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

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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.

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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

References

  1. R. Cava, J. Ventanas, J. Ruiz, A.I. Andrés, T. Antequera, Sensory characteristics of Iberian ham: influence of rearing system and muscle location. Food Sci. Techol. Int. 6, 235–242 (2000)

    Article  Google Scholar 

  2. S. Ventanas, J. Ventanas, J. Ruiz, M. Estévez, Iberian pigs for the development of high quality cured products, in Recent Research in Development in Agricultural and Food Chemistry, ed. by S.G. Paraday (Research Singpost, Trivandrum, 2005), pp. 27–53

    Google Scholar 

  3. J. Ruiz, J. Ventanas, R. Cava, M.L. Timón, C. Garcia, Sensory characteristics of Iberian ham: influence of processing time and slice location. Food Res. Int. 31, 53–58 (1998)

    Article  Google Scholar 

  4. J. Ruiz, C. Garcia, E. Muriel, A.I. Andrés, J. Ventanas, Influence of sensory characteristics on the acceptability of dry-cured ham. Meat Sci. 61, 347–354 (2002)

    Article  CAS  PubMed  Google Scholar 

  5. A.V.A. Resurreción, Sensory aspects of consumer choices for meat and meat products. Meat Sci. 66, 11–20 (2004)

    Article  Google Scholar 

  6. G. Gandermer, Lipids in muscles and adipose tissues changes during processing and sensory properties of meat products. Meat Sci. 62, 309–321 (2002)

    Article  Google Scholar 

  7. E. Muriel, J. Ruiz, J. Ventanas, M.J. Petrón, T. Antequera, Meat quality characteristics in different lines of Iberian pigs. Meat Sci. 67, 299–307 (2004)

    Article  PubMed  Google Scholar 

  8. J.F. Tejeda, G. Gandermer, T. Antequera, M. Viau, C. Garcia, Lipid traits of muscles as related to genotype and fattening diet in Iberian pigs: total intramuscular lipids and triacyglycerols. Meat Sci. 60, 357–363 (2002)

    Article  CAS  PubMed  Google Scholar 

  9. T. Pérez-Palacios, T. Antequera, M.L. Durán, A. Caro, P.G. Rodríguez, J. Ruiz, MRI-based analysis, lipid composition and sensory traits for studying Iberian dry-cured hams from pigs fed with different diets. Food Res. Int. 43, 248–254 (2010)

    Article  CAS  Google Scholar 

  10. D. Caballero, M. Asensio, C. Fernández, N. Martín, A. Silva, Classifying different Iberian pig genetic lines by applying chemical-instrumental parameters of dry-cured Iberian shoulders. J. Food Sci. Technol. 55, 4589–4599 (2018)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. D.S. Mottram, Flavour information in meat and meat products: a review. Food Chem. 62, 415–424 (1998)

    Article  CAS  Google Scholar 

  12. D. Machielis, L. Istasse, Solid-phase microextraction evaluation of key aroma compounds in cooked beef meat as influenced by breed and diet. in 48th International Congress on Meat Science and Technology (ICOMST). (Rome, 2002)

  13. L. Martin, J.J. Córdoba, T. Antequera, M.L. Timón, J. Ventanas, Effects of salt and temperature on proteolysis during ripening of Iberian ham. Meat Sci. 49, 145–153 (1998)

    Article  CAS  PubMed  Google Scholar 

  14. F. Toldrá, M. Flores, Y. Sanz, Dry-cured ham flavour: enzymatic generation and process influence. Food Chem. 59, 523–530 (1997)

    Article  Google Scholar 

  15. A. Silva, R. Reina, J. García-Casco, J. Ventanas, Chemical-instrumental-sensory parameters and chemometrics as tools to discriminate among the quality categories of dry-cured Iberian shoulder. Grasas Aceites 64(2), 201–209 (2013)

    Article  CAS  Google Scholar 

  16. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, From data mining to knowledge discovery in databases. AI Mag. 17, 37–54 (1996)

    Google Scholar 

  17. S. Sayad, Real Time Data Mining (Self-Help Publishers, Cambridge, 2011)

    Google Scholar 

  18. B.L. Batista, L.R.S. Da Silva, B.A. Rocha, J.L. Rodrigues, A.A. Beretta-Silva, T.O. Bonates, V.S.D. Gomes, R.M. Barbosa, F. Barbosa, Multi-element determination in Brazilian honey samples by inductively coupled mass spectrometry and estimation of geographic origin with data mining techniques. Food Res. Int. 29, 209–215 (2012)

