We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Effect of sample presentation on the identification of polypropylene (PP), polyethylene terephthalate (PET), and polystyrene (PS) using a portable near infrared instrument
AI summary Read the abstract
Researchers evaluated how sample presentation mode — ceramic, aluminium foil, or glass vial — affected the accuracy of portable near-infrared spectroscopy for classifying PP, PET, and polystyrene microplastics, finding classification accuracies ranging from 73-100% depending on the sampling mode used.
The quality of both the data and the sampling method plays an essential role in model development in near infrared (NIR) spectroscopy. Among the different factors, sampling presentation is key during the development of a NIR application, particularly when portable instruments are used to analyse microplastics (MP). The objective of this study was to evaluate the effect of sample presentation mode (e.g. ceramic, aluminium foil and glass vial) on the classification of different MPs, such as polypropylene (PP), polyethylene terephthalate (PET), and polystyrene (PS) using a portable NIR instrument. The results demonstrated that NIR spectroscopy could classify PP, PET, and PS microplastics, although classification accuracy varied depending on sample presentation. The PCA score plots showed differences between the sampling modes used to collect the spectra. Furthermore, the PLS models indicated that their performance depends on the sampling mode used where the coefficient of determination in cross-validation (R2cv) ranged between 0.79 and 0.90. The results also showed that NIR spectroscopy was able to classify PP, PET, and PS with accuracies ranging from 73 to 100 %, depending on the sample presentation mode utilised. The development and implementation of rapid analytical methods, such as NIR spectroscopy, provides a non-destructive and green tool to identify the type of MP analysed.
More Papers Like This
Polymer Type Identification of Marine Plastic Litter Using a Miniature Near-Infrared Spectrometer (MicroNIR)
AI summary Read the abstract
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.
Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning
AI summary Read the abstract
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.
Analysis of marine plastic fragments using a portable near-infrared instrument: The effect of scanning position on the classification of polymer type
AI summary Read the abstract
Scientists tested a handheld scanning device that can quickly identify what type of plastic washes up on beaches, without needing a lab. This matters because knowing the exact plastic type helps determine if it's safe to recycle or if it might break down into harmful microplastics that can end up in our water, food, and eventually our bodies—making cleanup efforts smarter and more efficient.
What determines accuracy of chemical identification when using microspectroscopy for the analysis of microplastics?
AI summary Read the abstract
An interlaboratory study analyzed metadata from microplastic identification using FTIR and Raman microspectroscopy to determine which factors most influenced correct polymer identification. Particle size, polymer type, and operator experience were among the key variables affecting accuracy of chemical identification in drinking water samples.
Comparison of Benchtop and Portable Near-Infrared Instruments to Predict the Type of Microplastic Added to High-Moisture Food Samples
AI summary Read the abstract
Researchers compared benchtop and portable near-infrared (NIR) spectroscopy instruments for classifying microplastics added to high-moisture food samples such as vegetables and fruit, evaluating whether portable instruments could match benchtop performance for rapid, non-destructive detection.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.