µFTIR detection of tyre wear particles: A new analytical process using neural network modeling to attempt their identification
Abstract
Tyre wear particles (TWPs) are a major source of microplastic contamination. Detection and quantification of TWPs in the environment is a major challenge. However, several methods are currently used, with their own advantages, disadvantages and limitations. In this study, a new method based on infrared micro spectroscopy was developed. In contrast to more conventional approaches based on spectral matching of characteristic peaks, a multi-criteria designation using a heatmap was chosen. This method was developed using absorbance and average spectral variation. These criteria were chosen due to their high potential for distinguishing carbon black filled. A neural network deep learning model was trained using spectral data from various microplastics and sediments. To test the detection performance of the model, the model was tested on some samples containing a known number of tyre particles. The method demonstrated high detection efficiency without false positives. However, the few particles omitted were associated with the use of a filter not perfectly flat. Analyses testing the method on a filter containing sediment from a stormwater pond revealed the presence of numerous particles corresponding to TWPs. This method is an innovative and promising approach for further studies on TWPs. Repeated application and continuous model training should improve its reliability, including more complex environmental matrices.
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BibTeX
@article{picard2026µftir,
title = {{µFTIR detection of tyre wear particles: A new analytical process using neural network modeling to attempt their identification}},
author = {Coralie Le Picard and Hélène Thomas and Tony Agion and Arno Bringer and Alexandre Michelet},
journal = {Talanta Open},
year = {2026},
month = sep,
doi = {10.1016/j.talo.2026.100656},
url = {https://univ-rochelle.hal.science/hal-05769540v1},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - µFTIR detection of tyre wear particles: A new analytical process using neural network modeling to attempt their identification
AU - Coralie Le Picard
AU - Hélène Thomas
AU - Tony Agion
AU - Arno Bringer
AU - Alexandre Michelet
PY - 2026
DA - 2026/09/29
JO - Talanta Open
DO - 10.1016/j.talo.2026.100656
UR - https://univ-rochelle.hal.science/hal-05769540v1
AB - Tyre wear particles (TWPs) are a major source of microplastic contamination. Detection and quantification of TWPs in the environment is a major challenge. However, several methods are currently used, with their own advantages, disadvantages and limitations. In this study, a new method based on infrared micro spectroscopy was developed. In contrast to more conventional approaches based on spectral matching of characteristic peaks, a multi-criteria designation using a heatmap was chosen. This method was developed using absorbance and average spectral variation. These criteria were chosen due to their high potential for distinguishing carbon black filled. A neural network deep learning model was trained using spectral data from various microplastics and sediments. To test the detection performance of the model, the model was tested on some samples containing a known number of tyre particles. The method demonstrated high detection efficiency without false positives. However, the few particles omitted were associated with the use of a filter not perfectly flat. Analyses testing the method on a filter containing sediment from a stormwater pond revealed the presence of numerous particles corresponding to TWPs. This method is an innovative and promising approach for further studies on TWPs. Repeated application and continuous model training should improve its reliability, including more complex environmental matrices.
SN - 2666-8319
ER -