Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy
Analytical and Bioanalytical Chemistry
Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy
Sebastian Primpke 0 1
Marisa Wirth 0 1
Claudia Lorenz 0 1
Gunnar Gerdts 0 1
0 Leibniz Institute for Baltic Sea Research Warnemünde , Seestraße 15, 18119 Rostock , Germany
1 Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research , Biologische Anstalt Helgoland, Kurpromenade 201, 27498 Helgoland , Germany
2 Sebastian Primpke
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.
Microplastics; Infrared; FTIR; Imaging; Database; Spectroscopy
The pollution of aquatic systems with small plastic particles
called microplastics (MP) [
] is an emerging topic in
environmental and analytical science [
]. These particles are
defined as < 5 mm in size and often further divided into
subcategories, e.g., large MP (5 mm–500 μm) and small MP (500–
1 μm) as described by Hidalgo-Ruz et al. . Two
Sebastian Primpke and Marisa Wirth contributed equally to this work.
introduction pathways for MP into the environment are
possible. The first is primary MP, to which the use and disposal of
microbeads in cosmetic and cleaning products largely
]. The second is secondary MP formed by fragmentation
of litter by mechanical or UV light-induced degradation. MP
are ubiquitous in the environment [
] and their reliable
monitoring is demanded within the European Marine Strategy
Framework Directive (MSFD) by descriptor 10 [
investigate and monitor MP pollution, it is necessary to identify the
]. One method is the visual identification without
further chemical identification, which has a high potential of
false counts. If MP are further investigated by chemical
identification, up to 70% falsely assigned particles can be found
. Therefore, chemical identification is necessary for
monitoring and different analytical methods are already in use for
To determine the mass of plastic within the sample, mass
spectrometry is combined with pyrolysis gas chromatography
] or thermal extraction desorption gas
chromatography (TED-GC) [
]. Both allow the chemical identification
of the polymer types as well as the determination of mass of
MP in a sample. Nonetheless, through these processes, the
sample is destroyed and particle sizes and numbers cannot be
calculated, which is a major drawback, for example, for
In contrast, spectroscopic methods like Fourier transform
infrared (FTIR) and Raman spectroscopy enable the measurement
of particle numbers and sizes as well as polymer identification.
Both methods identify the MP polymers through their molecular
vibrations in a complementary manner [
] and can be
introduced into microscopic setups, which allows chemical imaging
]. Single-element detectors, which are already frequently
applied in MP analysis , were used for first setups for
chemical imaging . Their major drawback is the high
measurement time necessary for large field sizes. The application of focal
plane array (FPA) detectors enables fast measurements of large
field sizes with high resolution [
], called FTIR imaging.
Typically, the (in)organic matrix of environmental samples is
reduced prior to measurement by chemical or enzymatic
treatment and the residue concentrated onto filters [
]. In earlier
13, 17, 18
], the complete filter areas were measured
followed by an analysis through integration of plastic
polymerspecific band regions for the generation of false color images.
The hereby pre-selected particles had to be compared via
manual comparison to reference spectra, which is a time-consuming
10, 11, 19
] and prone to human bias.
Through the development of an automated analysis
pipeline , it was shown that the expenditure of time and human
bias was reduced to a minimum, while large field sizes could
be measured. Further, it was found that small microplastic
particles, which were previously missed in manual analysis,
could now be successfully identified. The automated analysis
pipeline uses spectral correlation of the raw and first derivative
of vector-normalized spectra against a reference library for
chemical identification. Afterwards, both results are compared
and if an identical result is found, the pixel is counted as
identified. By image analysis, the size and number of particles
for each polymer are determined.
For all described FTIR-based analyses, the underlying
database is crucial for the quality of the results. 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].
