Food and Bioprocess Technology

, Volume 10, Issue 4, pp 750–758 | Cite as

Optimization of MRI Acquisition and Texture Analysis to Predict Physico-chemical Parameters of Loins by Data Mining

  • Trinidad Pérez-Palacios
  • Daniel Caballero
  • Teresa Antequera
  • Maria Luisa Durán
  • Mar Ávila
  • Andrés Caro
Original Paper


The main objective of this study was to configure the acquisition and analysis of low-field magnetic resonance imaging (MRI) to predict physico-chemical characteristics of Iberian loin, evaluating the use of different MRI sequences (spin echo, SE; gradient echo, GE; turbo 3D, T3D), computational texture feature methods (GLCM, NGLDM, GLRLM, GLCM + NGLDM + GLRLM), and data mining techniques (multiple linear regression, MLR; isotonic regression, IR). Moderate to very good correlation coefficients and low mean absolute error were found when applying MLR or IR on any method of computational texture features from MRI acquired with SE or GE. For T3D sequence, accurate results are only obtained by applying IR on GLCM or GLCM + NGLDM + GLRLM methods. Considering not only the accuracy of the methodology but also consumed time and required resources, the use of SE sequences for MRI acquisition, GLCM method for MRI texture analysis, and MLR could be indicated for prediction physico-chemical characteristics of loin.


Low-field MRI sequence Computational texture features Data mining prediction Physico-chemical characteristics Loin 



The authors wish to acknowledge the funding received from the FEDER-MICIN-Infrastructure Research Project (UNEX10-1E-402). We also wish to thank the “Montesano” company from Jerez de los Caballeros (Badajoz), as well as the Animal Source Foodstuffs Innovation Service (SIPA) from the University of Extremadura, for their direct contribution and support.


