2University of Belgrade,Institute of General and Physical Chemistry, 11000 Belgrade, Studentski trg 12-16, Serbia
3Agricultural University of Athens, Iera Odos 75, Athens 118 55, Greece "/>

Food & Feed Research


Volume 45, Issue2
extrusion, antinutritive components, cyanogenic glycosides, artificial neural network
TOOLS Creative Commons License
Dušica S. Čolović1*, Lato L. Pezo2, Radmilo R. Čolović1, Vojislav V. Banjac1, Olivera M. Đuragić1, Nickolas G. Kavallieratos3, Nedeljka J. Spasevski1
1University of Novi Sad, Institute of Food Technology, 21000 Novi Sad, Bulevar cara Lazara 1, Serbia
2University of Belgrade,Institute of General and Physical Chemistry, 11000 Belgrade, Studentski trg 12-16, Serbia
3Agricultural University of Athens, Iera Odos 75, Athens 118 55, Greece


For many years, linseed has been attracted a great attention in animal nutrition because of its exceptionally favourable fatty acid composition and high content of essential α-linolenic acid. However, the presence of antinutritive components, cyanogenic glycosides, limits its inclusion in the animal’s diet. Several ways of linseed detoxification were observed in literature, emphasizing extrusion as one of the most effective processes. In the presented study, the application of Artificial Neural Network (ANN) has been observed, as a tool for prediction of process influence on the deterioration of cyanogenic glycosides during the extrusion process of linseed-sunflower meal co-extrudate. The content of hydrogen cyanide (HCN) was determined according to the AOAC method as an indicator of cyanogenic glycosides in the produced co-extrudate. Extrusion of the material was performed on a laboratory single screw extruder. The performance of ANN model was compared with experimental data in order to develop rapid and accurate method for prediction of HCN content in co-extrudate. According to the experimental results, the highest HCN content (126 mg/kg) was determined at the lowest moisture content (7%) and the lowest screw speed (240 rpm). With the increase of moisture content and temperature during extrusion, the content of HCN drastically decreased. The ANN model showed high prediction accuracy (r2> 0.999), which indicates that the model could be easily and reliably applied in practice.

