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Analysis of Waste Polymer Composition by a Simple Device for Raman Spectra Decomposition
Summary
Scientists built an affordable device that uses light (laser) scanning to identify what different fabric and plastic blends are actually made of, even when materials are mixed together in ways that are hard to tell apart. This matters because better tools for sorting and identifying waste plastics and textile fibers could improve recycling efforts and help track microplastic pollution, tiny plastic particles that have been found in our water, food, and even our bodies. While this study focuses on the technology itself rather than health effects, better detection methods are an important step toward understanding and reducing our exposure to microplastics in the environment.
Today, the search for resources related to non-fossil raw materials that require less carbon-based energy consumption, use less water, and produce recyclable waste is prevailing. In the future, will this effort be replaced by resources sufficient to be produced in environmentally friendly ways? Resources will be circulated in a controlled manner, and materials will be sustainable. Most of the energy sources will be sustainable, derived from natural resources (sun, wind, waves, geothermal sources, etc.). The world will be managed by data, enabling resource sufficiency. Sustainable development is therefore a long-term strategy including economic, human (social), and environmental (material) resources. This strategy requires development in the complex identification and quantification of waste of different origins, including plastics and fibers. The identification of polymer complex mixtures and fibrous-blend waste, including microplastics, is a fundamental tool for effective environmental monitoring and comprehensive recycling management. Raman spectroscopy, combined with spectral unmixing techniques, provides a powerful tool for resolving overlapping spectral components and characterizing the composition of fibrous polymeric materials. The general goal of hyperspectral unmixing is to decompose an observed spectral mixture matrix into a set of pure component spectra and their respective portions (contributions or abundance) or ideally concentrations. This requires solving an inverse problem under physical and mathematical constraints. Principal component analysis (PCA), based on singular value decomposition (SVD), followed by independent component analysis (ICA) rotation, is used to reduce the number of components to a meaningful set of endmembers. The extracted endmembers should be statistically independent, nonnegative, and sum to one, because they represent real chemical components in the mixture. These requirements are fulfilled by constrained quadratic programming using the Newton linearization method. The RAMIX program, based on these procedures, has already been described, and its source code is available in another article written in Python. It is designed for the analysis of experimental Raman spectra of polymeric mixtures and for mapping waste fibrous blends. This program is used here for Raman spectral analysis of compressed textile samples composed of different staple fiber types. To evaluate Raman spectra, a simple, cost-effective, custom-built measurement system was created. This system allows mapping of fibrous mixtures by Raman spectra across a line or an area.