In the case of the automated analysis, the spectra have to be
sorted into clusters, which are necessary, as no categorization
or too fine categorization would lead to errors in the
assignment process. Large gaps or several small particles instead of
one large particle would be assigned, falsifying the determined
particle abundance derived by image analysis. Especially in a
large database with several entries of similar materials, this is
likely to happen, as the same material can be assigned to
different database hits. For standardization of MP analysis,
the database should be designed with adaptability for future
research questions, while the original design can serve as a
reference point for future versions and different spectral
ranges and derivatives.
In this study, we present a detailed novel approach for an
adaptable database design (ADD) for the automated analysis
based on statistical methods followed by validation.
Therefore, we investigated the typical spectral range (3600–
1250 cm−1) for Anodisc [
] filter material regarding
differences within the reference spectra by cluster analysis. By
manual evaluation of the generated clusters and further validation,
an initial reference library in the spectral range 3600–
1250 cm−1 was determined for ADD and evaluated, which
can serve as a basis for future database adaptations.
Materials and methods
To set up a general spectral database, polymer samples from
different suppliers were measured via attenuated total
reflection (ATR)-FTIR spectroscopy on a Bruker Tensor 27 System
(Bruker Optics GmbH) with a diamond platinum ATR-unit
(Bruker Optics GmbH). The spectra were recorded in
absorbance mode within the range from 4000 to 400 cm−1 with a
resolution of 4 cm−1 and 32 scans were co-added. Each
measurement was performed in triplicate. Selected materials were
additionally measured in transmission mode via a μFTIR
microscope (see below) at a resolution of 8 cm−1 with six
The FTIR imaging measurements were performed on a
Bruker Tensor 27 spectrometer connected to a Hyperion
3000 μFTIR microscope (Bruker Optics GmbH) equipped
with a 64 × 64 FPA detector. The microscope is equipped with
a × 4 lens for the collection of visual images of the sample
surface and × 15 Cassegrain objectives for IR analysis. Data
collection was performed with the OPUS 7.5 (Bruker Optics
GmbH) software. All data shown was measured with 4 × 4
binning at a resolution of 8 cm−1 with six co-added scans in
accordance with literature [
]. The minimum detectable
particle size with these parameters was 11 × 11 μm.
Spectral database design
The recorded ATR spectra were processed using the OPUS
7.5 software. Three spectra for each sample were averaged
and an infobox was created containing sample name,
abbreviation, supplier, source ID, form, color, and method. The
spectra were baseline corrected using the concave rubberband
correction with 10 iterations and 64 baseline points. In the
case of black material, the spectra were subjected to an
extended ATR correction beforehand. For entries based on
transmission FTIR measurements, 20 single spectra were
isolated from each dataset and afterwards treated as described
above. However, a straight line was generated in the
wavenumber range of 2420–2200 cm−1 to exclude the CO2 band.
All spectra were made compatible so they contain the same
number of wavenumber datapoints in the considered spectral
range (x axis). Spectra with a low signal-to-noise ratio were
excluded afterwards. The combined data is further provided as
a Microsoft Excel Sheet (ESM_2.xslx) within the Electronic
Supplementary Material (ESM). Samples of different types of
polymer-based fibers as well as of different plant types and all
animal furs were received from the Bremer Faserinstitut in
Automated analysis and image analysis
The automated analysis and image analysis were conducted as
described in previous work . Briefly, all spectral analyses
were performed on HP KP719AV computers equipped with
an Intel© Core 2 Duo™ processor, 8-GB RAM, AMD
Radeon HD 5450 graphic card, extra USB3.0 controller card,
and a SANDISK Extreme 64-GB USB stick. The library
searches were performed through a macro within the OPUS
For image analysis, the raw data was analyzed by Python
Script and SimpleITK [26, 27] functions using Anaconda
(Anaconda, Inc.) and Spyder on a HP Z400 workstation with
an Intel© Core Xeon W3550 CPU, 12-GB RAM, NVIDIA
Quadro FX 1800 graphic card, and an additional CSL PCI
Express Card USB3.0 controller. The results of the image
analysis were further investigated using OriginPro2017G
Cluster analysis with PRIMER 6
To generate clusters, spectra were subjected to a
hierarchical cluster analysis using the Primer 6 software
equipped with the Permanova+ package (PRIMER-E).
