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Chemical-instrumental-sensory traits and data mining for classifying dry-cured Iberian shoulders from pigs with different diets

  • Daniel CaballeroEmail author
  • María Asensio
  • Carlos Fernández
  • Raquel Reina
  • Juan García-Casco
  • Noelia Martín
  • Antonio Silva
Original Paper
  • 12 Downloads

Abstract

Iberian pigs are an autochthonous porcine breed exclusively from the south western of Iberian Peninsula. In this study, the main objective was to classify dry-cured Iberian shoulders from pigs with different diets. Thus, morphology, physico-chemical and sensory parameters, fatty acid profile and volatile compounds were determined. From this data, two datasets were created, for training and validation purpose. Results on this study, firstly demonstrate the capability of data mining techniques to classify shoulder as function of their different diets by using different chemical-instrumental-sensory parameters. Different classification models were tested in the training datasets. After that, all classification models were performed in the validation datasets and the model of J48 decision tree and fatty acid profile reached the best results (Sensitivity and Specificity > 0.750). From this classification model, a software application was developed for determining the diet of the Iberian pigs. This application could be used for the meat industries and inspection agencies.

Keywords

Decision trees Fatty acid profile Aromatic compounds Morphometric measurements Quality parameters 

Abbreviations

FA

Fatty acids

MUFA

Mono-unsaturated fatty acids

KDD

Knowledge discovery in databases

100AG

Diet based on acorn and grass 100%

75AG

Diet based on acorn and grass 75% and feed 25%

50AG

Diet based on acorn and grass 50% and feed 50%

25AG

Diet based on acorn and grass 25% and feed 75%

0AG

Diet based on feed 100%

GC-FID

Gas chromatography flame ionisation detector

SPME

Solid phase micro extraction

GC–MS

Gas chromatography mass spectrometry

SENS

Sensitivity

SPEC

Specificity

TBARS

Thiobarbituric acid-reactive substance

TEP

Tetraethoxypropane

MDA

Malonaldehyde

FAME

Fatty acid methyl esters

HS

Headspace

LRI

Linear retention indexes

AU

Area units

WEKA

Waikato environment for knowledge analysis

DT

Decision tree

CAL

Calibration

VAL

Validation

TP

True positive

TN

True negative

FP

False positive

FN

False negative

ANOVA

Analysis of variance

SFA

Saturated fatty acids

PUFA

Poly-unsaturated fatty acids

LDA

Lateral discrimination analysis

RF

Random forest

K-NN

K-nearest neighbours

RBS

Rules based systems

ANN

Artificial neural networks

Notes

Acknowledgements

Daniel Caballero thanks the “Junta de Extremadura” for the post-doctoral Grant (PO17017).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Animal Source Foodstuffs Innovation Services (SiPA)University of ExtremaduraCáceresSpain
  2. 2.Chemometrics and Analytical Technology, Department of Food Science, Faculty of ScienceUniversity of CopenhagenFrederiksberg CDenmark
  3. 3.Departamento de Mejora Genética Animal, Instituto Nacional de Investigación Y Tecnología Agraria y Alimentaria (INIA)MadridSpain

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