Analysis of MRI by fractals for prediction of sensory attributes: A case study in loin

Daniel Caballero*, Teresa Antequera, Andrés Caro, José Manuel Amigo, Bjarne Kjær Ersbøll, Anders Bjorholm Dahl, Trinidad Pérez-Palacios

*Corresponding author for this work

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Abstract

This study investigates the use of fractal algorithms to analyse MRI of meat products, specifically loin, in order to determine sensory parameters of loin. For that, the capability of different fractal algorithms was evaluated (Classical Fractal Algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA). Moreover, the influence of the acquisition sequence of MRI (Gradient echo, GE; Spin Echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple Linear regression, MLR) on the accuracy of the prediction was analysed. Results on this study firstly demonstrate the capability of fractal algorithms to analyse MRI from meat product. Different combinations of the analysed techniques can be applied for predicting most sensory attributes of loins adequately (R > 0.5). However, the combination of SE, OPFTA and MLR offered the most appropriate results. Thus, it could be proposed as an alternative to the traditional food technology methods.
Original languageEnglish
JournalJournal of Food Engineering
Volume227
Pages (from-to)1-10
Number of pages10
ISSN0260-8774
DOIs
Publication statusPublished - 2018

Keywords

  • Fractals
  • Meat
  • MRI analysis
  • Prediction
  • Sensory traits

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