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Dive into the research topics where Daniel Caballero is active.

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Featured researches published by Daniel Caballero.


Food and Bioprocess Technology | 2017

Optimization of MRI Acquisition and Texture Analysis to Predict Physico-chemical Parameters of Loins by Data Mining

Trinidad Pérez-Palacios; Daniel Caballero; Teresa Antequera; Maria Luisa Durán; Mar Ávila; Andrés Caro

The main objective of this study was to configure the acquisition and analysis of low-field magnetic resonance imaging (MRI) to predict physico-chemical characteristics of Iberian loin, evaluating the use of different MRI sequences (spin echo, SE; gradient echo, GE; turbo 3D, T3D), computational texture feature methods (GLCM, NGLDM, GLRLM, GLCM + NGLDM + GLRLM), and data mining techniques (multiple linear regression, MLR; isotonic regression, IR). Moderate to very good correlation coefficients and low mean absolute error were found when applying MLR or IR on any method of computational texture features from MRI acquired with SE or GE. For T3D sequence, accurate results are only obtained by applying IR on GLCM or GLCM + NGLDM + GLRLM methods. Considering not only the accuracy of the methodology but also consumed time and required resources, the use of SE sequences for MRI acquisition, GLCM method for MRI texture analysis, and MLR could be indicated for prediction physico-chemical characteristics of loin.


Journal of the Science of Food and Agriculture | 2017

Non‐destructive analysis of sensory traits of dry‐cured loins by MRI–computer vision techniques and data mining

Daniel Caballero; Teresa Antequera; Andrés Caro; María Mar Ávila; Pablo García Rodríguez; Trinidad Pérez-Palacios

BACKGROUND Magnetic resonance imaging (MRI) combined with computer vision techniques have been proposed as an alternative or complementary technique to determine the quality parameters of food in a non-destructive way. The aim of this work was to analyze the sensory attributes of dry-cured loins using this technique. For that, different MRI acquisition sequences (spin echo, gradient echo and turbo 3D), algorithms for MRI analysis (GLCM, NGLDM, GLRLM and GLCM-NGLDM-GLRLM) and predictive data mining techniques (multiple linear regression and isotonic regression) were tested. RESULTS The correlation coefficient (R) and mean absolute error (MAE) were used to validate the prediction results. The combination of spin echo, GLCM and isotonic regression produced the most accurate results. In addition, the MRI data from dry-cured loins seems to be more suitable than the data from fresh loins. CONCLUSIONS The application of predictive data mining techniques on computational texture features from the MRI data of loins enables the determination of the sensory traits of dry-cured loins in a non-destructive way.


Food Research International | 2017

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

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

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.


international conference on computer vision systems | 2015

Including 3D-textures in a Computer Vision System to Analyze Quality Traits of Loin

Mar Ávila; Daniel Caballero; M. Luisa Durán; Andrés Caro; Trinidad Pérez-Palacios; Teresa Antequera

Texture analysis by co-occurrences on magnetic resonance imaging MRI involves a non-invasive nor destructive method for studying the distribution of several texture features inside meat products. Traditional methods are based on 2D image sequences, which limit the distribution of texture to a single plane. That implies a loss of information when texture features are studied from different orientations. In this paper a new 3D algorithm is proposed and included in a computer vision system to study the distribution of textures in 3D images of Iberian loin from different orientations. The semantic interpretation of textural composition in each orientation is also reached.


IEEE Latin America Transactions | 2017

New fractal features and data mining to determine food quality based on MRI

Daniel Caballero; Andrés Caro; María Mar Ávila; Pablo García Rodríguez; Teresa Antequera; Trinidad Pérez Palacios

The extraction of textural information from images to explore parameters related to food quality is very common. In this paper, the extraction of quality features from MRI is performed by a new fractal algorithm and second order statistics, as an alternative to the classical texture approaches. The proposed method needs fewer features than classical textures, computing them with a lower computational complexity. Quality characteristics from MRI of Iberian loins are extracted to validate the practical application of the proposed algorithm. The new method is compared to the standard fractal algorithm and also to the classical texture approaches. Characteristics obtained by means of the new fractal algorithm, by the standard fractal algorithm, and by the three classical texture methods are correlated to the results obtained by using physico-chemical methods. The correlations achieve coefficients higher than 0.75. Therefore, the new algorithm could be used to calculate quality parameters of meat products in a non-destructive and efficient way, being also suitable for the meat industries to characterize meat products.


computer analysis of images and patterns | 2017

Development of a New Fractal Algorithm to Predict Quality Traits of MRI Loins

Daniel Caballero; Andrés Caro; José Manuel Amigo; Anders Bjorholm Dahl; Bjarne Kjær Ersbøll; Trinidad Pérez-Palacios

