Prediction of pork quality parameters by applying fractals and data mining on MRI.

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition 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) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy.

[1]  Andrés Caro,et al.  Monitoring the ripening process of Iberian ham by computer vision on magnetic resonance imaging. , 2007, Meat science.

[2]  Trinidad Pérez-Palacios,et al.  Applying data mining and Computer Vision Techniques to MRI to estimate quality traits in Iberian hams , 2014 .

[3]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[4]  H. D. Brunk,et al.  Statistical inference under order restrictions : the theory and application of isotonic regression , 1973 .

[5]  Andrés Caro,et al.  Modeling salt diffusion in Iberian ham by applying MRI and data mining , 2016 .

[6]  Gerrit Polder,et al.  Measuring surface distribution of carotenes and chlorophyll in ripening tomatoes using imaging spectrometry , 2004 .

[7]  R. Virgili,et al.  Use of Magnetic Resonance Imaging for monitoring Parma dry-cured ham processing. , 2009, Meat science.

[8]  José Manuel Amigo,et al.  Identification and quantification of turkey meat adulteration in fresh, frozen-thawed and cooked minced beef by FT-NIR spectroscopy and chemometrics. , 2016, Meat science.

[9]  Jorge Ruiz,et al.  MRI-based analysis, lipid composition and sensory traits for studying Iberian dry-cured hams from pigs fed with different diets , 2010 .

[10]  Andrés Caro,et al.  Non-destructive analysis of sensory traits of dry-cured loins by MRI-computer vision techniques and data mining. , 2017, Journal of the science of food and agriculture.

[11]  M. Manera,et al.  Local connected fractal dimension analysis in gill of fish experimentally exposed to toxicants. , 2016, Aquatic toxicology.

[12]  F. Pedreschi,et al.  Color changes in the surface of fresh cut meat: A fractal kinetic application , 2013 .

[13]  Teresa Antequera,et al.  Data Mining on MRI-Computational Texture Features to Predict Sensory Characteristics in Ham , 2016, Food and Bioprocess Technology.

[14]  I. Arzate-Vázquez,et al.  Nanoindentation study on apple tissue and isolated cells by atomic force microscopy, image and fractal analysis , 2016 .

[15]  R. Virgili,et al.  Magnetic resonance imaging and relaxation analysis to predict noninvasively and nondestructively salt-to-moisture ratios in dry-cured meat. , 2005, Magnetic resonance imaging.

[16]  Maria Luisa Durán,et al.  Magnetic resonance imaging to classify loin from iberian pig , 2001 .

[17]  Andrés Caro,et al.  MRI-based analysis of feeding background effect on fresh Iberian ham , 2011 .

[18]  T. Pérez-Palacios,et al.  Comparison of different methods for total lipid quantification in meat and meat products. , 2008, Food chemistry.

[19]  R. K. Agrawal,et al.  First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images , 2012 .

[20]  Rob J. Hyndman,et al.  Another Look at Forecast Accuracy Metrics for Intermittent Demand , 2006 .

[21]  Tom M. Mitchell,et al.  Machine Learning and Data Mining , 2012 .

[22]  Pablo García Rodríguez,et al.  Analyzing magnetic resonance images of Iberian pork loin to predict its sensorial characteristics , 2005, Comput. Vis. Image Underst..

[23]  W. Horwitz,et al.  Official methods of analysis of AOAC International , 2010 .

[24]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[25]  M. C. Utrilla,et al.  QUALITY ATTRIBUTES OF PORK LOIN WITH DIFFERENT LEVELS OF MARBLING FROM DUROC AND IBERIAN CROSS , 2010 .

[26]  T. Antequera,et al.  Physico-Chemical and Sensory Characteristics of Dry-Cured Loin from Different Iberian Pig Lines , 2004 .

[27]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[28]  Richard Ipsen,et al.  Using fractal image analysis to characterize microstructure of low-fat stirred yoghurt manufactured with microparticulated whey protein , 2012 .

[29]  Piotr Zapotoczny,et al.  Evaluation of the quality of cold meats by computer-assisted image analysis , 2016 .

[30]  Paulo Cortez,et al.  Lamb Meat Quality Assessment by Support Vector Machines , 2006, Neural Processing Letters.

[31]  Damian G. Kelty-Stephen,et al.  Fractal scaling in bottlenose dolphin (Tursiops truncatus) echolocation: A case study , 2016 .

[32]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[33]  Knut Conradsen,et al.  Boundary Fractal Analysis of Two Cube-oriented Grains in Partly Recrystallized Copper , 2015 .

[34]  Corrado Lagazio,et al.  Monitoring dry-curing of S. Daniele ham by magnetic resonance imaging. , 2013, Food chemistry.

[35]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[36]  Junichi Sugiyama,et al.  Near-infrared imaging spectroscopy based on sugar absorption band for melons. , 2002, Journal of agricultural and food chemistry.

[37]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[38]  E. Cernadas,et al.  Magnetic resonance imaging as a predictive tool for sensory characteristics and intramuscular fat content of dry-cured loin , 2003 .

[39]  Franco Pedreschi,et al.  Correlation of the fractal enzymatic browning rate with the temperature in mushroom, pear and apple slices , 2016 .

[40]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[41]  Andrés Caro,et al.  Finding the largest area rectangle of arbitrary orientation in a closed contour , 2012, Appl. Math. Comput..

[42]  Andrés Caro,et al.  Optimization of MRI Acquisition and Texture Analysis to Predict Physico-chemical Parameters of Loins by Data Mining , 2017, Food and Bioprocess Technology.