumborum Using Metabolomics Analysis. Meat Sci. 2015, 108, 155–164. DOI: 10.1016/j.meatsci.2015.06.006.
  • Bertram, H. C.;. NMR Spectroscopy and NMR Metabolomics in Relation to Meat Quality. In New Aspects of Meat Quality. From Genes to Ethics; Purslow, P.P., Ed.; Woodhead Publishing: Cambridge, United Kingdom, 2017; pp 355–371.
  • Bonny, J. M.; Laurent, W.; Labas, R.; Taylor, R.; Berge, P.; Renou, J. P. Magnetic Resonance Imaging of Connective Tissue: A Non-destructive Method for Characterising Muscle Structure. J. Sci. Food Agric. 2000, 81(3), 337–341. DOI: 10.1002/1097-0010(200102)81:3<337::AID-JSFA827>3.0.CO;2-W.
  • Murphy, L.;. Ionizing Radiation in Veterinary Medicine. In Veterinary Toxicology. Basic and Clinical Principles; Gupta, R.C., Ed.; Academic Press: Cambridge, Massachusetts, U. S. A., 2018; pp 327–337.
  • Reis, M. M.; Van Beers, R.; Al-Sarayeh, M.; Shorten, P.; Yan, W. Q.; Saeys, W.; Klette, R.; Craigie, C. R. Chemometrics and Hyperspectral Imaging Applied to Assessment of Chemical, Textural and Structural Characteristics of Meat. Meat Sci. 2018, 144, 100–109. DOI: 10.1016/j.meatsci.2018.05.020.
  • Du, C. J.; Sun, D. W. Recent Developments in the Applications of Image Processing Techniques for Food Quality Evaluation. Trends Food Sci. Tech. 2004, 15(5), 230–249. DOI: 10.1016/j.tifs.2003.10.006.
  • Jackman, P.; Sun, D. W.; Allen, P. Recent Advances in the Use of Computer Vision Technology in the Quality Assessment of Fresh Meats. Trends Food Sci. Tech. 2011, 22(4), 185–197. DOI: 10.1016/j.tifs.2011.01.008.
  • Jackman, P.; Sun, D. W. Recent Advances in Image Processing Using Image Texture Features for Food Quality Assessment. Trends Food Sci. Tech. 2013, 29(1), 35–43. DOI: 10.1016/j.tifs.2012.08.008.
  • Pérez-Santaescolástica, C.; Fraeye, I.; Barba, F. J.; Gómez, B.; Tomasevic, I.; Romero, A.; Moreno, A.; Toldrá, F.; Lorenzo, J. M. Application of Non-invasive Technologies in Dry-cured Ham: An Overview. Trends Food Sci. Tech. 2019, 86, 360–374. DOI: 10.1016/j.tifs.2019.02.011.
  • Feig, S.;. Comparison of Costs and Benefits of Breast Cancer Screening with Mammography, Ultrasonography, and MRI. Obstet. Gyn. Clin. N. Am. 2011, 38(1), 179–196. DOI: 10.1016/j.ogc.2011.02.009.
  • Ladd, M. E.; Bachert, P.; Meyerspeer, M.; Moser, E.; Nagel, A. M.; Norris, D. G.; Schmitter, S.; Speck, O.; Straub, S.; Zaiss, M. Pros and Cons of Ultra-high-field MRI/MRS for Human Application. Prog. Nucl. Magn. Res. Sp. 2018, 109, 1–50.
  • Pykett, I. L.;. NMR Imaging in Medicine. Sci. Am. 1982, 246(5), 78–88. DOI: 10.1038/scientificamerican0582-78.
  • Antequera, T.; Caro, A.; Rodríguez, P. G.; Pérez-Palacios, T. Monitoring the Ripening Process of Iberian Ham by Computer Vision on Magnetic Resonance Imaging. Meat Sci. 2007, 76(3), 561–567. DOI: 10.1016/j.meatsci.2007.01.014.
  • Caballero, D.; Caro, A.; Rodríguez, P. G.; Durán, M. L.; Ávila, M. M.; Palacios, R.; Antequera, T.; Pérez-Palacios, T. Modeling Salt Diffusion in Iberian Ham by Applying MRI and Data Mining. J. Food Eng. 2016, 189, 115–122. DOI: 10.1016/j.jfoodeng.2016.06.003.