Traditionally, the quality traits of meat products have been estimated by means of physico-chemical methods. Computer vision algorithms on MRI have also been presented as an alternative to these destructive methods since MRI is non-destructive, non-ionizing and innocuous. The use of fractals to analyze MRI could be another possibility for this purpose. In this paper, a new fractal algorithm is developed, to obtain features from MRI based on fractal characteristics. This algorithm is called OPFTA (One Point Fractal Texture Algorithm). Three fractal algorithms were tested in this study: CFA (Classical fractal algorithm), FTA (Fractal texture algorithm) and OPFTA. The results obtained by means of these three fractal algorithms were correlated to the results obtained by means of physico-chemical methods. OPFTA and FTA achieved correlation coefficients higher than 0.75 and CFA reached low relationship for the quality parameters of loins. The best results were achieved for OPFTA as fractal algorithm (0.837 for lipid content, 0.909 for salt content and 0.911 for moisture). These high correlation coefficients confirm the new algorithm as an alternative to the classical computational approaches (texture algorithms) in order to compute the quality parameters of meat products in a non-destructive and efficient way.


International Journal of Food Engineering | 2017

Effect of Cooking Conditions on Quality Characteristics of Confit Cod: Prediction by MRI

Trinidad Pérez Palacios; Daniel Caballero; Sara Bravo; Jorge Mir Bel; Teresa Antequera

Abstract This study evaluates the effect of low temperature (60, 64 and 68 °C) and different times (15 and 20 min) of cooking on physicochemical and sensory characteristics of confit cod and analyzes confit cod in a non-destructive way by means of magnetic resonance imaging (MRI) and computer vision techniques. Higher scores for acceptability and flavor in 60 °C–20 min and 64 °C–20 min samples and some physicochemical differences were found. These results allow distinguishing four groups of confit cods as a function of cooking conditions – 60 °C–15 min/68 °C–15 min/68 °C–20 min/60 °C–20 min, 64 °C–15 min, 64 °C–20 min – and proposing to cook confit cod at 60–64 °C during 20 min. Prediction by means of computational texture features from MRI gave moderate-to-good correlation (0.6–0.75) for six quality attributes and very good-to-excellent relationship (0.75–1) for other six. Thus, computational texture features seem to be appropriate to determine the quality attributes of confit cod in a non-destructive way.


Journal of the Science of Food and Agriculture | 2018

Near-infrared spectroscopy-based analysis to study sensory parameters on pork loins as affected by cooking methods and conditions: Near-infrared spectroscopy-based analysis to study sensory parameters

Alberto González-Mohíno; Teresa Antequera; Sonia Ventanas; Daniel Caballero; Jorge Mir-Bel; Trinidad Pérez-Palacios

BACKGROUND The main objectives of this study were to evaluate the use of near infrared spectroscopy (NIRS) to classify pork loins under different methods and cooking conditions, and to predict sensory attributes of this product. RESULTS Samples were oven cooked at two temperatures (150 and 180 °C) for different times (45, 60 and 75 min) and confit cooked for different times (120, 180 and 240 min). All cooked loin samples were subjected to a Quantitative Descriptive Analysis by a trained panel. For classification, principal component analysis was performed based on the NIRS database, showing a good discrimination between loins samples subjected to different cooking conditions. Regarding prediction, a data mining technique (multiple linear regression) was applied on a database constructed with data from NIRS and sensory analysis. CONCLUSION The correlation coefficient and the mean absolute error obtained suggest that the calculated prediction equations of this study are valid to predict the changes in the sensory attributes depending on the cooking method and conditions used for pork loins.


computer analysis of images and patterns | 2017

Development of a New Fractal Algorithm to Predict Quality Traits of MRI Loins: Proceedings of the17th International Conference, Part I

Daniel Caballero; Andrés Caro; Jose Manuel Amigo Rubio; Anders Bjorholm Dahl; Bjarne Kjær Ersbøll; Trinidad Pérez-Palacios

The two volume set LNCS 10424 and 10425 constitutes the refereed proceedings of the 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017, held in Ystad, Sweden, in A ...


Journal of Food Engineering | 2014

Applying data mining and Computer Vision Techniques to MRI to estimate quality traits in Iberian hams

Trinidad Pérez-Palacios; Daniel Caballero; Andrés Caro; Pablo García Rodríguez; Teresa Antequera

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Andrés Caro

University of Extremadura

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Anders Bjorholm Dahl

Technical University of Denmark

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Bjarne Kjær Ersbøll

Technical University of Denmark

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Mar Ávila

University of Extremadura

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