Comparative modeling of zinc oxide release from poly(ethylene-furanoate)/poly(3-hydroxybutyrate)-zinc oxide composite electrospun films using artificial neural network and response surface methodology for active packaging
Athira ARAMUGHAN, Anuj NIROULA, James KEGERE, Muhammad Z. IQBAL, Akmal NAZIR
Abstract. This study investigated zinc release from electrospun films composed of sustainable polymers, poly(ethylene furanoate) (PEF) and poly(3-hydroxybutyrate) (PHB) reinforced with ZnO for active packaging of model foods. ZnO imparts antibacterial functionality, while electrospinning ensures uniform film structure. A Box–Behnken design with 15 experimental runs was used to evaluate the effects of pH (4–9), temperature (4–40 °C), and exposure duration on Zn release, quantified via inductively coupled plasma–mass spectrometry (ICP–MS). Comparative modeling using artificial neural networks (ANN) and response surface methodology (RSM) revealed consistent trends: Zn release increased with temperature, peaked then declined with rising pH, and followed a U-shaped pattern over time. Both models showed high accuracy (R² > 0.99), but ANN outperformed RSM, yielding lower root mean square error (RMSE: 0.118 vs. 0.4193) and absolute average deviation (AAD: 0.0802 vs. 0.3492). The ANN model was further applied to predict Zn release in three food systems, tomato (pH 4.7), leafy greens (pH 7), and egg albumin (pH 8.2), under varying storage conditions. These predictions demonstrated the potential of PHB/PEF–ZnO films to deliver controlled Zn release tailored to food matrix and environment. Our findings support the use of these sustainable composites as active packaging materials, offering regulated antibacterial action and contributing to extended shelf life of different food produce. This work introduces a data-driven framework combining electrospun bio-based composites with machine-learning modeling to predict zinc release under realistic food storage conditions, highlighting ANN as a superior predictive tool compared to conventional RSM.
Keywords
Poly(ethylene Furanoate) (PEF), Poly(3-Hydroxybutyrate) (PHB), Electrospinning, Zinc, Artificial Neural Network (ANN), Response Surface Methodology
Published online 6/20/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Athira ARAMUGHAN, Anuj NIROULA, James KEGERE, Muhammad Z. IQBAL, Akmal NAZIR, Comparative modeling of zinc oxide release from poly(ethylene-furanoate)/poly(3-hydroxybutyrate)-zinc oxide composite electrospun films using artificial neural network and response surface methodology for active packaging, Materials Research Proceedings, Vol. 67, pp 609-616, 2026
DOI: https://doi.org/10.21741/9781644904176-81
The article was published as article 81 of the book Climate Action and Sustainability
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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