    Article  CAS  Google Scholar 

  19. T. Pérez-Palacios, D. Caballero, S. Bravo, J. Mir-Bel, T. Antequera, Effect of cooking conditions on quality characteristics of confit cod: prediction by MRI. Int. J. Food Eng. 13(8), 20160311 (2017)

    Article  CAS  Google Scholar 

  20. G. Holmes, D. Fletcher, P. Reutermann, An application of data mining to fruit and vegetable sample identification using gas chromatography-mass spectrometry. in X International Congress on Environmental Modeling and Software Managing Resources of a Limited Planet. (Leipzig, 2012)

  21. D. Caballero, A. Caro, P.G. Rodríguez, M.L. Durán, M.M. Ávila, R. Palacios, T. Antequera, T. Pérez-Palacios, Modeling salt diffusion in Iberian ham by applying MRI and data mining. J. Food Eng. 189, 115–122 (2016)

    Article  CAS  Google Scholar 

  22. T. Pérez-Palacios, T. Antequera, M.L. Durán, A. Caro, P.G. Rodríguez, R. Palacios, MRI-based analysis of feeding background effect on fresh Iberian ham. Food Chem. 126, 1366–1372 (2011)

    Article  CAS  Google Scholar 

  23. J. Folch, M. Lees, G.H. Sloane-Stanley, A simple method for the isolation and purification of total lipids from animal tissues. J. Biol. Chem. 226(1), 497–509 (1957)

    CAS  Google Scholar 

  24. Association of Official Analytical Chemistry (AOAC), Official Methods of Analysis of AOAC International. Vols. 1 and 2, 17th edn. (AOAC International, Gaithersburg, 2000)

    Google Scholar 

  25. H.C. Hornsey, The color of cooked cured pork. Estimation of the nitric oxide-haem pigments. J. Sci. Food Agric. 7, 534–541 (1956)

    Article  CAS  Google Scholar 

  26. A.M. Salih, D.M. Smith, J.F. Price, L.E. Dawson, Modified extraction 2-thibarbituric acid method for measuring lipid oxidation in poultry. Poult. Sci. 66, 1483–1488 (1987)

    Article  CAS  PubMed  Google Scholar 

  27. S.R. Sandler, W. Karo (eds.), Source Book of Advanced Organic Laboratory Preparations (Academic Press, San Diego, 1992)

    Google Scholar 

  28. S. Ventanas, J. Ventanas, J. Tovar, C. García, M. Estévez, Extensive feeding versus oleic acid and tocopherol enriched mixed diets for the production of Iberian dry-cured hams: effect on chemical composition, oxidative status and sensory traits. Meat Sci. 77, 246–256 (2007)

    Article  CAS  PubMed  Google Scholar 

  29. C. Garcia, J. Ventanas, T. Antequera, J. Ruiz, R. Cava, P. Álvarez, Measuring sensory quality of Iberian ham by Rasch model. J. Food Qual. 19, 397–412 (1996)

    Article  Google Scholar 

  30. H. Stone, J. Sidel, S. Oliver, A. Woolsey, R.C. Singleton, Sensory evaluation by quantitative descriptive analysis. Food Tech. 28, 24–34 (1974)

    Google Scholar 

  31. S. Drazin, M. Montag, Decision Tree Analysis Using WEKA. Machine Learning-Project II (University of Miami, Miami, 2012)

    Google Scholar 

  32. R. Safavian, D. Landgrebe, A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)

    Article  Google Scholar 

  33. J.R. Quinlan (ed.), C4.5.: Programs for Machine Learning (Morgan Kaufmann Publishers, San Francisco, 1993)

    Google Scholar 

  34. M.N. Anyanwu, S.G. Shiva, Comparative analysis of serial decision tree classification algorithms. Int. J. Comput. Sci. Secur. (IJCSS) 3, 230–240 (2009)

    Google Scholar 

  35. A. Priyam, G.R. Abhijeeta, A. Rathee, S. Srivatstava, Comparative analysis of decision tree classification algorithms. Int. J. Curr. Eng. Technol. (IJCET) 3(2), 334–337 (2013)

    Google Scholar 

  36. J. Demsar, Statistical comparisons of classifier over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    Google Scholar 

  37. D.J. Hand, Assessing the performance of classification methods. Int. Stat. Rev. 80, 400–414 (2012)

    Article  Google Scholar 

  38. R. Reina, P. López-Buesa, J. Sánchez del Pulgar, J. Ventanas, C. García, Effect of IGF-II (insulin-like growth factor-II) genotype on the quality of dry-cured hams and shoulders. Meat Sci. 92, 562–568 (2012)

