Prediction of Quality Features in Iberian Ham by Applying Data Mining on Data From MRI and Computer Vision Techniques

This paper aims to predict the quality features of an Iberian hams by using non-destructive methods of analysis and data mining. An Iberian hams were analyzed by Magnetic Resonance Imaging (MRI) and Computer Vision Techniques (CVT) throughout their ripening process and physico-chemical parameters fr...

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Bibliographic Details
Published in:International journal of data mining & knowledge management process 2014-03, Vol.4 (2), p.1-11
Main Authors: Caballero, Daniel, Caro, Andrés, Perez-Palacios, Trinidad, G. Rodriguez, Pablo, Palacios, Ramón
Format: Article
Language:English
Subjects:
Computer vision
Data mining
Estimates
Ham
Magnetic resonance imaging
Mathematical analysis
Nondestructive testing
Online Access:Get full text
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Summary:This paper aims to predict the quality features of an Iberian hams by using non-destructive methods of analysis and data mining. An Iberian hams were analyzed by Magnetic Resonance Imaging (MRI) and Computer Vision Techniques (CVT) throughout their ripening process and physico-chemical parameters from them were also measured. The obtained data were used to create an initial database. Deductive techniques of data mining were used to estimate new data, allowing the insertion of new records in the database. Predictive techniques of data mining were applied (multiple linear regression) on MRI-CVT data, achieving prediction equations of weight, moisture and lipid content. Finally, data from prediction equations were compared to data determined by physical-chemical analysis, obtaining high correlation coefficients in most cases. Therefore, data mining, MRI and CVT are suitable tools to estimate quality traits of Iberian hams. This would improve the control of the ham processing in a non-destructive way.
ISSN:2231-007X
2230-9608
DOI:10.5121/ijdkp.2014.4201