Journal
JOURNAL OF FOOD ENGINEERING
Volume 227, Issue -, Pages 1-10Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2018.02.005
Keywords
Fractals; MRI analysis; Data mining; Prediction sensory traits; Meat
Categories
Funding
- COST association
- Farm Animal Imaging action (FAIM) [COST-FA1102]
- European Social Fund [COST-STSM-FA1102-26642]
- FEDER-MICCIN [UNEX-10-1E-402]
- Junta de Extremadura [GRU15113, GRU15173]
Ask authors/readers for more resources
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, (C) 2018 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available