For this, all negative values in the spectra were set to
0. To exclude effects from different concentrations and
varying contacts between diamond crystal and material
during the ATR measurement, all data was normalized
to percentage. For the analysis, the Hellinger distance of
the different spectra was calculated and subsequently
subjected to cluster analysis.
For further investigations of the cluster analysis, the
similarity profile (SIMPROF) routine, a permutation procedure
that tests for the presence of sample groups, was used .
When applying it to the analysis of dendrograms generated via
hierarchical cluster analysis, it can provide stopping rules for
further fractionation of samples into subgroups.
Ref7P: For preparation of a reference sample with known
content, synthetic polymers as well as natural materials (seven
in total, see Table S1, ESM_3.pdf) with a size range from
approx. 150 μm down to a few microns were mixed. In a glass
bottle with a ground joint and stopper, each material was given
into MilliQ (30 mL, 0.22 μm, Merck Millipore) and the
spatula was washed afterwards thrice with 30% ethanol (3 × 1 mL,
filtered over 0.2 μm) each time. A Teflon-coated stirring bar
was added. Prior to filtration, the mixture was stirred for
30 min on a magnetic stirrer and 1 mL of the mixture was
filtered onto an Anodisc filter (0.2 μm, GE Whatman). The
filter was washed with 30% ethanol (5 mL) and dried for 24 h
at 30 °C. The sample was placed under the μFTIR microscope
and measured via FTIR imaging in the range of 3600–
Reference filters RefA to RefD: For each reference filter
(see Tables S2–S5 and Figs. S1–S4 for details, ESM_3.pdf),
small particles were either produced by cutting from polymer
foils or fine-grinded polymer samples were directly applied. In
each case, up to 11 materials were placed manually under a
stereomicroscope (SZX16, Olympus) onto an Anodisc
(0.2 μm) filter. The position and shape of the particles were
determined via an overview image prior to FTIR
measurement. The FTIR imaging measurements were performed in a
spectral range of 3600–1250 cm−1.
RefEnv1: To make the results comparable to those of our
previous study , the therein analyzed environmental
sample H18_21 was used as reference for the automated analysis.
As the original measurement was only conducted in the
spectral range of 3200–1250 cm−1, the sample was re-measured
via FTIR imaging with a range of 3600–1250 cm−1, as
Environmental sample RefEnv2: The environmental
sample was chosen from a previous study  of samples from
waste water treatment plants. It was collected at waste water
treatment plant Oldenburg on 13 August 2015 in front of a
post-filtration unit. The (in)organic matrix was removed via
enzymatic digestion [16, 17]. For comparison, the sample was
re-measured in the spectral range of 3600–1250 cm−1 (original
study 3200–1250 cm−1) , as described above. Prior to
image analysis, the data belonging to the polypropylene
support of the Anodisc filter was removed.
All reference datasets are available in the ESM as
JCAMPDx files (see ESM_4.zip).
To determine the data quality, the identified spectra were
additionally analyzed manually by expert knowledge. For this,
the spectra were opened with the OPUS 7.5 software and
compared to the assigned reference spectra. The measured
spectra were visually compared to the assigned reference
spectra regarding the presence/absence of essential and
additional bands. The categorization of the data quality was
performed in accordance with literature . Each spectrum was
labeled with a number of either 1, 0.75, 0.5, 0.25, or 0.01 in
dependence of the number of minor or major differences: 1 =
no difference, 0.75 = one minor difference, 0.5 = two minor
differences, 0.25 = three minor or one major difference,
0.01 ≥ three minor differences, or > one major difference.