  • Fantazzini, P.; Gombia, M.; Schembri, P.; Simoncini, N.; Virgili, R. Use of Magnetic Resonance Imaging for Monitoring Parma Dry-cured Ham Processing. Meat Sci. 2009, 82(2), 219–227. DOI: 10.1016/j.meatsci.2009.01.014.
  • Manzoco, L.; Anese, M.; Marzona, S.; Innocente, N.; Lagazio, C.; Nicoli, M. C. Monitoring Dry-curing of San Daniele Ham by Magnetic Resonance Imaging. Food Chem. 2013, 141(3), 2246–2252. DOI: 10.1016/j.foodchem.2013.04.068.
  • Hansen, C. L.; Van Der Berg, F.; Ringgard, S.; Stodkilde-Jorgensen, H.; Karlsson, A. H. Diffusion of NaCl in Meat Studied by 1H and 23Na Magnetic Resonance Imaging (MRI). J. Food Eng. 2008, 31, 457–471.
  • Vestergaard, C.; Risum, J.; Adler-Nissen, J. 23Na-MRI Quantification of Sodium and Water Mobility in Pork during Brine Curing. Meat Sci. 2005, 69(4), 663–672. DOI: 10.1016/j.meatsci.2004.11.001.
  • Caballero, D.; Pérez-Palacios, T.; Caro, A.; Amigo, J. M.; Dahl, A. B.; Ersboll, B. K.; Antequera, T. Prediction of Pork Quality Parameters by Applying Fractals and Data Mining on MRI. Food Res. Int. 2017, 99, 739–747. DOI: 10.1016/j.foodres.2017.06.048.
  • Bajd, F.; Skrlep, M.; Candek-Potokar, M.; Sersa, I. MRI-aided Texture Analysis of Compressed Meat Products. J. Food Eng. 2017, 207, 108–118. DOI: 10.1016/j.jfoodeng.2017.03.026.
  • García-García, A. B.; Fernández-Valle, M. E.; Castejón, D.; Escudero, R.; Cambero, M. I. Use of MRI as a Predictive Tool for Physico-chemical and Rheologycal Features during Cured Ham Manufacturing. Meat Sci. 2019, 148, 171–180. DOI: 10.1016/j.meatsci.2018.10.015.
  • Ishiwatari, N.; Fukuoka, M.; Sakai, N. Effect of Protein Denaturation Degree on Texture and Water State of Cooked Meat. J. Food Eng. 2013, 117(3), 361–369. DOI: 10.1016/j.jfoodeng.2013.03.013.
  • Pérez-Palacios, T.; Caballero, D.; Antequera, T.; Durán, M. L.; Ávila, M. M.; Caro, A. Optimization of MRI Acquisition and Texture Analysis to Predict Physico-chemical Parameters of Loins by Data Mining. Food Bioprocess Tech. 2017, 10(4), 750–758. DOI: 10.1007/s11947-016-1853-4.
  • Ávila, M. M.; Evaluación de técnicas avanzadas de regresión y de características de textura en imágenes de resonancia magnética para determinar parámetros de calidad de productos cárnicos; Doctoral Thesis. University of Extremadura: Cáceres, Spain, 2018.
  • Caballero, D.; Algoritmos de extracción de características a partir de imágenes de resonancia magnética para evaluar parámetros de calidad en productos cárnicos mediante minería de datos; Doctoral Thesis. University of Extremadura: Cáceres, Spain. 2017.
  • Sonka, M.; Hlavac, V.; Boyle, R. Image Processing, Analysis, and Machine Vision; PWS Publishing: Pacific Grove, California, U.S.A., 1999.
  • Otsu, N.;. A Threshold Selection Method from Gray-level Histograms. IEEE Trans. Syst. Man Cybern. 1979, 9(1), 62–66. DOI: 10.1109/TSMC.1979.4310076.
  • Raof, R. A. A.; Salleh, Z.; Sahidan, M. Y.; Mashor, S. S.; Noor, M.; Mohamad, I. F.; Hasan, H. Color Thresholding Method for Image Segmentation Algorithm of Ziehl-Neelsen Sputum Slide Images. Lect. Notes Elect. Eng. 2008, 367, 212–217.
  • Caro, A.; Rodríguez, P. G.; Durán, M. L.; Antequera, T. Active Contours for Real Time Applications. In Perspective on Pattern Recognition; Flournier, M.D., Ed.; Nova Science Publishers Inc.: Hauppauge, New York, U. S. A, 2012; pp 173–186.
