We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Inter-instrument definition of valid criteria for the automatic identification of microplastics by micro-Raman spectroscopy
AI summary Read the abstract
Researchers developed a standardized methodology for automatically identifying microplastics using micro-Raman spectroscopy across different laboratory instruments. They determined optimal match algorithms and threshold values that achieved a 95% true positive rate with minimal false positives, even when spectra were collected on different spectrometers. The study addresses a key barrier to reliable, reproducible microplastic identification in environmental and health research.
The assessment of the impact of microplastic contamination on the environment and human health requires a reliable identification of the polymer type of these particles. μ-Raman spectroscopy is a popular technique for identifying microplastics by comparing the reference spectra with those of the particles analysed. Automatic identification of microplastics requires defining an algorithm for the match between these spectra and setting a minimum match above which identification is performed with adequately high true and low false results rates. Ideally, the algorithm and match threshold should apply to different spectrometers and spectra collection parameters. This research presents a methodology to determine the best match algorithm for polymer identification using μ-Raman spectroscopy data collected on different instruments and laboratories, associated with a true positive rate (TP) of 95 % and a false positive rate (FP) lower than 5 %. Determining the match threshold (P5»P) by the bootstrap method does not require assumptions regarding match distribution. The normal distribution of the match between the reference and a particle's spectra from a different material allows FP determination. Identifying PET microparticles was optimal using Pearson's correlation coefficient (P5»P = 0.6244, TP = 95 %, FP = 4.9 × 10 %). Identification quality was tested based on three unweighted and three weighted correlation coefficients. Spectra with signal-to-noise ratios lower than 10 were forwarded to manual identifications. The MS Excel files used in the research are available as supporting information. The developed methodology for setting up identification criteria of microplastics by spectroscopy proved to be adequate for μ-Raman assessments and robust to different spectrometers and spectra collection conditions.
More Papers Like This
Inter-equipment reliable identification of microplastics by micro-FTIR using low computational resources
AI summary Read the abstract
Researchers developed a low-computational-resource method for reliable microplastic identification by micro-FTIR spectroscopy that performs consistently across different instruments. The approach, funded under the EU PlasticTrace metrology project, addresses inter-equipment variability — a major barrier to harmonized microplastic monitoring — enabling more comparable results across laboratories.
Inter-equipment reliable identification of microplastics by micro-FTIR using low computational resources
AI summary Read the abstract
Researchers developed a low-computational-resource method for reliable microplastic identification by micro-FTIR spectroscopy that performs consistently across different instruments. The approach, funded under the EU PlasticTrace metrology project, addresses inter-equipment variability — a major barrier to harmonized microplastic monitoring — enabling more comparable results across laboratories.
Analytical methods for identifying microplastics in environment: a critical review
AI summary Read the abstract
This review systematically covers the full analytical pipeline for identifying environmental microplastics — from sample collection and density separation through digestion, filtration, and spectroscopic identification — highlighting that Raman spectroscopy outperforms FTIR for particles under 10 µm and that method standardization remains the central challenge for cross-study comparability.
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.
A comparison of spectroscopic analysis methods for microplastics: Manual, semi-automated, and automated Fourier transform infrared and Raman techniques
AI summary Read the abstract
Researchers compared manual, semi-automated, and fully automated methods for identifying microplastics using FTIR and Raman spectroscopy. They found that the semi-automated approach was the best balance of accuracy and efficiency, detecting 22% more microplastic particles than manual analysis while taking less time. The fully automated method was fastest but had an 80% false positive rate, while Raman microscopy was better for very small particles but took nine times longer.
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.