Radial Basis Artificial Neural Network Models for Predicting Solubility Index of Roller Dried Goat Whole Milk Powder

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)

Abstract

In this work, Radial Basis (Exact Fit) and Radial Basis (Fewer Neurons) artificial neural network (ANN) models were developed to evaluate its capability in predicting the solubility index of roller dried goat whole milk powder. The ANN models were trained with a data file composed of variables: loose bulk density, packed bulk density, wettability and dispersibility, while solubility index was the output variable. The modeling results showed that there is an agreement between the experimental data and the predicted values, with coefficient of determination and Nash-Sutcliffe coefficient close to 1. Therefore, this method may be effective for rapid estimation of solubility index of roller dried goat whole milk powder.

Keywords

Radial basis function ANN Solubility index Goat milk powder MATLAB 

References

  1. 1.
    Doganis, P., Alexandridis, A., Patrinos, P., Sarimveis, H.: Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. J. Food Engg., 75, 196–204 (2006)Google Scholar
  2. 2.
    Ribeiro, A.C., Ribeiro, S.D.A.: Specialty products made from goat milk. Small Ruminant Res. 89, 225–233 (2010)CrossRefGoogle Scholar
  3. 3.
    Gori, A., Chiara, C., Selenia, M., Nocetti, M., Fabbri, A., Caboni, M.F., Losi, G.: Prediction of seasonal variation of butters by computing the fatty acids composition with artificial neural networks. Euro. J. Lip. Sci. Tech. 113(11), 1412–1419 (2011)CrossRefGoogle Scholar
  4. 4.
    Ni, H., Gunasekaran, S.: Food quality predication with neural networks. Food Tech. 52(10), 60–65 (1998)Google Scholar
  5. 5.
    Jimenez-Marquez, S.A., Thibault, J., Lacroix. C.: Prediction of moisture in cheese of commercial production using neurocomputing models. Int. Dairy J. 15, 1156–1174 (2005)Google Scholar
  6. 6.
    Goyal, S., Goyal, G.K.: Radial basis (exact fit) and linear layer (Design) ANN models for shelf life prediction of processed cheese. Int. J. u- e- Service Sci. Tech., 5(1), 63–69 (2012)Google Scholar
  7. 7.
    Goyal, S., Goyal, G.K.: Supervised machine learning feedforward backpropagation models for predicting shelf life of processed cheese. J. Engg., 1(2), 25–28 (2012)Google Scholar
  8. 8.
    Goyal, S., Goyal, G.K.: Analyzing shelf life of processed cheese by soft computing. Sci. J. of Ani. Sci., 1(3), 119–125 (2012)Google Scholar
  9. 9.
    Sanzogni, L., Kerr, D.: Milk production estimates using feed forward artificial neural networks. Comp. Electro. Agri. 32(1), 21–30 (2001)CrossRefGoogle Scholar
  10. 10.
    Goyal, S., Goyal, G.K.: Radial basis (exact fit) artificial neural network technique for estimating shelf life of burfi. Adv. Comp. Sci. App., 1(2), 93–96 (2012)Google Scholar
  11. 11.
    Guyer, D., Yang, X.: Use of genetic artificial neural networks and spectral imaging for defect detection on cherries. Comp. Electro. Agri. 29(3), 179–194 (2000)CrossRefGoogle Scholar
  12. 12.
    Goyal S., Goyal, G.K.: Central nervous system based computing models for shelf life prediction of soft mouth melting milk cakes. Int. J. Info. Tech. Comp. Sci., 4(4), 33–39 (2012)Google Scholar
  13. 13.
    Raharitsifa, N., Ratti, C.: Foam-mat freeze-drying of apple juice part 1: experimental data and ANN simulations. J. Food Process Engg. 33, 268–283 (2010)CrossRefGoogle Scholar
  14. 14.
    Qiao, J., Wang, N., Ngadi, M.O., Kazemi, S.: Predicting mechanical properties of fried chicken nuggets using image processing and neural network techniques. J. Food Engg. 79(3), 1065–1070 (2007)CrossRefGoogle Scholar
  15. 15.
    Omid, M., Akram, A., Golmohammadi, A.: Modeling thermal conductivity of Iranian flat bread using artificial neural networks. Int. J. Food Prop. 14(4), 708–720 (2011)CrossRefGoogle Scholar
  16. 16.
    Serpen, A., Gökmen, V.: Modeling of acrylamide formation and browning ratio in potato chips by artificial neural network. Mol. Nut. Food Res. 51(4), 383–389 (2007)CrossRefGoogle Scholar
  17. 17.
    Omid, M., Baharlooei, A., Ahmadi, H.: Modeling drying kinetics of pistachio nuts with multilayer feed-forward neural network. Drying Tech. Int. J. 27(10), 1069–1077 (2009)CrossRefGoogle Scholar
  18. 18.
    Mateo. F., Gadea. R., Medina. Á., Mateo. R., Jiménez, M.: Predictive assessment of ochratoxin a accumulation in grape juice based-medium by aspergillus carbonarius using neural networks. J. App. Microbio., 107(3), 915–927 (2009)Google Scholar
  19. 19.
    Loukas, Y.L.: Radial basis function networks in host-guest interactions: instant and accurate formation constant calculations. Anal. Chimica Acta 417(2), 221–229 (2000)CrossRefGoogle Scholar
  20. 20.
    Sutrisno., Edris, I.M., Sugiyono, P.: Quality prediction of mangosteen during storage using artificial neural network. In: International Agricultural Engineering Conference, Bangkok, Thailand, 7–10 Dec 2009Google Scholar
  21. 21.
    Fernandez, C., Soria, E., Martin, J.D., Serrano, A.J.: Neural networks for animal science applications: two case studies. Exp. Sys. Applic. 31, 444–450 (2006)CrossRefGoogle Scholar
  22. 22.
    Cravener, T., Roush, W.: Improving neural network prediction of amino acid ledients. Poult. Sci. 78, 983–991 (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.National Dairy Research InstituteKarnal India

Personalised recommendations