Abstract:We present an automated approach to reduce the time demand currently needed for data analyses. We have developed a novel analysis pipeline, followed by image analysis with Python and Simple ITK image processing modules.
“…The majority of the plastic
particles were assigned to PP with 39%, PPSU with 26%, and rubber with 18%. Similar to the previous study for the automated analysis [20], the ADD was further validated by expert
knowledge via manual reanalysis (see Table S9, ESM_3.pdf), and
results between both studies were compared.Fig. 4Polymer-type-dependent false color image of the sample RefEnv1
after automated analysis with the adaptable database design…”
Section: Resultsmentioning
confidence: 99%
“…S12, ESM_3.pdf), hampered the identification success of
plastic polymers. To test the hypothesis, the database from the previous study
[20] was applied to the re-measured
dataset RefEnv1 (larger wave number range). In total, 701 particles could be
identified, of which 281 were made of plastic.…”
Section: Resultsmentioning
confidence: 99%
“…While different methods are available for
data handling [20–22], commercial
databases are unsuitable for these methods. Application of the automated analysis on
different samples made it clear that for standardized analyses, a specialized
database design is necessary to distinguish between different materials in specific
spectral ranges [23–25].…”
The identification of microplastics becomes increasingly challenging
with decreasing particle size and increasing sample heterogeneity. The analysis of
microplastic samples by Fourier transform infrared (FTIR) spectroscopy is a
versatile, bias-free tool to succeed at this task. In this study, we provide an
adaptable reference database, which can be applied to single-particle identification
as well as methods like chemical imaging based on FTIR microscopy. The large
datasets generated by chemical imaging can be further investigated by automated
analysis, which does, however, require a carefully designed database. The novel
database design is based on the hierarchical cluster analysis of reference spectra
in the spectral range from 3600 to 1250 cm−1. The hereby
generated database entries were optimized for the automated analysis software with
defined reference datasets. The design was further tested for its customizability
with additional entries. The final reference database was extensively tested on
reference datasets and environmental samples. Data quality by means of correct
particle identification and depiction significantly increased compared to that of
previous databases, proving the applicability of the concept and highlighting the
importance of this work. Our novel database provides a reference point for data
comparison with future and previous microplastic studies that are based on different
databases.
Graphical abstractᅟ
Electronic supplementary materialThe online version of this article (10.1007/s00216-018-1156-x) contains supplementary material, which is available to authorized
users.
“…The majority of the plastic
particles were assigned to PP with 39%, PPSU with 26%, and rubber with 18%. Similar to the previous study for the automated analysis [20], the ADD was further validated by expert
knowledge via manual reanalysis (see Table S9, ESM_3.pdf), and
results between both studies were compared.Fig. 4Polymer-type-dependent false color image of the sample RefEnv1
after automated analysis with the adaptable database design…”
Section: Resultsmentioning
confidence: 99%
“…S12, ESM_3.pdf), hampered the identification success of
plastic polymers. To test the hypothesis, the database from the previous study
[20] was applied to the re-measured
dataset RefEnv1 (larger wave number range). In total, 701 particles could be
identified, of which 281 were made of plastic.…”
Section: Resultsmentioning
confidence: 99%
“…While different methods are available for
data handling [20–22], commercial
databases are unsuitable for these methods. Application of the automated analysis on
different samples made it clear that for standardized analyses, a specialized
database design is necessary to distinguish between different materials in specific
spectral ranges [23–25].…”
The identification of microplastics becomes increasingly challenging
with decreasing particle size and increasing sample heterogeneity. The analysis of
microplastic samples by Fourier transform infrared (FTIR) spectroscopy is a
versatile, bias-free tool to succeed at this task. In this study, we provide an
adaptable reference database, which can be applied to single-particle identification
as well as methods like chemical imaging based on FTIR microscopy. The large
datasets generated by chemical imaging can be further investigated by automated
analysis, which does, however, require a carefully designed database. The novel
database design is based on the hierarchical cluster analysis of reference spectra
in the spectral range from 3600 to 1250 cm−1. The hereby
generated database entries were optimized for the automated analysis software with
defined reference datasets. The design was further tested for its customizability
with additional entries. The final reference database was extensively tested on
reference datasets and environmental samples. Data quality by means of correct
particle identification and depiction significantly increased compared to that of
previous databases, proving the applicability of the concept and highlighting the
importance of this work. Our novel database provides a reference point for data
comparison with future and previous microplastic studies that are based on different
databases.
Graphical abstractᅟ
Electronic supplementary materialThe online version of this article (10.1007/s00216-018-1156-x) contains supplementary material, which is available to authorized
users.
“…Besonders Focal‐Plane‐Array‐ (FPA)‐Detektoren erlauben es, in der FTIR‐ Mikrospektroskopie eine größere Fläche gleichzeitig zu scannen. Die Auswertung der Daten lässt sich anschließend automatisieren, was eine schnelle, detailreiche Ermittlung der Ergebnisse in Form von Polymerverteilung, Größenverteilung und Partikelanzahl ermöglicht .…”
Section: Analyse Von Mikroplastikunclassified
“…Die Messung mittels FPA‐FTIR‐Mikrospektroskopie hat den Vorteil, dass die komplette Filterfläche und nicht jeder einzelne Partikel gemessen wird, und dieses vergleichsweise schnell erfolgt . In Abbildung ist ein Beispiel für das Ergebnis der automatischen Auswertung einer Filterprobe gezeigt . Bei den Klärwerksproben wurden bei einer Auflösung von 11 µm über 1,7 Millionen Spektren pro Probe gemessen, was ca.…”
Zusammenfassung
Die Untersuchung von Umweltproben auf Mikroplastik stellt eine große analytische Herausforderung dar. Bislang gibt es keine Langzeitdaten über die Variabilität und Zusammensetzung der gefundenen Belastungen. Es stehen aktuell unterschiedliche Varianten zur Probennahme und zur Mikroplastikextraktion aus Umweltproben zur Verfügung. In bisherigen Untersuchungen wurden sie jedoch nicht einheitlich durchgeführt, was die Vergleichbarkeit von erhobenen Daten erschwert. Anhand einer Beprobung von zwei Klärwerksausflüssen an unterschiedlichen Tagen konnte gezeigt werden, dass es deutliche Schwankungen in der Mikroplastikkonzentration am Klärwerksausfluss gab. Die jeweils festgestellte Partikelzusammensetzung unterschied sich ebenfalls an beiden Tagen. Es konnte gezeigt werden, dass eine Filtrationseinheit in Oldenburg die Mikroplastikbelastung um > 70 % reduziert und dabei Partikel größer >100 µm effizient entfernt. Die Verteilung der Partikelgrößen weist auf eine verstärkte Emission von sehr kleinen Partikeln gerade kleiner 50 µm hin.
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