Food and Bioprocess Technology

, Volume 7, Issue 11, pp 3217–3225 | Cite as

Application of Neural Networks and Meta-Learners to Recognize Beef from OTM Cattle by Using Volatile Organic Compounds

  • Tomás Arredondo
  • Erwin Oñate
  • Rocío Santander
  • Gerda Tomic
  • José R. Silva
  • Elizabeth Sánchez
  • Cristian A. Acevedo
Original Paper


Cattle with age under 30 months (UTM) are usually slaughtered for human consumption to be not associated of the disease called mad cow. Though the age of the animal can generally be estimated by dentition, this method cannot be applied to a piece of meat from which the teeth have been removed. Since volatile organic compounds have been used to analyze food samples, this technique was used to recognize meat obtained from cattle aged over 30 months (OTM).

The monitoring of the volatile organic compounds (VOCs) released by UTM and OTM beef were done by using gas chromatography. A dataset with more than 500 chromatograms (each one with 17 VOCs fully identified) from fresh meat, refrigerated meat and vacuum-packaged meat was used to develop a classifier by using neural networks. Neural networks were trained with backpropagation, and then further optimized by using meta-learners.

Optimal configuration of the neural networks allowed discriminating between beef obtained from OTM or UTM cattle with accuracy close to 90 %. Results contrast favorably with more traditional statistical methods such as linear discriminant analysis (LDA), soft independent modeling of class analogies (SIMCA), partial least square discriminate analysis (PLS-DA), and support vector machines (SVM).

In conclusion, VOCs can be used as a fingerprint to recognize OTM beef from a pool of meat obtained indistinctly from fresh meat, refrigerated, or vacuum-packaged meat.


Beef Meta learner Neural network Over 30 months Volatile organic compounds 



The authors wish to thank DGIP -UTFSM by Grant 23.13.56 and CONICYT from Chile by FONDEF Grant D08i1102.


