Detoksifikacija koekstrudata lanenog semena i suncokretove sačme - predviđanje procesa

Dušica Čolović,Olivera Đuragić,Nedeljka Spasevski,Lato Pezo,Radmilo Čolović,Vojislav Banjac,Nickolas Kavallieratos

doi:10.5937/ffr1802193c

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.

Highlights

Many antinutritional and toxic factors occur in conventional seeds cultivated for the purposes of feed production
The optimum number of hidden neurons was chosen upon minimizing the difference between predicted Artificial Neural Network (ANN) values and desired outputs, using Sum of Squares (SOS) during testing as a performance indicator
ANN-based model could be effectively used for predictive purposes, modelling and optimization, according to the results shown above

Summary

Introduction

Many antinutritional and toxic factors occur in conventional seeds cultivated for the purposes of feed production. The role of these factors in plants is to defend seeds against environmental vagaries and help to protect them (Kumar and Sharma, 2008). These factors, though good for the plant, cause deleterious effects or could be even toxic to animals. When it comes to linseed, cyanogenic glycosides (CGs) are the main limitation of linseed unrestricted usage in animal nutrition (Čolović et al, 2016). The reason for toxicity of CGs lies in the release of hydrogen

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