Results and discussion
Data quality and modifications
For the design of the ADD, it has to be considered that the
achieved spectral quality for FTIR imaging is generally lower
than that for ATR measurements. Therefore, ATR spectra that
feature only minor differences needed to be grouped into
clusters. After the collection of all database spectra, it was found
that the ATR data showed a systematic artifact of the crystal in
the region from 2475 to 1970 cm−1. The influence of the
artifact was tested with a small dataset of eight polymer types
including low-density polyethylene (LDPE) and high-density
polyethylene (HDPE). Both materials only showed small
differences between the spectra (see Fig. S5, ESM_3.pdf). When
the artifact was replaced with a straight line (see Fig. S6,
ESM_3.pdf), it was possible to distinguish between HDPE
and LDPE, while it was impossible if the artifact was present.
All further materials could be well separated by cluster
analysis independent of the artifact. As no decisive information
can be measured within the region of the artifact by
ATRFTIR , the data in this region was replaced with a straight
line for the subsequent statistical cluster analysis.
First, it was evaluated whether SIMPROF is suitable to
automatically generate clusters from the obtained dendrogram by
calculating which spectra belong to the same statistical
subgroup. In Fig. S7 (ESM_3.pdf), the red dashed lines in the
dendrogram show clusters that SIMPROF determined as
belonging to the same subgroup. Hence, according to
SIMPROF, most spectra in Fig. S7 (ESM_3.pdf) belong to
different subgroups and clusters respectively. When the
significance level was lowered to 1% or increased to 10%, the
findings did not change significantly. Apparently, SIMPROF
is not a suitable method to determine the clusters. Even though
the spectra all belong to different subgroups according to
SIMPROF, they cannot be left as individual spectra for the
automated analysis, as explained above.
The alternative was the manual generation of clusters. In
this case, spectra were grouped into clusters if (1) they were
positioned on the same branch of the dendrogram and (2) the
spectra were identical or showed only minor differences by
expert knowledge. Spectra of the same polymer were also
grouped when greater differences between the spectra were
present but only if (1) was still fulfilled. Consequently,
clusters had to be generated manually based on expert knowledge.
Figure 1 shows the dendrogram that was obtained when
319%-normalized spectra from the ATR database were
subjected to a hierarchical cluster analysis. In total, 107 clusters
were generated manually. They usually consisted of more than
five and up to 29 spectra. In contrast, 56 clusters only
contained one spectrum. The latter ones were spectra of rather
unconventional polymers and other substances, of which only
one sample could be provided. Numbers were assigned to all
generated clusters and a library with 107 database entries was
Cluster optimization by reduction of clusters
In a first approach, reference samples RefEnv1 and Ref7P
were applied for performance tests of the initial library. The
analysis results were compared against the already-validated
results obtained with the database used in Primpke et al. .
Required modifications were evident when (1) individual
particles consisted of different database entries or (2) expected
particles did not get detected at all. The first case was caused
when the different pre-processing routines of the automated
analysis yielded the same database entry but found different
entries on the same particle. When the different routines
yielded different database entries and no match was found at
all, the second case ensued.
Figure 2 shows image analysis pictures that include
examples of single particles from filters RefEnv1 and Ref7P, which
showed matches with two or more different database entries.
The polyurethane (PUR) particle from Fig. 2a gave matches
with four different clusters: PUR 2, PUR varnish, polyester
urethane (PESTUR) 2, and alkyd varnish. This shows that
within the given spectral region and quality, it is not possible
to distinguish between polyurethanes and acrylic or alkyd
varnishes. This is understandable, as the urethane group
structurally resembles the ester groups present in alkyd varnishes or
acrylic polymers. In a similar manner, it was not possible to
separate animal fur (keratins) from zein, a protein material
extracted from corn (Fig. 2b). Figure 2c, d shows further
examples for polypropylene (PP), where one pixel was assigned
to polybutene/polypentene and a cellulose particle, where it
was impossible to distinguish between cellulose from different
plant sources. All clusters that were found to interfere with
others were checked for structurally similar substances and
merged at this step.
