Article metrics

  • citations in SCindeks: 0
  • citations in CrossRef:0
  • citations in Google Scholar:[=>]
  • visits in previous 30 days:15
  • full-text downloads in 30 days:10
article: 3 from 34  
Back back to result list
Food and Feed Research
2018, vol. 45, iss. 2, pp. 193-201
article language: English
document type: Original Paper
doi:10.5937/FFR1802193C
Creative Commons License 4.0
Detoxification of linseed-sunflower meal co-extrudate: Process prediction
aInstitute for Food Technology, Novi Sad
bInstitute of General and Physical Chemistry
cAgricultural University of Athens, Athens, Greece

e-mail: dusica.colovic@fins.uns.ac.rs

Project

Investigation of contemporary biotechnological processes in animal feed production aimed at increasing food competitiveness, quality and safety (MESTD - 46012)
COST CA 15118 project

Abstract

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.

Keywords

References

Altarazi, S., Ammouri, M., Hijazi, A. (2018) Artificial neural network modeling to evaluate polyvinylchloride composites' properties. Computational Materials Science, 153: 1-9
AOAC International (2000) Official methods of analysis of AOAC International. Arlington, VA, USA, 17th Ed. Official Method 915.03, part B
Basheer, I., Hajmeer, M. (2000) Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1): 3-31
Cubeddu, A., Rauh, C., Delgado, A. (2014) Hybrid artificial neural network for prediction and control of process variables in food extrusion. Innovative Food Science & Emerging Technologies, 21: 142-150
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, 1761-1772; 18
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
Ćurčić, B.L., Pezo, L.L., Filipović, V.S., Nićetin, M.R., Knežević, V. (2014) Osmotic Treatment of Fish in Two Different Solutions-Artificial Neural Network Model. Journal of Food Processing and Preservation, 39(6): 671-680
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, 44(13): 15918-15923
EFSA (2007) Opinion of the Scientific Panel on contaminants in the food chain [CONTAM] related to cyanogenic compounds as undesirable substances in animal feed. EFSA Journal, 5(2): 434
Fan, F.H., Ma, Q., Ge, J., Peng, Q.Y., Riley, W.W., 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
Ferreira, S.L.C., Bruns, R.E., Ferreira, H.S., Matos, G.D., David, J.M., Brandão, G.C., da Silva, E.G.P., Portugal, L.A., dos Reis, P.S., Souza, A.S., dos Santos, W.N.L. (2007) Box-Behnken design: An alternative for the optimization of analytical methods. Analytica Chimica Acta, 597(2): 179-186
Hu, X., Weng, Q. (2009) Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113(10): 2089-2102
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, vol. 49, br. 1, str. 63-68
Kollo, T., von Rosen, D. (2005) Advanced Multivariate Statistics with Matrices. Dordrecht: Springer Nature
Kumar, A., Sharma, S. (2008) An evaluation of multipurpose oil seed crop for industrial uses (Jatropha curcas L.): A review. Industrial Crops and Products, 28(1): 1-10
Li, Y., Bridgwater, J. (2000) Prediction of extrusion pressure using an artificial neural network. Powder Technology, 108(1): 65-73
Montano, J.J., Palmer, A. (2003) Numeric sensitivity analysis applied to feedforward neural networks. Neural Computing & Applications, 12(2): 119-125
Montgomery, D.C. (1984) Design and analysis of experiments. New York: John Wiley & Sons, 2nd Ed
Pezo, L.L., Ćurčić, B.Lj., Filipović, V.S., Nićetin, M.R., Koprivica, G.B., Mišljenović, N.M., Lević, L.B. (2013) Artificial neural network model of pork meat cubes osmotic dehydration. Hemijska industrija, vol. 67, br. 3, str. 465-475
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. Computer Physics Communications, 181(2): 259-270
Shankar, T.J., Bandyopadhyay, S. (2007) Prediction of Extrudate Properties Using Artificial Neural Networks. Food and Bioproducts Processing, 85(1): 29-33
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
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
Taylor, B.J. (2006) Methods and Procedures for the Verification and Validation of Artificial Neural Networks. New York: Springer Science and Business Media
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) Nota: Aplicación del sistema de simulación de redes neurales para el control de la deshidratación osmótica. Food Science and Technology International, 3(6): 459-465
Turanyi, T., Tomlin, A.S. (2014) Analysis of Kinetics Reaction Mechanisms. Berlin: Springer
Wu, M., Li, D., Wang, L., Zhou, Y., Brooks, M., Chen, X., Mao, Z. (2008) Extrusion detoxification technique on flaxseed by uniform design optimization. Separation and Purification Technology, 61(1): 51-59