AUTHOR(S) / АУТОР(И): Marija Banožić 
, Sara Ćuk , Mladen Zovko , Nikolina Kajić
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DOI: https://doi.org/10.46793/SBT26.913B
ABSTRACT / САЖЕТАК:
This study analyzed optimization of ultrasound-assisted nicotine extraction from tobacco leaf and byproducts (scrap, dust, rib) using Artificial Neural Networks (ANN). Experimental nicotine yields ranged from 0.024% to 0.798%. A feedforward ANN identified extraction time as the most critical parameter (importance ~0.41), followed by temperature. Although data variability resulted in low R2 values (probably due small number of experiments), low RMSE scores confirmed absolute prediction proximity. Optimal conditions were determined at 70 °C, 45 min, and 20 ml/g. While ANN effectively visualized non-linear trends via response surfaces, Response Surface Methodology (RSM) is suggested for future robust statistical modeling.
KEYWORDS / КЉУЧНЕ РЕЧИ:
artificial neural network, extraction, nicotine
ACKNOWLEDGEMENT / ПРОЈЕКАТ:
The research presented in this article is part of a project entitled “Use of Artificial Intelligence and Predictive Modeling for the Extraction of Bioactive Compounds from Plant-Based Byproducts” financially supported by federal ministry of science and Education of Bosnia and Herzegovina.
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