  • Molano, R.; Rodríguez, P. G.; Caro, A.; Durán, M. L. Finding the Largest Area Rectangle of Arbitrary Orientation in a Closed Contour. Appl. Math. Comput. 2012, 218, 9866–9874.
  • Caballero, D.; Caro, A.; Dahl, A. B.; Ersboll, B. K.; Amigo, J. M.; Pérez-Palacios, T.; Antequera, T. Comparison of Different Image Analysis Algorithms on MRI to Predict Physico-chemical and Sensory Attributes of Loin. Chemom. Intel. Lab. 2018, 180, 54–63. DOI: 10.1016/j.chemolab.2018.04.008.
  • Haralick, R. M.; Shanmugam, K.; Dinstein, I. Textural Features for Image Classification. IEEE Trans. Syst. Man, Cybern. 1973, 3(6), 610–621. DOI: 10.1109/TSMC.1973.4309314.
  • Galloway, M. M.;. Texture Analysis Using Gray Level Run Lengths. Comput. Gr. Image Process. 1975, 4(2), 172–179. DOI: 10.1016/S0146-664X(75)80008-6.
  • Siew, L. H.; Hodgson, R. M.; Wood, E. J. Texture Measures for Carpet Wear Assessment. IEEE Trans. Patt. Anal. Mach. Intel. 1988, 10(1), 92–104. DOI: 10.1109/34.3870.
  • Sun, C.; Wee, G. Neighbouring Gray Level Dependence Matrix. Comput. Vis. Graph. Image Process. 1982, 23(3), 341–352. DOI: 10.1016/0734-189X(83)90032-4.
  • Hibbert, D. B.;. Fractals in Chemistry. Chemom. Intel. Lab. 1991, 11(1), 1–11. DOI: 10.1016/0169-7439(91)80001-7.
  • Mandelbrot, B. B.;. The Fractal Geometry of Nature; WH Freeman and Co: New York, New York, U.S.A., 1982.
  • Caballero, D.; Caro, A.; Ávila, M. M.; Rodríguez, P. G.; Antequera, T.; Pérez-Palacios, T. New Fractal Features and Data Mining to Determine Food Quality Based on MRI. IEEE Lat. Am. Trans. 2017, 15(9), 1777–1784. DOI: 10.1109/TLA.2017.8015085.
  • Missiaen, J. M.; Thomas, G. Homogeneity Characterization of Binary Grain Mixtures Using a Variance Analysis of Two-dimensional Numerical Fractions. J. Phys. Condens. Matter. 1995, 7(15), 2937–2948. DOI: 10.1088/0953-8984/7/15/002.
  • Evans, O. D.; Kim, Y. Efficient Implementation of Image Warping on a Multimedia Processor. Real-Time Imaging. 1998, 4(6), 417–428. DOI: 10.1006/rtim.1998.7010.
  • Zarei, N.; Sepyani, A. Different Methods of Image Mapping, Its Advantages and Disadvantages. Int. Academ. J. Sci. Eng. 2016, 3, 1–10.
  • Sezgin, M.; Sankur, B. Survey over Image Thresholding Techniques and Quantitative Performance Evaluation. J. Electron. Imaging. 2004, 13(1), 146–165. DOI: 10.1117/1.1631315.
  • Vala, H. J.; Baxi, A. A Review on Otsu Image Segmentation Algorithm. Int. J. Adv. Res. Comput. Eng. Technol. 2013, 2, 387–389.
  • Ávila, M. M.; Caballero, D.; Durán, M. L.; Caro, A.; Pérez-Palacios, T.; Antequera, T. Including 3D-textures in a Computer Vision System to Analyse Quality Traits of Loin. Lect. Notes Comput. Sci. 2015, 9163, 456–465.
  • Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P. From Data Mining to Knowledge Discovery in Databases. Am. Assoc. Artif. Intel. 1996, 17, 37–54.
  • Bro, R.; Smilde, A. K. Principal component analysis. Anal. Methods. 2014, 6(9), 2812–2831. DOI: 10.1039/C3AY41907J
  • Safavian, R.; Landgrebe, D. A Survey of Decision Tree Classifier Methodology. IEEE Trans. Syst. Man Cybern. 1991, 21(3), 660–674. DOI: 10.1109/21.97458.
