We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Qualitative discrimination and quantitative prediction of microplastics in ash based on near-infrared spectroscopy
Summary
Researchers developed a method using portable near-infrared spectroscopy to quickly detect and measure microplastics in waste incineration ash. The study found that this approach can both identify the type of plastic present and predict its concentration, offering a faster alternative to traditional laboratory testing methods for monitoring microplastic contamination in ash residues.
Microplastics are recognized as a new environmental pollutant. Researchers have detected their presence in waste incineration ash. However, traditional testing methods take a very long testing period. There is a lack of research on detecting microplastics in waste incineration ash. In this paper, a portable near-infrared spectra (NIRS) spectrometer was used for qualitative discrimination and quantitative prediction of microplastics in ash. A total of 84 sets of simulated ash samples containing different types (PP, PS, PE, and PVC) and contents (2.4 wt% - 20 wt%) of microplastics were used in the model. The results show the qualitative discrimination model using support vector machines (SVM) method with multiplicative scatter correction (MSC) preprocessing could effectively identify the microplastic types in the ash with 100% detection accuracy. Furthermore, the partial least squares regression (PLSR) model was effective in quantitatively predicting the content of microplastics in ash. The Rp of the PP, PS, PE, and PVC models are 0.95, 0.93, 0.89, and 0.95, respectively. The RPD of the PP, PS, PE, and PVC models are 3.97, 3.96, 2.89 and 5.02, respectively. This study shows that microplastics in ash can be detected rapidly and accurately using portable near-infrared spectrometers.
Sign in to start a discussion.
More Papers Like This
Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning
Researchers used a portable near-infrared spectrometer combined with machine learning to rapidly identify and classify seven types of consumer plastic waste on-site without damaging the samples. Faster and cheaper plastic identification tools are important for improving plastic recycling efficiency and ultimately reducing the amount of plastic that ends up as microplastic pollution.
Influx of Near-Infrared Technology in Microplastic Community: A Bibliometric Analysis
This bibliometric analysis mapped the growing use of near-infrared spectroscopy for microplastic identification, finding it is emerging as a practical, portable alternative to FTIR spectroscopy for field-based polymer analysis.
Comparative Study of Chemometric Approaches and Machine Learning for Miniaturized Near-infrared (micronir) Spectroscopy in Plasticwaste Sorting
This study tested a miniaturized near-infrared (NIR) spectroscopy device combined with chemometric and machine learning methods to sort different types of plastic waste. The approach accurately identified polymer types, supporting more efficient plastic recycling operations that could reduce microplastic generation.
Is incineration the terminator of plastics and microplastics?
Researchers extracted microplastics from the bottom ash of 17 different types of incinerators and identified them by micro-FTIR spectroscopy, demonstrating that incineration does not fully destroy plastic waste and that unburned microplastics persist in ash residues.
Polymer Type Identification of Marine Plastic Litter Using a Miniature Near-Infrared Spectrometer (MicroNIR)
Researchers tested a miniature near-infrared spectrometer (MicroNIR) for rapidly identifying polymer types in marine plastic litter collected from beaches, finding it could accurately distinguish common plastics like polyethylene and polypropylene. Low-cost, portable identification tools are important for large-scale monitoring of marine plastic pollution.