Based on the information gathered via the analysis of data
from filters RefA, RefB, RefEnv1, and Ref7P, a number of
similar changes were made, which are summarized in Fig. 1,
Fig. 1 Dendrogram of manually
generated clusters. For lucidity,
the spectra were grouped and the
number of contained spectra
written in brackets behind the
cluster name. All merged clusters
(see text for details) were
connected by green lines; for all
later excluded clusters, the lines
are marked red (reduction of
clusters) and orange (cluster
marked in red. Nonetheless, the overall approach of evaluating
the whole set of clusters at once and trying to identify all
interferences was found to be unsuitable. A number of
problems could be correctly identified and solved, but it
became more and more evident that a large amount of
modifications was necessary to create an applicable database. Hence,
Fig. 2 Image analysis pictures of
single particles with different
assigned database entries
evaluating the full set of clusters proved to be too complex and
time consuming. To further investigate the clustering for
ADD, the approach was changed at this point.
Cluster optimization based on cluster categories
In order to find a more suitable approach, the remaining 58
clusters were sorted into four categories according to their
importance for microplastic analysis. Aspects like produced
polymer amounts per year, water solubility, fields of application,
and, in relation to that, the expected abundance in
environmental MP samples were considered for the categories. The full list
of remaining clusters and their assigned categories is provided
in the ESM (see Table S6, ESM_3.pdf). The first category
contained clusters with the most abundant plastic polymers
(polyethylene (PE)/rubber, PP,
polystyrene/styreneacrylonitrile (PS/SAN), polycarbonate (PC), polyamide (PA),
polyvinylchloride (PVC), polyester/polyethylene terephthalate/
polybutylene terephthalate (PES/PET/PBT) and PUR/varnish),
silicone, and three common natural substances: cellulose,
animal fur, and quartz sand. These materials were categorized as
Bvery important^ for microplastic analysis and used for a basic
library. For the verification process, the reference samples RefA
to RefD were used, which consisted of materials of the different
remaining clusters. This basic library was verified and all
suitable clusters from categories 2 (Bimportant^) and 3 (Bless
important^) were introduced stepwise into the library. The
clusters from category 4 were marked Bnot important^ for
microplastic analysis at this stage and were excluded. During
the process, it was evaluated for any added cluster whether all
reference particles were identified, whether they were assigned
the correct database entry, and whether they disturbed the
assignment of any other particle.
Figure 3 illustrates how interferences with other substances
were detected. It depicts the closed image of the same cellulose
particle in absence/presence of the cluster silica gel within the
library (Fig. 3a–d). It is evident that with silica in the database,
the edges of the particle were not detected anymore, and thus
the depiction of the particle was smaller. All observed
interferences were evaluated in a similar manner. However, the benefit
of having a substance in the database was always weighed
against the deficit that was caused by the interference. For
example, Bchitin 1^ was found to slightly hinder the detection of
cellulose but since it is a very common component of marine
samples, it was kept in the library nonetheless.
All changes that were made during optimization based on
cluster categories are summarized in Fig. 1, marked in orange.
Upon examining the dendrogram in Fig. 1, it was striking that
not all mergings of clusters that were performed during the
optimization process were in unison with the structure of the
dendrogram. For example, the PUR/varnish cluster consisted
of two groups of clusters, which were located on different
branches of the dendrogram. This showed that, while the
cluster analysis was a helpful tool to sort the spectra, it was not
capable of completely predicting the necessary clusters for the
automated analysis. This underlines the fact that the
conducted cluster optimization process was vital for the development
of a functioning ADD.
During the optimization process, 20 clusters of in total 27
could be verified without constraints. To test the performance
of this preliminary database, obtained results from the analysis
of RefEnv1 were manually reanalyzed by expert knowledge.