  1. Acevedo, C. A., Sánchez, E., Reyes, J., & Young, M. E. (2010). Volatile profiles of human skin cell cultures in different degrees of senescence. Journal of Chromatography B, 878, 449–455.CrossRefGoogle Scholar
  2. Aran, O., Yildiz, O. T., & Alpaydin, E. (2009). An incremental framework based on cross-validation for estimating the architecture of a multilayer perceptron. International Journal of Pattern Recognition and Artificial Intelligence, 23, 59–190.CrossRefGoogle Scholar
  3. Arredondo, T., & Ormazabal, W. (2013). Meta-learning framework applied in bioinformatics inference system design. International Journal of Data Mining and Bioinformatics, (In Press).Google Scholar
  4. Adkin, A., Webster, V., Arnold, M., Wells, G., & Matthews, D. (2010). Estimating the impact on the food chain of changing bovine spongiform encephalopathy (BSE) control measures: the BSE control model. Preventive Veterinary Medicine, 93, 170–182.CrossRefGoogle Scholar
  5. Boser, B.E., Guyon, I.M., & Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning theory (Pittsburgh, PA, United States, July 27–29,1992). COLT’92. New York, NY: ACMGoogle Scholar
  6. Brereton, R. (2003). Chemometrics data analysis for the laboratory and chemical plant. Chichester: Wiley.Google Scholar
  7. Dehghani, A., Mohammadi, Z., Maghsoudlou, Y., & Mahoonak, A. (2012). Intelligent estimation of the canola oil stability using artificial neural networks. Food and Bioprocess Technology, 5, 533–540.CrossRefGoogle Scholar
  8. Elmore, J. S., Warren, H. E., Mottram, D. S., Scollan, N. D., Enser, M., Richardson, R. I., & Wood, J. D. (2004). A comparison of the aroma volatiles and fatty acid compositions of grilled beef muscle from Aberdeen Angus and Holstein–Friesian steers fed diets based on silage or concentrates. Meat Science, 68, 27–33.CrossRefGoogle Scholar
  9. European Commission (2009). Commission Regulation (EC) No 620/ 2009 of 13 July 2009 providing for the administration of an import tariff quota for high-quality beef.Google Scholar
  10. Eriksson, L., Johansson, E., Kettaneh-Wold, N., Trygg, J., Wikstrom, C., & Wold, S. (2006). Multi and megavariate data analysis: Part I. Basic principles and applications (3rd ed.). Umea: Umetrics Academy.Google Scholar
  11. Fatemi, M., & Baher, E. (2005). Prediction of retention factors in supercritical fluid chromatography using artificial neural network. Journal of Analytical Chemistry, 60, 860–865.CrossRefGoogle Scholar
  12. Fazaeli, M., Emam-Djomeh, Z., Omid, M., & Kalbasi-Ashtari, A. (2011). Prediction of the physicochemical properties of spray-dried black mulberry. Juice using artificial neural networks. Food and Bioprocess Technology, 6, 585–590.CrossRefGoogle Scholar
  13. Haykin, S. (1998). Neural networks: a comprehensive foundation (2nd ed.). Upper Saddle River: Prentice Hall.Google Scholar
  14. Ironside, J. (2010). Variant Creutzfeldt–Jakob disease. Haemophilia, 16, 175–180.CrossRefGoogle Scholar
  15. Klaypradit, W., Kerdpiboon, S., & Singh, R. (2011). Application of artificial neural networks to predict the oxidation of menhaden fish oil obtained from Fourier transform infrared spectroscopy method. Food and Bioprocess Technology, 4, 475–480.CrossRefGoogle Scholar
  16. Kumar, S., & Mittal, G. (2010). Rapid detection of microorganisms using image processing parameters and neural network. Food and Bioprocess Technology, 3, 741–751.CrossRefGoogle Scholar
  17. Leiva, M., Arredondo, T.V., Candel, D., Dombrovskaia, L., Agulló, L., Seeger, M., & Vásquez, F. (2009). Feed-forward artificial neural network based inference system applied in bioinformatics data-mining. In: International Joint Conference on Neural Networks, Atlanta, pp. 1744–1749.Google Scholar
  18. Mitchell, T. (1997). Machine learning (1st ed.). New York: McGraw-Hill.Google Scholar
  19. Mohebbi, M., Fathi, M., & Shahidi, F. (2011). Genetic algorithm–artificial neural network modeling of moisture and oil content of pretreated fried mushroom. Food and Bioprocess Technology, 4, 603–609.CrossRefGoogle Scholar
  20. Neelakanta, P. S. (1999). Information theoretic aspects of neural networks. Florida: CRC Press.Google Scholar
  21. Rajendran, A., & Thangavelu, V. (2012). Optimization and modeling of process parameters for lipase production by Bacillus brevis. Food and Bioprocess Technology, 5, 310–322.CrossRefGoogle Scholar
  22. Santander, R., Creixell, W., Sánchez, E., Tomic, G., Silva, J., & Acevedo, C. (2013). Recognizing age at slaughter of cattle from beef samples using GC/MS-SPME chromatographic method. Food and Bioprocess Technology, 6, 3345–3352.CrossRefGoogle Scholar
  23. Simon, H. (1983). Why should machines learn? In R. Michalski, J. Carbonell, & T. Mitchell (Eds.), Machine learning: an artificial intelligence approach (pp. 25–37). Palo Alto: Morgan Kaufmann.Google Scholar
  24. Toker, O. S., & Dogan, M. (2013). Effect of temperature and starch concentration on the creep/recovery behaviour of the grape molasses: modelling with ANN, ANFIS and response surface methodology. European Food Research and Technology, 236, 1049–1061.CrossRefGoogle Scholar
  25. Toker, O. S., Yilmaz, M. T., Karaman, S., Dogan, M., & Kayacier, A. (2012). Adaptive neuro-fuzzy inference system and artificial neural network estimation of apparent viscosity of ice-cream mixes stabilized with different concentrations of xanthan gum. Applied Rheology, 22, 63918.Google Scholar
  26. Vasta, V., Ratel, J., & Engel, E. (2007). Mass spectrometry analysis of volatile compounds in raw meat for the authentication of the feeding background of farm animals. Journal of Agricultural and Food Chemistry, 55, 4630–4639.CrossRefGoogle Scholar
  27. Vivalta, R., & Drissi, Y. (2002). A perspective view and survey of meta-learning. Artificial Intelligence Review, 18, 77–95.CrossRefGoogle Scholar
  28. Yalcin, H., Toker, O. S., Ozturk, I., Dogan, M., & Kisi, O. (2012). Prediction of fatty acid composition of vegetable oils based on rheological measurements using nonlinear models. European Journal of Lipid Science and Technology, 114, 1217–1224.CrossRefGoogle Scholar
  29. Watanabe, A., Ueda, Y., Higuchi, M., & Shiba, N. (2008). Analysis of volatile compounds in beef fat by dynamic-headspace solid-phase microextraction combined with gas chromatography–mass spectrometry. Journal of Food Science, 73, C420–C425.CrossRefGoogle Scholar
  30. Werbos, P. (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences. PhD dissertation, Harvard University.Google Scholar
  31. Xie, Y., Baeza-Baeza, J., Torres-Lapasió, J., García-Alvarez-Coque, M., & Ramis-Ramos, G. (1995). Modelling and prediction of retention in high-performance liquid chromatography by using neural networks. Chromatographia, 41, 435–444.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Tomás Arredondo
    • 1
  • Erwin Oñate
    • 1
  • Rocío Santander
    • 2
  • Gerda Tomic
    • 2
  • José R. Silva
    • 2
  • Elizabeth Sánchez
    • 3
  • Cristian A. Acevedo
    • 3
  1. 1.Departamento de ElectrónicaUniversidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.Departamento de Ciencia y Tecnología de los Alimentos, Facultad TecnológicaUniversidad de Santiago de ChileSantiagoChile
  3. 3.Centro de BiotecnologíaUniversidad Técnica Federico Santa MaríaValparaísoChile

Personalised recommendations