Identification of Aged Polypropylene with Machine Learning and Near–Infrared Spectroscopy for Improved Recycling

Polymers, Journal Year: 2025, Volume and Issue: 17(5), P. 700 - 700

Published: March 6, 2025

The traditional plastic sorting process primarily relies on manual operations, which are inefficient, pose safety risks, and result in suboptimal separation efficiency for mixed waste plastics. Near-infrared (NIR) spectroscopy, with its rapid non-destructive analytical capabilities, presents a promising alternative. However, the analysis of NIR spectra is often complicated by overlapping peaks complex data patterns, limiting direct applicability. This study establishes comprehensive machine learning-based spectroscopy model to distinguish polypropylene (PP) at different aging stages. A dataset was collected from PP samples subjected seven simulated stages, followed construction classification analyze these spectral variations. confirmed using Fourier-transform infrared (FTIR). Mechanical property analysis, including tensile strength elongation break, revealed gradual decline prolonged aging. After 40 days accelerated aging, break dropped approximately 30%, retaining only about one-sixth original mechanical performance. Furthermore, various preprocessing methods were evaluated identify most effective technique. combination second derivative method linear -SVC achieved accuracy 99% precision 100%. demonstrates feasibility accurate identification thereby enhancing quality recycled plastics promoting automated, precise, sustainable recycling processes.

Language: Английский

Mechanisms of exopolysaccharide synthesis by Streptococcus thermophilus IMAU20246 determined by the carbon source

Food Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 144069 - 144069

Published: March 1, 2025

Language: Английский