title: Non-destructive analysis of sensory traits of dry-cured loins by MRI-computer vision techniques and data mining.
abstract: BACKGROUND
  Magnetic resonance imaging (MRI) combined with computer vision techniques have been proposed as an alternative or complementary technique to determine the quality parameters of food in a non-destructive way. The aim of this work was to analyze the sensory attributes of dry-cured loins using this technique. For that, different MRI acquisition sequences (spin echo, gradient echo and turbo 3D), algorithms for MRI analysis (GLCM, NGLDM, GLRLM and GLCM-NGLDM-GLRLM) and predictive data mining techniques (multiple linear regression and isotonic regression) were tested.
  
  
  RESULTS
  The correlation coefficient (R) and mean absolute error (MAE) were used to validate the prediction results. The combination of spin echo, GLCM and isotonic regression produced the most accurate results. In addition, the MRI data from dry-cured loins seems to be more suitable than the data from fresh loins.
  
  
  CONCLUSIONS
  The application of predictive data mining techniques on computational texture features from the MRI data of loins enables the determination of the sensory traits of dry-cured loins in a non-destructive way. © 2016 Society of Chemical Industry.
authors.affiliation: Food Technology, Meat and Meat Products Research Institute, University of Extremadura, 10003, Cáceres, Spain.
authors.forename: Daniel
authors.initials: D
authors.lastname: Caballero
authors.affiliation: Food Technology, Meat and Meat Products Research Institute, University of Extremadura, 10003, Cáceres, Spain.
authors.forename: Teresa
authors.initials: T
authors.lastname: Antequera
authors.affiliation: Department of Computer Science, Meat and Meat Products Research Institute, University of Extremadura, 10003, Cáceres, Spain.
authors.forename: Andrés
authors.initials: A
authors.lastname: Caro
authors.affiliation: Department of Computer Science, Meat and Meat Products Research Institute, University of Extremadura, 10003, Cáceres, Spain.
authors.forename: María Del Mar
authors.initials: MDM
authors.lastname: Ávila
authors.affiliation: Department of Computer Science, Meat and Meat Products Research Institute, University of Extremadura, 10003, Cáceres, Spain.
authors.forename: Pablo
authors.initials: P
authors.lastname: G Rodríguez
authors.affiliation: Food Technology, Meat and Meat Products Research Institute, University of Extremadura, 10003, Cáceres, Spain.
authors.forename: Trinidad
authors.initials: T
authors.lastname: Perez-Palacios
mesh_terms: D000818:Animals; D057225:Data Mining; D005519:Food Preservation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008461:Meat Products / Q000032:analysis*; D013552:Swine; D013649:Taste
keywords: Iberian loin; computational texture features; data mining; magnetic resonance imaging; sensory traits