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.

中文翻译：

---

通过分形分析 MRI 以预测感官属性：腰部案例研究

摘要 本研究调查了使用分形算法分析肉制品，特别是腰部的 MRI，以确定腰部的感官参数。为此，评估了不同分形算法的能力（经典分形算法，CFA；分形纹理算法，FTA 和一点分形纹理算法，OPFTA）。此外，MRI的采集序列（梯度回波，GE；Spin Echo，SE和Turbo 3D，T3D）和数据挖掘的预测技术（等渗回归，IR和多元线性回归，MLR）对精度的影响预测进行了分析。这项研究的结果首先证明了分形算法分析肉制品 MRI 的能力。分析技术的不同组合可用于充分预测腰部的大多数感官属性 (R > 0.5)。然而，SE、OPFTA 和 MLR 的组合提供了最合适的结果。因此，它可以被提议作为传统食品技术方法的替代品。