AUTHOR(S) / АУТОР(И): Biljana Lončar 
, Vladimir Filipović , Milica Nićetin , Miloš Radosavljević , Ivica Đalović , Milenko Košutić , Jelena Filipović
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DOI: https://doi.org/10.46793/SBT26.695L
ABSTRACT / САЖЕТАК:
This study used artificial neural network (ANN) modeling to predict and optimize the physical and technological properties of quinoa-enriched extruded products. A multilayer perceptron model was developed to estimate bulk density (BD), expansion index (EI), hardness (Har), number of fractures (NF), and crispiness work (Crw) as functions of screw speed (350–650 rpm) and quinoa addition (0–30%). The model demonstrated high predictive accuracy and successfully passed goodness-of-fit tests. Global sensitivity analysis revealed that screw speed was the dominant factor, while higher quinoa levels and screw speeds promoted expansion and reduced density and hardness. The ANN approach proved effective for modeling and optimization of extrusion conditions.
KEYWORDS / КЉУЧНЕ РЕЧИ:
artificial neural network, extrusion, quinoa, texture
ACKNOWLEDGEMENT / ПРОЈЕКАТ:
The research presented in this article is the part of projects supported by the Provincial Secretariat of Higher Education and Scientific Research, Autonomous Province of Vojvodina, Republic of Serbia, contract number: 003794135 2025 09418 003 000 000 001/2.
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