Titlebar

Export bibliographic data
Literature by the same author
plus on the publication server
plus at Google Scholar

 

Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning

Title data

Hufnagl, Benedikt ; Stibi, Michael ; Martirosyan, Heghnar ; Wilczek, Ursula ; Möller, Julia N. ; Löder, Martin G. J. ; Laforsch, Christian ; Lohninger, Hans:
Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning.
In: Environmental Science & Technology Letters. Vol. 9 (2022) Issue 1 . - pp. 90-95.
ISSN 2328-8930
DOI: https://doi.org/10.1021/acs.estlett.1c00851

Project information

Project title:
Project's official titleProject's id
SFB 1357 Mikroplastik391977956
PLAWES – The Journey of Microplastics from the River Weser to the North SeaBMBF Project PLAWES, grant 03F0789A
MiKoBo BWMK18007BWMK18007
BabbA - Biologisch abbaubare Beutel in der BioabfallverwertungBWBAW20101

Project financing: Bundesministerium für Bildung und Forschung
Deutsche Forschungsgemeinschaft
Andere

Abstract in another language

The problem of automating the data analysis of microplastics following a spectroscopic measurement such as focal plane array (FPA)-based micro-Fourier transform infrared (FTIR), Raman, or QCL is gaining ever more attention. Ease of use of the analysis software, reduction of expert time, analysis speed, and accuracy of the result are key for making the overall process scalable and thus allowing nonresearch laboratories to offer microplastics analysis as a service. Over the recent years, the prevailing approach has been to use spectral library search to automatically identify spectra of the sample. Recent studies, however, showed that this approach is rather limited in certain contexts, which led to developments for making library searches more robust but on the other hand also paved the way for introducing more advanced machine learning approaches. This study describes a model-based machine learning approach based on random decision forests for the analysis of large FPA-μFTIR data sets of environmental samples. The model can distinguish between more than 20 different polymer types and is applicable to complex matrices. The performance of the model under these demanding circumstances is shown based on eight different data sets. Further, a Monte Carlo cross validation has been performed to compute error rates such as sensitivity, specificity, and precision.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Polymer particles; Machine learning; Fourier transform infrared spectroscopy; Software; Polymers
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Biology > Chair Animal Ecology I > Chair Animal Ecology I - Univ.-Prof. Dr. Christian Laforsch
Research Institutions > Research Centres > Bayreuth Center of Ecology and Environmental Research- BayCEER
Research Institutions > Collaborative Research Centers, Research Unit > SFB 1357 - MIKROPLASTIK
Faculties
Faculties > Faculty of Biology, Chemistry and Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Biology
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Biology > Chair Animal Ecology I
Research Institutions
Research Institutions > Research Centres
Research Institutions > Collaborative Research Centers, Research Unit
Result of work at the UBT: Yes
DDC Subjects: 500 Science > 540 Chemistry
500 Science > 570 Life sciences, biology
500 Science > 590 Animals (Zoology)
Date Deposited: 15 Dec 2021 08:23
Last Modified: 21 Jan 2022 07:46
URI: https://eref.uni-bayreuth.de/id/eprint/68152