The data quality determination was performed as described
previously in literature . It was discovered that the clusters
PC, polymethyl methacrylate (PMMA), polysulfone (PPSU),
PS/SAN, PA, and PVC were assigned correctly within the
9 5 % c o n f i d e n c e i n t e r v a l . F o r P P, p o l y a c e t a l /
polyoxymethylene (POM), and PUR/varnish, error values
between 10 and 50% were found. On the contrary, the clusters
PE/rubber and silicone yielded a high number of false
assignments at this stage. This showed that the ADD still required
improvement. While the cluster silicone was removed, the
reseparation of the PE/rubber cluster into the separate PE,
chlorinated PE, and rubber 2 clusters substantially reduced the
amount of misassignments and improved particle
identification and depiction. A second change that became evident from
the manual reanalysis was the merging of the acrylates and
PUR/varnish cluster. These materials could not be
distinguished from one another in the considered spectral region.
No further improvements regarding the results for filters RefA
to RefD could be achieved (see Fig. S8, ESM_3.pdf).
Optimization with transmission FTIR data
While the ATR spectra yielded a suitable database for polymer
identification, larger particles were often not targeted well.
This is caused if total absorbance occurs during transmission
measurements. If one datapoint reaches the limit of detection,
the measured values will be independent of the rest of the
spectrum and characteristic bands get lost. Based on filters
RefA to RefD, the respective transmission spectra were
collected and added to the distinct clusters manually without
further analysis. This further allowed the reintroduction of
the clusters polycaprolactone and ethylene-vinyl-acetate
(EVA). All materials introduced as transmission FTIR data
are summarized in Table S7 (ESM_3.pdf).
Afterwards, the particle identification of reference samples
RefA to RefD (see Fig. S9, ESM_3.pdf) improved and most
particles could be identified. In conclusion, the introduction of
transmission FTIR data in ADD was found to be a necessary
Introduction of new materials into ADD
As a last step for the setup of ADD, it was exemplarily
investigated how to introduce further materials into the database. In a
recent study , large amounts of black particles were found
in deep sea sediments, which were presumed to consist of coal.
Cluster number adaptable database design
Number of contained spectra
To include these new materials, six spectra of coal (charcoal and
conventional coal) were measured via ATR-FTIR and the data
was handled as described above. After extending the dataset, an
analogous cluster analysis was performed.
The coal spectra were included in the dendrogram as new
clusters (see Fig. S10, ESM_3.pdf), while the overall
dendrogram structure did not change significantly. The new dataset
was manually binned into two new clusters for the automated
analysis (charcoal, 2 spectra and coal, 4 spectra) afterwards.
This process highlights the ability to add new spectra/
materials to the existing ADD by a combination of cluster
analysis and manual clustering. With this data included, the final
reference design for ADD was determined with 32 clusters (see
Table 1) and is available in the ESM (see ESM_5.xslx).
Performance of the ADD
The overall performance of the ADD was benchmarked
against two reference samples (RefEnv1 and Ref7P) and an
environmental sample (RefEnv2). Results from the analysis of
RefEnv1 with the ADD are depicted in Fig. 4. For a better
overview, each polymer was highlighted with a different RGB
value (see Table S8 for details, ESM_3.pdf). In general, many
small particles and one large particle that was assigned to the
acrylates/PUR/varnish cluster were detected. High loads of
plant fiber (gray), rubber type 3 (yellow), PP (brown), and
PPSU (light blue) were found. The particle size distribution
(see Fig. S11, ESM_3.pdf) had a maximum at the size class of
11 μm, representing 35% of the determined polymer particles,
while 85% had a smaller size than 50 μm. 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
Fig. 4 Polymer-type-dependent
false color image of the sample
RefEnv1 after automated analysis
with the adaptable database
the automated analysis , the ADD was further validated by
expert knowledge via manual reanalysis (see Table S9,
ESM_3.pdf), and results between both studies were
With the ADD, a higher amount of particles (1221 herein
versus 1097 before ) was detected. The relative share of
certain assignments increased from 82.1 to 82.8% while
misassignments decreased from 3.1 to 1.6%. The general data
quality, especially of PE and PVC, was several times better than
that in the previous study, which shows the necessity of a
welldetermined database design. However, the overall number of
plastic particles decreased from 733 to 195. One possible
explanation would be particle loss between the measurements,
which could be excluded by visual inspection of the overview
images. It was found that only one prominent particle was
missing and several slightly changed their position. The major
difference between the present study and previous ones [20, 25]
is that the range for the library search was broadened by
400 cm−1 to 3600–1250 cm−1. Diatom shells, which were still
abundant in this extracted sediment sample, have a weak band
(–Si–O–H bonds) in the range from 3200 to 3600 cm−1. It can
be assumed that this band, which was present as background
signal over almost the complete filter (see Fig. S12,
ESM_3.pdf), hampered the identification success of plastic
polymers. To test the hypothesis, the database from the previous
study  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. This result is
much closer to what was achieved with ADD in this study
and thus confirmed the hypothesis. The second main difference
between the results from previous and present database was a
reduction in the amount of detected varnish particles. This is
reasonable, as one particular reference varnish spectrum was
not included into ADD, as the material was no longer available.
