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

DETOXIFICATION OF LINSEED-SUNFLOWER MEAL CO-EXTRUDATE – PROCESS PREDICTION

DOI: UDK:
633.521+581.48:636.087.2]:677.021.125:547.918
JOURNAL No:
Volume 45, Issue2
PAGES
193-202
KEYWORDS
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

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.



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