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PREDICTION THE SOLUBILITY AND SWELLING INDEX OF STARCH FILMS APPLYING MACHINE LEARNING METHODS
Abstract
The utilization of renewable materials has raised worldwide interest regarding biodegradable polymers, proving important scientific, ecological, and economic relevance. Biopolymers are versatile materials used in various applications due to their high strength, low weight, and minimal environmental impact. However, their characterization for understanding their properties requires extensive experimental measurement. In this context, conventional research is improved by the integration of artificial intelligence (AI) and machine learning (ML) for predicting material properties. This research studies the optimization of starch extraction from potato waste and the influence of plasticizers and fillers on some physical properties, such as swelling index, water solubility and thermal stability. Starch films contain glycerol, sorbitol and biochar in varying percents were characterized by Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis and tensile strength. The results indicated that films with 20% glycerol content showed enhanced flexibility and solubility, whereas films with 30% sorbitol showed superior thermal stability, exhibiting a degradation temperature by 12 degrees, compared with standard sample which doesn-t have additives. It has been observed that a higher percentage of biochar about 15 % reduces the solubility of polymer films, a very important characteristic when starch polymer films are used as food packaging. The experimental data obtained was used to predict the solubility and swelling index of starch films applying three machine learning methods.
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