    Article  CAS  PubMed  Google Scholar 

  39. A.I. Andrés, S. Ventanas, J. Ventanas, R. Cava, J. Ruiz, Physico-chemical changes throughout the ripening of dry-cured hams with different salt content and processing conditions. Eur. Food Res. Technol. 221, 30–35 (2005)

    Article  CAS  Google Scholar 

  40. J.J. Córdoba, E. Aranda, J.M. Benito, Alteraciones originadas por microorganismos, ácaros e insectos en jamones Ibéricos, in Tecnología del Jamón Ibérico, ed. by J. Ventanas (Mundi-Prensa, Madrid, 2001), pp. 465–488

    Google Scholar 

  41. J. Ventanas, Jamón ibérico y Serrano: Fundamentos de la elaboración y la calidad (Mundi-Prensa, Madrid, 2012)

    Google Scholar 

  42. C.M. Rosell, F. Toldrá, Effect of curing agent on m-calpain activity throuhgout the curing process. Eur. Food Res. Technol. 203(4), 320–325 (1996)

    CAS  Google Scholar 

  43. E. Muriel, T. Antequera, J. Ruiz, Utilización de piensos con elevado contenido en ácido oleico. Influencia sobre la composición de la grasa intramuscular en cerdos Ibéricos. Eurocarne 103, 179–190 (2002)

    Google Scholar 

  44. C. García, J.J. Berdagué, T. Antequera, C.J. López-Bote, J.J. Córdoba, J. Ventanas, Volatile components of dry-cured Iberian ham. Food Chem. 41, 23–32 (1991)

    Article  Google Scholar 

  45. I.L. Hinrichsen, H.J. Andersen, Volatile compounds and chemical changes in cured pork: role of three halotolerant bacteria. J. Agric. Food Chem. 42, 1537–1542 (1994)

    Article  CAS  Google Scholar 

  46. E. Cernadas, M. Fernández-Delgado, E. González-Rufino, P. Carrión, Influence of normalization and color space to color texture classification. Pattern Recognit. 61, 120–138 (2017)

    Article  Google Scholar 

  47. M. Fernández-Delgado, E. Cernadas, S. Barro, D. Amorim, Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15, 3133–3181 (2014)

    Google Scholar 

  48. A.P. Hearty, M.J. Gibney, Analysis of meal patterns with use of supervised data mining techniques—artificial neural networks and decision trees. Am. J. Clin. Nutr. 88, 1632–1642 (2008)

    Article  CAS  PubMed  Google Scholar 

  49. J. Fernández, I. Clemente, C. Amador, A. Membrillo, P. Azor, A. Molina, Use of different sources of information for the recovery and genetic management of endagered populations: example with the extreme case of Iberian pig Dorado strain. Livest. Sci. 149, 282–288 (2012)

    Article  Google Scholar 

  50. Ministerio de Agricultura, Pesca, Alimentación y Medio Ambiente (MAPAMA). Datos de las denominaciones de origen protegidas (D.O.P.), Indicaciones geográficas protegidas (I.G.P.) y Especialidades Tradicionales Garantizadas (E.T.G.) de Productos Agroalimentarios. Madrid, Spain: Centro de Publicaciones del MAPAMA. (2016)

  51. M.A. Sentandreu, E. Sentandreu, Authenticity of meat products: tools against fraud. Food Res. Int. 60, 19–29 (2014)

    Article  Google Scholar 

  52. N.Z. Ballin, Authentication of meat and meat products. Meat Sci. 86(3), 577–587 (2010)

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

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

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Authors and Affiliations

  1. Animal Source Foodstuffs Innovation Services (SiPA), University of Extremadura, Av/Universidad S/N, 10003, Cáceres, Spain

    Daniel Caballero, María Asensio, Carlos Fernández, Raquel Reina, Noelia Martín & Antonio Silva

  2. Chemometrics and Analytical Technology, Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958, Frederiksberg C, Denmark

    Daniel Caballero

  3. Departamento de Mejora Genética Animal, Instituto Nacional de Investigación Y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain

    Juan García-Casco

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  1. Daniel Caballero
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Correspondence to Daniel Caballero.

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Caballero, D., Asensio, M., Fernández, C. et al. Chemical-instrumental-sensory traits and data mining for classifying dry-cured Iberian shoulders from pigs with different diets. Food Measure 13, 2935–2950 (2019). https://doi.org/10.1007/s11694-019-00214-4

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