  • Fernández-Delgado, M.; Cernadas, E.; Barro, S.; Amorim, D. Do We Need Hundreds of Classifiers to Solve Real World Classification Problems? J. Mach. Learn. Res. 2014, 15, 1–30.
  • Bro, R.;. Multiway Calibration. Multilinear PLS. J Chemom. 1996, 10(1), 47–61. DOI: 10.1002/(SICI)1099-128X(199601)10:1<47::AID-CEM400>3.0.CO;2-C.
  • Rencher, A. C.; Christensen, W. F. Methods of Multivariate Analysis. Eds. John Wiley and Sons: New York, New York, U.S.A., 2012.
  • Stout, Q. F.;. Strict L Isotonic Regression. J. Optimiz. Theory App. 2012, 152(1), 121–135. DOI: 10.1007/s10957-011-9865-8.
  • McLachlan, G. J. Discriminant Analysis and Statistical Pattern Recognition. Eds. Wiley: New York, New York, U.S.A., 2004.
  • Caballero, D.; Antequera, T.; Caro, A.; Ávila, M. M.; Rodríguez, P. G.; Pérez-Palacios, T. Non-destructive Analysis of Sensory Traits of Dry-cured Loins by MRI-computer Vision Techniques and Data Mining. J Sci. Food Agric. 2017, 97(9), 2942–2952. DOI: 10.1002/jsfa.8132.
  • Ávila, M. M.; Caballero, D.; Antequera, T.; Durán, M. L.; Caro, A.; Pérez-Palacios, T. Applying 3D Textures Algorithms on MRI to Evaluate Quality Traits of Loin. J. Food Eng. 2018, 222, 258–266. DOI: 10.1016/j.jfoodeng.2017.11.028.
  • Caballero, D.; Antequera, T.; Caro, A.; Amigo, J. M.; Ersboll, B. K.; Dahl, A. B.; Pérez-Palacios, T. Analysis of MRI by Fractals for Prediction of Sensory Attributes: A Case Study in Loin. J. Food Eng. 2018, 227, 1–10. DOI: 10.1016/j.jfoodeng.2018.02.005.
  • Ávila, M. M.; Durán, M. L.; Caballero, D.; Antequera, T.; Pérez-Palacios, T.; Cernadas, E.; Fernández-Delgado, M. Magnetic Resonance Imaging, Texture Analysis and Regression Techniques to Non-destructively Predict the Quality Characteristics of Meat Pieces. Eng. Appl. Artif. Intel. 2019, 82, 110–125. DOI: 10.1016/j.engappai.2019.03.026.
  • Monziols, M.; Collewet, G.; Bonneau, M.; Mariette, F.; Davenel, A.; Kouba, M. Quantification of Muscle, Subcutaneous Fat and Intramuscular Fat in Pig Carcasses and Cuts by Magnetic Resonance Imaging. Meat Sci. 2006, 72(1), 146–154. DOI: 10.1016/j.meatsci.2005.06.018.
  • Bernau, M.; Kremer, P. V.; Lauterbach, E.; Tholen, E.; Petersen, B.; Pappenberger, E.; Scholz, A. M. Evaluation of Carcass Composition of Intact Boars Using Linear Measurements from Performance Testing, Dissection, Dual Energy X-ray Absorptiometry (DXA) and Magnetic Resonance Imaging (MRI). Meat Sci. 2015, 104, 58–66. DOI: 10.1016/j.meatsci.2015.01.011.
  • Torres, J. P.; Ávila, M. M.; Caro, A.; Pérez-Palacios, T.; Caballero, D. Non-destructively Prediction of Quality Parameters of Dry-cured Iberian Ham by Applying Computer Vision and Low-field MRI. Lect. Notes Comput. Sci. 2019, 11867, 498–507.
  • Davenel, A.; Seigneurin, F.; Collewet, G.; Remignon, H. Estimation of Poultry Breast Meat Yield: Magnetic Resonance Imaging as a Tool to Improve the Positioning of Ultrasonic Scanners. Meat Sci. 2000, 56(2), 153–158. DOI: 10.1016/S0309-1740(00)00034-6.
  • Bonny, J. M.; Zanca, M.; Tanguy, O. B.; Dedieu, V.; Joandel, S.; Renou, J. P. Characterization in Vivo of Muscle Fiber Types by Magnetic Resonance Imaging. Magn. Reson. Imaging. 1998, 16(2), 167–173. DOI: 10.1016/S0730-725X(97)00249-X.
