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. 2019 Dec 13:7:100742.
doi: 10.1016/j.mex.2019.11.015. eCollection 2020.

Automated rapid & intelligent microplastics mapping by FTIR microscopy: A Python-based workflow

Gerrit Renner  1   2 , Torsten C Schmidt  2 , Jürgen Schram  1
Affiliations

Automated rapid & intelligent microplastics mapping by FTIR microscopy: A Python-based workflow

Gerrit Renner et al. MethodsX. .

Abstract

The analysis of environmental microplastic particles using FTIR microscopy is a challenging task, due to the very high number of individual particles within a single sample. Therefore, automatable, fast and robust approaches are highly requested. Micro particles were commonly enriched on filters, and sub- or the whole filter area was investigated, which took more than 20h and produced millions of data, which had to be evaluated. This paper presents a new approach of such filter area analysis using an intelligent algorithm to measure only those spots on a filter that would produce evaluable FTIR data. Empty spaces or IR absorbers like carbon black particles were not measured which successfully reduced the total analysis time from 50h to 7h. The presented method is based on system independent Python workflow and can easily be implemented on other FTIR systems. •Fast and intelligent FTIR microscopy area mapping without FPA detector•Total time reduction from 50 h to 7 h•Platform independent approach based on Python.

Keywords: Chemometrics; FTIR microscopy; Mapping; Microplasitcs; Python.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

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Graphical abstract
Fig. 1
Fig. 1
Schematic workflow to extract the numeric spectral data from a screenshot of a plotted FTIR spectrum. Each pixel is a member of an n × m matrix and contains a gray scaled value. The line of the plotted spectrum is black and can be extracted by looking for the lowest gray scaled values within every column. The corresponding row indices correlate with the absorbance values of the original FTIR spectrum.
Fig. 2
Fig. 2
The overviewing camera image of the mapped area consists of 40,000 individual overlapping photos. These were merged using a Gaussian gradient to avoid visible cutting edges.
Fig. 3
Fig. 3
The individual photos were merged together, and an optical particle recognition was performed. All measured FTIR spectra were assigned to the detected particles, while each FTIR spectrum covered four tiles, due to the overlapping measurements. The false coloring of each particle was based on the most common identification results of the FTIR spectra.
Fig. 4
Fig. 4
Overview of the three mapped microplastics samples.
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See this image and copyright information in PMC

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