Journal article
Analysis of MRI by fractals for prediction of sensory attributes: A case study in loin
University of Extremadura1
University of Copenhagen2
Department of Applied Mathematics and Computer Science, Technical University of Denmark3
Statistics and Data Analysis, Department of Applied Mathematics and Computer Science, Technical University of Denmark4
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark5
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.
Language: | English |
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Year: | 2018 |
Pages: | 1-10 |
ISSN: | 18735770 and 02608774 |
Types: | Journal article |
DOI: | 10.1016/j.jfoodeng.2018.02.005 |
ORCIDs: | 0000-0003-1319-1312 , Ersbøll, Bjarne Kjær and Dahl, Anders Bjorholm |