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  1. Altarazi, S., Ammouri, M., Hijazi, A. (2018). Artificial neural network modeling to evaluate polyvinylchloride composites’ properties. Computational Materials Science, 153, 1-9.
  2. AOAC (2000). Official Methods of Analysis of AOAC International, 17th Ed., AOAC International, Arlington,VA, USA, Official Method 915.03, part B.
  3. Basheer, I.A., Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43 (1), 3-31.
  4. Cubeddu, A., Rauh, C., Delgado, A. (2014). Hybrid artificial neural network for prediction and control of process variables in food extrusion. Innovative Food Science and Emerging Technologies, 21, 142-150.
  5. Ćurčić, B.Lj., Pezo, L.L., Filipović, V.S., Nićetin, M.R., Knežević, V. (2015).Osmotic Treatment of Fish in Two Different Solutions‐Artificial Neural Network Model. Journal of Food Processing and Preservation, 39 (6), 671-680.
  6. Čolović, D., Čolović, R., Lević, J., Ikonić, B., Vukmirović, Đ., Lević, Lj. (2016). Linseed-sunflower meal co-extrudate as a functional additive for animal feed – extrusion optimization. Journal of Agricultural Science and Technology, 18, 1761-1772.
  7. Čolović, D., Lević, J., Čabarkapa, I., Čolović, R., Lević, Lj.,Sedej, I. (2015). Stability of an extruded, linseed-based functional feed additive with the supplementation of Vitamin E and carvacrol. Journal of Animal and Feed Sciences, 24 (4), 348-357.
  8. Deng, L., Feng, B., Zhang, Y. (2018). An optimization method for multi-objective and multi-factor designing of a ceramic slurry: Combining orthogonal experimental design with artificial neural networks. Ceramics International, In press, corrected proof.
  9. EFSA (2006).Opinion of the scientific panel on contaminants in the food chain on a request from the commission related to cyanogenic compounds as undesirable substances in animal feed question. N° EFSA-Q-2003-064 (https://efsa.onlinelibrary.wiley.com/doi/epdf/10. 2903/j.efsa.2007.434).
  10. Fan, F.H., Ma, Q., Ge, J., Peng, Q. Y., Tang, S. Z. (2013). Prediction of texture characteristics from extrusion food surface images using a computer vision system and artificial neural networks. Journal of Food Engineering, 118 (4), 426-433.
  11. Ferreira, S.L.C., Bruns, R.E., Ferreira, H. S.,Matos, G.D., David, J.M., Brandão, G.C., daSilva, E.G.P., Portugal, L. A., dos Reis, P.S., Souza, A.S., dos Santos, W.N.L. (2007). BoxBehnken Design: An Alternative for the Optimization of Analytical Methods. Analytica Chimica Acta, 597 (2), 179-186.
  12. Hu, X., Weng, Q. (2009). Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multilayer perceptron neural networks. Remote Sensing of Environment, 113 (10), 2089-2102.
  13. Ivanov, D., Kokić, B., Brlek, T., Čolović, R., Vukmirović, Đ, Lević, J., Sredanović, S. (2012). Effect of microwave heating on content of cyanogenic glycosides in linseed. Ratarstvo i povrtarstvo, 49 (1), 63-68.
  14. Kollo, T., von Rosen, D. (2005). Advanced multivariate statistics with matrices, Springer, Dordrecht.
  15. Kumar, A., Sharma S. (2008). An evaluation of multipurpose oil seed crop for industrial uses (Jatrophacurcas L.): A review. Industrial Crops and Products, 28 (1), 1-10.
  16. Li, Y.Y., Bridgwater, J. (2000). Prediction of etrusion pressure using an artificial neural network. Powder Technology, 108 (1), 65-73.
  17. Montaño, J.J., Palmer, A. (2003). Numeric sensitivity analysis applied to feedforward neural networks. Neural Computing and Applications, 12, 119-125.
  18. Montgomery, D.C. (1984). Design and analysis of experiments, 2nd Ed., John Wiley and Sons, New York.
  19. Pezo, L.L., Ćurčić, B.Lj, Filipović, V.S., Nićetin, M.R., Koprivica, G.B., Mišljenović, N.M., Lević, Lj. B. (2013). Artificial neural network model of pork meat cubes osmotic dehydration. Hemijska Industrija, 67 (3), 465-475.
  20. Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., Tarantola, S. (2010). Variance based sensitivity analysis of model output. Design and Estimator for the Total Sensitivity Index, 181 (2), 259-270.
  21. Shankar, T.J., Bandyopadhyay, S. (2007). Prediction of extrudate properties using artificial neural networks. Food and Bioproducts Processing, 85 (1), 29-33.
  22. Sovány, T., Tislér, Z., Kristó, K., Kelemen, A., Regdon, G.(2016). Estimation of design space for an extrusion–spheronization process using response surface methodology and artificial neural network modelling. European Journal of Pharmaceutics and Biopharmaceutics, 106, 79- 87.
  23. Sun, Z., Zhang, K., Chen, C., Wu, Y., Tang, Y., Georgiev, M.I., Zhang, X., Lin, M., Zhou, M. (2018). Biosynthesis and regulation of cyanogenic glycoside production in forage plants. Applied Microbiology and Biotechnology, 102 (1), 9 - 16.
  24. Taylor, B.J. (2006). Methods and Procedures for the Verification and Validation of Artificial Neural Networks, Springer Science and Business Media, New York.
  25. Trelea, I.C., Raoult-Wack, A.L., Trystram, G. (1997). Note: Application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration). Food Science and Technology International 3 (6), 459-465.
  26. Turanyi, T., Tomlin, A.S. (2014). Analysis of Kinetics Reaction Mechanisms, Springer, Berlin Heidelberg.
  27. Wu, M., Li, D., Wang, L., Zhou, Y.G., Brooks, M.S.L., Chen, X.D., Mao, Z.H. (2008). Extrusion detoxification technique on linseed by uniform design optimization. Separation and Purification Technology, 61 (1), 51-59.