In the following, sample Ref7P was analyzed (see Figs.
S13 and S14, ESM_3.pdf) to prove the ability of ADD to
distinguish polymers at high sample loads with different
polymers present in close proximity to each other. In total, 96.4%
of the particles were assigned to the correct polymer cluster.
Only in the case of copolyamide, a higher amount of
misassignments (8.7%) was found, mainly to the cluster
polycaprolactone. All other polymers were assigned correctly
to their respective clusters for over 95% of the database hits.
The results show that ADD is capable of assigning polymers
even from complex mixtures and is therefore suitable for
further application on environmental samples.
For this, a sample of treated waste water (RefEnv2)
was chosen. When applying ADD to this dataset,
different types of particles, mainly PE, PP, varnish, EVA, and
rubber, could be successfully identified (Figs. 5 and 6),
Fig. 6 Size distribution and
polymer composition for plastic
particles derived via automated
analysis for the sample RevEnv2.
The region for particles with a
size > 50 μm was highlighted for
a better overview
demonstrating the high variability of polymers present in
treated waste water. In the sample, 90% of plastic particles
were smaller than 50 μm in size, while 53% of the overall
particles were found in the smallest size class of 11 μm. The
analysis of RevEnv2 highlights the performance of ADD on
complex samples. Nonetheless, the previously discussed
results from RefEnv1 showed that a background signal from
diatom shells in extracted sediment samples can hinder
polymer identification, which is currently a limitation of the
method and has to be addressed during sample treatment.
All in all, however, the chosen approach of combining
statistical methods, expert knowledge, and manual validation
proved to have produced a versatile database for the analysis
of MP in environmental samples. Furthermore, the chosen
approach is also suitable for Raman spectra (data not shown).
First studies based on the combination of automated analysis
and ADD have already been published [23–25].
It could be shown that through the statistical analysis and
manual clustering of reference spectra, the basis for an
adaptable reference database for the analysis of MP can be
provided. While the final clustering had to be based on expert
knowledge, the general scheme allowed a straightforward
assignment of new materials to existing entries or as entirely new
entries. The generated database was benchmarked against six
reference datasets and it was confirmed that the chosen setup
can identify particles of various sizes and materials. Through
the exemplary test on an environmental sample, it could be
proven that the database is applicable to complex sample
material. Moreover, the ADD can be expanded with new spectra
in the future, allowing the harmonization of the FTIR analysis.
In addition, by providing a reference dataset with five
reference samples and an environmental sample for validation and
comparison, new and old databases can be referenced to the
ADD. This significantly increases the comparability of FTIR
studies for past and future publications.
Acknowledgments The authors thank the crew of the RV Heincke for
Funding information This work was supported by the German Federal
Ministry of Education and Research (Project BASEMAN—Defining the
baselines and standards for microplastics analyses in European waters;
BMBF grant 03F0734A). C.L. thanks the Deutsche Bundesstiftung
Umwelt (DBU) for financial support.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
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