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

Abstract

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.

Keywords

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

Notes

Acknowledgments

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.

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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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