Bio-Hybrid Films from Chirich Tuber Starch: A Sustainable Approach with Machine Learning-Driven Optimization

被引:0
|
作者
Karaogul, Eyyup [1 ]
Sariisik, Gencay [2 ]
Ogutlu, Ahmet Sabri [2 ]
机构
[1] Harran Univ, Fac Engn, Dept Food Engn, TR-63300 Sanliurfa, Turkiye
[2] Harran Univ, Fac Engn, Dept Ind Engn, TR-63300 Sanliurfa, Turkiye
关键词
sustainability; chirich tuber; bio-hybrid films; machine learning; starch-based bioplastics; circular economy; CROSS-LINKING; DOLOMITE;
D O I
10.3390/su17051935
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study investigates the potential of Chirich (Asphodelus aestivus) tuber, one of Turkey's natural resources, for sustainable bio-hybrid film production. Bio-hybrid films developed from Chirich tuber starch in composite form with polyvinyl alcohol (PVOH) were thoroughly examined for their physical, mechanical, and barrier properties. During the production process, twin-screw extrusion and hydraulic hot pressing methods were employed; the films' optical, chemical, and barrier performances were analyzed through FT-IR spectroscopy, water vapor permeability, solubility, and mechanical tests. To evaluate the films' durability against environmental factors and model their properties, advanced computational model algorithms such as Gradient Boosting Regression (GBR), Random Forest Regression (RFR), and AdaBoost Regression (ABR) were utilized. The results showed that the GBR algorithm achieved the highest accuracy with 99.92% R2 and presented the most robust model in terms of sensitivity to environmental factors. The results indicate that Chirich tuber-based bio-hybrid films exhibit significantly enhanced mechanical strength and barrier performance compared to conventional corn starch-based biodegradable polymers. These superior properties make them particularly suitable for industrial applications such as food packaging and medical materials, where durability, moisture resistance, and gas barrier characteristics are critical. Moreover, their biodegradability and potential for integration into circular economy frameworks underscore their environmental sustainability, offering a viable alternative to petroleum-derived plastics. The incorporation of ML-driven optimization not only facilitates precise property prediction but also enhances the scalability of bio-hybrid film production. By introducing an innovative, data-driven approach to sustainable material design, this study contributes to the advancement of bio-based polymers in industrial applications, supporting global efforts to mitigate plastic waste and promote environmentally responsible manufacturing practices.
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页数:21
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