We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Improved Microplastic Identification from Simultaneously Collected Photothermal Infrared and Raman Spectra Using Multiview Conformal Prediction
AI summary Read the abstract
Scientists have developed a more reliable way to identify tiny plastic particles (microplastics) by combining two different light-based detection methods and using statistics to double-check the results, rather than relying on just one test with an arbitrary cutoff. This matters because accurately spotting microplastics in air, water, and our environment is the first step to understanding how much of this potentially harmful material we're actually exposed to—and this new method proved better at correctly flagging real plastic particles while avoiding false alarms on non-plastic debris.
Microplastics (MPs) have been documented in urban and remote locations across the globe. Further identification and quantification of MPs are required to determine how widespread they are in the environment. One of the most popular chemometric methods for identifying microplastics (MPs) is database matching, in which an unknown spectrum is compared with reference library spectra by calculating likeness scores. Threshold minima determine if the score is high enough to consider the reference spectrum as a potential match, yet these thresholds are frequently set arbitrarily. There is a growing consensus that MP identification should involve multiple measurement techniques, but statistically robust methods to relate multiple database matching scores are lacking. Herein, multiview conformal prediction (MVCP) incorporates two views (i.e., photothermal infrared (PTIR) and Raman spectra) to calculate multidimensional thresholds for MP identification with statistical confidence. The chemical identities returned for an unknown particle have a statistical assurance that one of the identities is the correct match based on a user-defined uncertainty parameter. The average number of potential matches returned by MVCP was closer to one chemical identitythe ideal number of identities returnedwhen compared to single-view CP methods that used either the PTIR or Raman spectra. Moreover, MVCP was less affected than its single-view counterparts when one of the two spectra was difficult, or impossible, to identify. To show the utility of MVCP for real-world samples, an ambient particle sample with MPs deposited on it was used to demonstrate that an MVCP threshold at 73% theoretical confidence maximized the fraction of MP particles correctly identified as plastic (0.80 ± 0.07), while limiting the fraction of non-MP environmental particles misidentified as plastic (0.10 ± 0.06). This initial application of MVCP to spectroscopy demonstrates the benefits of utilizing multiple spectral methods with data analysis routines for identifying MPs with statistical confidence.
More Papers Like This
Dataset for the statistical confidence of microplastic particle identification via infrared and Raman spectra
AI summary Read the abstract
This dataset accompanies a study developing a statistically rigorous method (multiview conformal prediction) for identifying microplastic particles from two simultaneously collected spectra (infrared and Raman), improving the accuracy and efficiency of plastic identification in environmental samples. Better identification methods are essential for reliable monitoring of microplastic pollution across different environments and regulatory contexts.
Identification method of microplastics based on Raman-infrared spectroscopy fusion
AI summary Read the abstract
Researchers developed a method that combines Raman and infrared spectroscopy data to improve the identification of microplastics, compensating for the limitations of each technique when used alone. Machine learning was used to process the combined spectral data, improving accuracy for detecting plastics with pigments or additives that interfere with standard measurements.
Optical photothermal infrared spectroscopy with simultaneously acquired Raman spectroscopy for two-dimensional microplastic identification
AI summary Read the abstract
Researchers demonstrated that optical photothermal infrared spectroscopy combined with simultaneous Raman acquisition enables more reliable two-dimensional microplastic identification, overcoming limitations of individual FTIR or Raman techniques alone.
Development of a multi-spectroscopy method coupling μ-FTIR and μ-Raman analysis for one-stop detection of microplastics in environmental and biological samples
AI summary Read the abstract
Scientists developed a new method that combines two types of microscopic analysis (infrared and Raman spectroscopy) on a single sample to detect microplastics more accurately and efficiently. By using barium fluoride as a substrate instead of standard filter membranes, they could identify microplastics as small as 10 micrometers in environmental and biological samples. Better detection methods like this are crucial for understanding how much microplastic contamination exists in our environment and food.
Two-dimensional Identification of Microplastics: Comparison of Optical Photothermal Infrared Spectroscopy with Fourier-Transform Infrared and Raman Spectroscopy
AI summary Read the abstract
Researchers compared optical photothermal infrared (O-PTIR) spectroscopy with conventional FTIR and Raman microspectroscopy for two-dimensional identification of microplastic particles. The study found that O-PTIR offers complementary capabilities to established techniques, with inter-system spectral comparisons demonstrating reproducibility across instruments.
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.