  1. Antequera, T., Caro, A., Rodríguez, P. G., & Perez-Palacios, T. (2007). Monitoring the ripening process of Iberian ham by computer vision on magnetic resonance imaging. Meat Science, 76, 561–567.CrossRefGoogle Scholar
  2. Antequera, T., Muriel, E., Rodríguez, P. G., Cernadas, E., & Ruiz, J. (2007). Magnetic resonance imaging as a predictive tool for sensory characteristics and intramuscular fat content of dry-cured loin. Journal of the Science of Food and Agricultural, 83, 268–274.CrossRefGoogle Scholar
  3. Association of Official Analytical Chemist (2000). Official methods of analysis of AOAC International (17th ed.). Gaithersburg: AOAC International.Google Scholar
  4. Barlow, R. E., Bartholomew, D., Bremner, J. M., & Brunk, H. D. (1972). Statistical Inference under order restriction: the theory and application of isotonic regression. New York: Wiley.Google Scholar
  5. Borge, L. (1985). Estimación y contrastes de hipótesis en el modelo lineal general con restricciones de desigualdad. Doctoral thesis: University of Valladolid, Spain.Google Scholar
  6. Cernadas, E., Antequera, T., Rodriguez, P. G., Duran, M. L., Gallardo, R., & Villa, D. (2001). Magnetic resonance imaging to classify loin from Iberian pig. In G. A. Webb, P. S. Belton, A. M. Gil, & I. Delgadillo (Eds.), Magnetic resonance imaging in food science. A view to the future (pp. 239–245). Cambridge: The Royal Society of Chemistry.CrossRefGoogle Scholar
  7. Cernadas, E., Carrión, P., Rodriguez, P. G., Muriel, E., & Antequera, T. (2005). Analyzing magnetic resonance images of Iberian pork loin to predict its sensorial characteristics. Computer Vision and Image Understanding, 98, 345–361.CrossRefGoogle Scholar
  8. Colton, T. (1974). Statistical in medicine. New York: Little Brown and Co..Google Scholar
  9. Cortez, P., Cedeira, A., Almeida, F., Matos, T., & Reis, J. (2009). Modeling wine preferences by data mining from physicochemical properties. Decision Support System, 47, 547–553.CrossRefGoogle Scholar
  10. Cortez, P., Portelinha, S., Rodrigues, S., Cadavez, V., & Teixeira, A. (2006). Lamb meat quality assessment by support vector machines. Neural Processing Letters, 24, 41–51.CrossRefGoogle Scholar
  11. Fatazzini, P., Gombia, M., Schembri, P., Simoncini, N., & Virgili, R. (2009). Use of magnetic resonance imaging for monitoring Parma dry-cured ham processing. Meat Science, 82, 219–227.CrossRefGoogle Scholar
  12. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. American Association for Artificial Intelligence, 17, 37–54.Google Scholar
  13. Gandemer, G. (2002). Lipids in muscles and adipose tissues changes during processing and sensory properties of meat products. Meat Science, 62, 309–321.CrossRefGoogle Scholar
  14. Haralick, R. M., & Shapiro, L. G. (1993). Computer and robot vision. Chicago: Addison-Wesley.Google Scholar
  15. Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22, 679–688.CrossRefGoogle Scholar
  16. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: data mining, inference and prediction. New York: Springer-Verlag.CrossRefGoogle Scholar
  17. Holmes, G., Fletcher, D., & Reutermann, P. (2012). An application of data mining to fruit and vegetable sample identification using gas chromatography-mass spectrometry. In International congress on environmental modeling and software managing resources of a limited planet. Leipzig: Germany.Google Scholar
  18. Klaypradith, W., Kerdpiboon, S., & Singh, R. K. (2010). Application of artificial neural networks to predict the oxidation of menhaden fish oil obtained from Fourier transform infrared spectroscopy method. Food and Bioprocess Technology, 4, 475–480.CrossRefGoogle Scholar
  19. Manzoco, L., Anese, M., Marzona, S., Innocente, N., Lagazio, C., & Nicoli, M. C. (2013). Monitoring dry-curing of S. Daniele ham by magnetic resonance imaging. Food Chemistry, 141, 2246–2252.CrossRefGoogle Scholar
  20. Martın, L., Cordoba, J. J., Antequera, T., Timon, M. L., & Ventanas, J. (1998). Effects of salt and temperature on proteolysis during ripening of Iberian ham. Meat Science, 49, 145–153.CrossRefGoogle Scholar
  21. Mitchell, T. M. (1999). Machine learning and data mining. Communications of the ACM, 42, 30–36.CrossRefGoogle Scholar
  22. Molano, R., Rodríguez, P. G., Caro, A., & Durán, M. L. (2012). Finding the largest area rectangle of arbitrary orientation in a closed contour. Applied Mathematics and Computation, 218, 9866–9874.CrossRefGoogle Scholar
  23. Muriel, E., Ruiz, J., Martin, D., Petron, M. J., & Antequera, T. (2004). Physico-chemical and sensory characteristics of dry-cured loin from different Iberian pig lines. Food Science and Technology International, 10, 117–123.CrossRefGoogle Scholar
  24. Nong, Y. (2014). Data mining: theories, algorithms, and examples. Boca Raton, FL: CRC Press.Google Scholar
  25. Perez-Palacios, T., Antequera, T., Duran, M. L., Caro, A., Rodriguez, P. G., & Palacios, R. (2011). MRI-based analysis of feeding background effect on fresh Iberian ham. Food Chemistry, 126, 1366–1372.CrossRefGoogle Scholar
  26. Perez-Palacios, T., Ruiz, J., Martin, D., Muriel, E., & Antequera, T. (2008). Comparison of different methods for total lipid quantification. Food Chemistry, 110, 1025–1029.CrossRefGoogle Scholar
  27. Pérez-Palacios, T., Antequera, T., Durán, M. L., Caro, A., Rodríguez, P. G., & Ruiz, J. (2010). MRI-based analysis, lipid composition and sensory traits for studying Iberian dry-cured hams from pigs fed with different diets. Food Chemistry, 126, 1366–1372.CrossRefGoogle Scholar
  28. Perez-Palacios, T., Caballero, D., Caro, A., Rodriguez, P. G., & Antequera, T. (2014). Applying data mining and computer vision techniques to MRI to estimate quality traits in Iberian hams. Journal of Food Engineering, 131, 82–88.CrossRefGoogle Scholar
  29. Ramírez, M. R., & Cava, R. (2007). Effect of Iberian x Duroc genotype on dry-cured loin quality. Meat Science, 76, 333–341.CrossRefGoogle Scholar
  30. Resurreccion, A. V. A. (2003). Sensory aspects of consumer choices for meat and meat products. Meat Science, 66, 11–20.CrossRefGoogle Scholar
  31. Ruiz, J., Garcia, C., Muriel, E., Andres, A. I., & Ventanas, J. (2002). Influence of sensory characteristics on the acceptability of dry-cured ham. Meat Science, 61, 347–354.CrossRefGoogle Scholar
  32. Siew, L. H., Hodgson, R. M., & Wood, E. J. (1988). Texture measures for carpet wear assessment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10, 92–104.CrossRefGoogle Scholar
  33. Song, Y. H., Kim, S. J., & Lee, S. K. (2002). Evaluation of ultrasound for prediction of carcass meat yield and meat quality in Korean native cattle. Asian Journal Animal Science, 15, 591–595.CrossRefGoogle Scholar
  34. Sonka, M., Hlavac, V., & Boyle, R. (1999). Image processing, analysis, and machine vision. Stanford: International Thomsom Publishing ITP.Google Scholar
  35. Toldrá, F., Flores, M., & Sanz, Y. (1997). Dry-cured ham flavour: enzymatic generation and process influence. Food Chemistry, 59, 523–530.CrossRefGoogle Scholar
  36. Utrilla, M. C., Soriana, A., & García Ruiz, A. (2010). Quality attributes of pork loin with different levels of marbling from Duroc and Iberian cross. Journal of Food Quality, 33, 802–820.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Trinidad Pérez-Palacios
    • 1
  • Daniel Caballero
    • 1
  • Teresa Antequera
    • 1
  • Maria Luisa Durán
    • 2
  • Mar Ávila
    • 2
  • Andrés Caro
    • 2
  1. 1.Food Technology, Research Institute of Meat and Meat Product (IproCar)University of ExtremaduraCáceresSpain
  2. 2.Computer Science Department, Research Institute of Meat and Meat Product (IproCar)University of ExtremaduraCáceresSpain

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