  • Pérez-Palacios, T.; Antequera, T.; Molano, R.; Rodríguez, P. G.; Palacios, R. Sensory Traits Prediction in Dry-cured Hams from Fresh Product via MRI and Lipid Composition. J. Food Eng. 2010, 101(2), 152–157. DOI: 10.1016/j.jfoodeng.2010.06.015.
  • Pérez-Palacios, T.; Antequera, T.; Durán, M. L.; Caro, A.; Rodríguez, P. G.; Palacios, R. MRI-based Analysis of Feeding Background Effect on Fresh Iberian Ham. Food Chem. 2011, 126(3), 1366–1372. DOI: 10.1016/j.foodchem.2010.11.101.
  • Caballero, D.; Antequera, T.; Caro, A.; Durán, M. L.; Pérez-Palacios, T. Data Mining on MRI-computational Texture Features to Predict Sensory Characteristics in Ham. Food Bioprocess Tech. 2016, 9(4), 699–708. DOI: 10.1007/s11947-015-1662-1.
  • Bajd, F.; Skrlep, M.; Candek-Potokar, M.; Vidmar, J.; Sersa, I. Use of Multiparametric Magnetic Resonance Microscopy for Discrimination among Different Processing Protocols and Anatomical Positions of Slovenian Dry-cured Hams. Food Chem. 2016, 197, 1093–1101. DOI: 10.1016/j.foodchem.2015.11.103.
  • Bajd, F.; Skrlep, M.; Candek-Potokar, M.; Vidmar, J.; Sersa, I. Application of Quantitative Magnetization Transfer Magnetic Resonance Imaging for Characterization of Dry-cured Hams. Meat Sci. 2016, 122, 109–118. DOI: 10.1016/j.meatsci.2016.08.001.
  • Mahmoud-Ghoneim, D.; Bonny, J. M.; Renou, J. P.; De Certaines, J. D. Ex-vivo Magnetic Resonance Image Texture Analysis Can Discriminate Genotypic Origin in Bovine Meat. J. Sci. Food Agric. 2005, 85(4), 629–632. DOI: 10.1002/jsfa.1841.
  • Lee, S.; Lohumi, S.; Lim, H. S.; Gotoh, T.; Cho, B. K.; Jung, S. Determination of Intramuscular Fat Content in Beef Using Magnetic Resonance Imaging. J. Fac. Agric. 2015, 60, 157–162. Kyushu University
  • Cernadas, E.; Antequera, T.; Rodríguez, P. G.; Durán, M. L.; Gallardo, R.; Villa, D. Magnetic Resonance Imaging to Classify Loin from Iberian Pigs. In Magnetic Resonance in Food Science: A View to the Future; Webb, G.A., Belton, P.S., Gil, A.M., Delgadillo, I., Eds.; The royal society of chemistry: Cambridge, United Kingdom, 2001; pp 239–245.
  • Antequera, T.; Muriel, E.; Rodriguez, P. G.; Cernadas, E.; Ruiz, J. Magnetic Resonance Imaging as a Predictive Tool for Sensory Characteristics and Intramuscular Fat Content of Dry-cured Loin. J. Sci. Food Agric. 2003, 83(4), 268–274. DOI: 10.1002/jsfa.1306.
  • Cernadas, E.; Carrión, P.; Rodríguez, P. G.; Muriel, E.; Antequera, T. Analysing Magnetic Resonance Images of Iberian Pork Loin to Predict Its Sensorial Characteristics. Comput. Vis. Image Und. 2005, 98(2), 345–361. DOI: 10.1016/j.cviu.2004.08.004.
  • Bounhara, M.; Clerjon, S.; Damez, J. L.; Chevarin, C.; Portanguen, S.; Kondjoyan, A.; Bonny, J. M. Dynamic MRI and Thermal Simulation to Interpret Deformation and Water Transfer in Meat during Heating. J. Agric. Food Chem. 2011, 59(4), 1229–1235. DOI: 10.1021/jf103384d.
  • Tingle, J. M.; Pope, J. M.; Baumgartner, P. A.; Sarafis, V. Magnetic Resonance Imaging of Fat and Muscle Distribution in Meat. Int. J. Food Sci. Tech. 1995, 30(4), 437–446. DOI: 10.1111/j.1365-2621.1995.tb01391.x.

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