Quantitative and Qualitative Differences of Common Microplastic Detection Procedures: Nile Red- assisted Fluorescence Microscopy and Confocal Micro-Raman Spectroscopy

Quantitative and Qualitative Differences of Common Microplastic Detection Procedures: Nile Red- assisted Fluorescence Microscopy and Confocal Micro-Raman Spectroscopy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantitative and Qualitative Differences of Common Microplastic Detection Procedures: Nile Red- assisted Fluorescence Microscopy and Confocal Micro-Raman Spectroscopy Steve Utecht, Stefan Krause, Tobias Schuetz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5803470/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 9 You are reading this latest preprint version Abstract Microplastics (MPs) are pervasive and widespread pollutants penetrating ecosystems worldwide, including aquatic environments and sediments. The lack of standardised evaluation procedures and limited sample throughput hampers accurate assessment of global MP pollution. High-throughput analytical methods are crucial for advancing our understanding of MP cycling in the environment. This study compares MP observations by confocal micro-Raman spectroscopy and Nile Red-assisted fluorescence microscopy to evaluate their effectiveness for high-throughput MP analysis using the percentage differences between the results of the two methods. The results show the influence of particle size on the detected percentage differences and demonstrate that both methods deliver better matching results at smaller particle sizes. The overall percentage difference between the two methods is 421%, with variations ranging over three orders of magnitude depending on morphological characteristics (particles and fibres) and particle size, whereas plastic type did not significantly affect results. The combination of the Fenton reagent's limited organic matter removal and the resulting increased risk of false-positive MP detection, along with Raman spectroscopy's ability to reliably distinguish MPs from organic components, offers opportunities for data validation and correction to enhance accuracy and reliability of the results. This study contributes to the development of robust methods for high-throughput MP analysis, enabling improved spatial and temporal monitoring of its fate and transport in natural fluxes. Microplastics Nile Red-assisted fluorescence microscopy Confocal micro-Raman spectroscopy Fenton`s reagent Data validation and correction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Although pollution by MP (< 5 mm in size) has been evidenced around the globe, there is still a critical lack of understanding the underlying fate and transport mechanisms that control MP occurrence and exposures in the environment (Käppler et al., 2015 ; Li et al., 2018 ; Tagg et al., 2015 ; Thompson et al., 2009 ). The lack of inter-comparability of results from existing studies contributes to the absence of comprehensive monitoring programs and standardized methods for the assessment of MPs in natural systems (Müller et al., 2020 ). Analytical techniques frequently used for quantitative and qualitative detections of MP samples such as confocal micro-Raman spectroscopy (cmRs), Fourier-transform infrared spectroscopy (FTIR) or pyrolysis-gas chromatography-mass spectrometry (pyro-GC-MS) show high detection accuracy but come at high instrumental and staffing cost (Shim et al., 2016 ; Sturm et al., 2021 ; Vašková, 2011 ). The cmRs is usually considered as a robust technique for the detection of MP offering high precision quantitative and qualitative measurements of a wide range of polymer types (Anger et al., 2018 ; Chakraborty et al., 2023 ; Dąbrowska, 2021 ; Sobhani et al., 2019 ; Xu et al., 2019 ). In addition, there is high certainty in separating organic matter detections from the data, as the obtained spectra do not coincide with plastic spectra. In the case of exclusively applying cmRs for the detection of MPs, very long measurement times severely limit high-throughput analyses required to process larger sample quantities. Other limitations include susceptibility to spectral fluorescence interference, the analytical detection limit of 1 µm, weak or altered signals from solid samples due to natural-related modifications and the lack of free available reference libraries (Ivleva et al., 2021; Shim et al., 2017 ; Wirnkor et al., 2019 ), Hence, the scope of studies aiming at MP detection is often limited by the restricted number of samples that can be analysed in a reasonable timeframe (Xu et al., 2019 ). Addressing this critical limiting factor, more efficient methodological approaches that take into account simplicity in application, time and cost efficiency, as well as accuracy, reliability and repeatability for the detection of MP from natural samples are urgently needed (Abusafia et al., 2023 ; Hengstmann and Fischer, 2019 ; Hurley et al., 2018 ; Jain et al., 2018 ; Prata et al., 2019a ; Renner et al., 2019 ; Tagg et al., 2017 ). Because of its ease of use, the fluorescent dye Nile Red (NR) in combination with fluorescence microscopy is frequently used as an inexpensive, time-efficient and widely accessible method for the quantification of MPs (Maes et al., 2016; Meyers et al., 2022 ; Nel et al., 2021 ; Patchaiyappan et al., 2020 ; Sancataldo et al., 2020 ; Shim et al., 2016 ). Many pristine plastic polymer types stain reasonably well with NR across different particle sizes, ranging from a few nanometres up to several centimetres, generating high-intensity NR signals that can be efficiently detected by fluorescence microscopy (Bianco et al., 2022; Carter et al., 2023 ; Cole, 2016 ; Kang et al., 2020 ; Tamminga et al., 2017; Zhang et al., 2023 ). Besides that, NR-dyed samples spiked with known quantities of MP show high recovery rates, proving that NRafm is an efficient MP detection method (Pan et al., 2022; Shim et al., 2016 , Stanton et al., 2019 ). Additionally, the application of NR-based staining procedures reduces measurement times and in combination with spectroscopic MP detection techniques such as cmRs, a reliably plastic polymer identified can be achieved. It should be noted that the Nile Red staining method, when used as a standalone method for detecting MP, is not recommended (Shim et al., 2016 ). Recent developments in the interpretation of photographic (NR) fluorescence (pixel brightness) images are promising and support the development of semi-automatic threshold-based separations between MP and organic material (Kukkola et al., 2023 ; Sancataldo et al., 2020 ). However, photographic (NR-) fluorescence images show that organic matter and MP can emit similar light intensities indicating that the presence of organic matter in natural samples may limit the detection accuracy of MPs (Konde et al., 2023 ; Sturm et al., 2021 & 2023 ). In addition, the use of organic solvents in the NR staining procedures of MPs carries the risk of overlooking organic solvent-induced morphological modifications as shown by Tamminga et al. (2017) and Kang et al. ( 2020 ) for polystyrene for instance. In an unpublished experiment, we determined a weight loss of 1% after 5 minutes and 7% after 20 minutes for PMMA (d = 2 mm) when exposed to an acetone-ethanol mixture (50:50). This limits the use of NR as a standalone tool for the detection of MP from natural systems. Both, the inadvertently labelling of residues of organic matter and potentially organic solvent-induced modifications of MPs, contribute to the generation of false positives MP detections. To perform reliable high-throughput MP analyses of natural samples, it is important to evaluate the opportunity of streamlining MP analyses by applying dual-method approaches combining the benefits of different detection techniques, which may facilitate the transition from punctual samplings to larger-scaled measurement campaigns (Liu et al., 2022 ; Range et al., 2023 ; Shim et al., 2016 ; Shruti et al., 2021). Besides the NRafm coupling with cmRs, other analytical techniques such as FTIR spectroscopy, flow cytometry, PLE spectroscopy, and O-PTIR spectroscopy have also been successfully combined with NRafm (Barett et al., 2020; Bianco et al., 2022; Erni-Cassola et al., 2017 ; Kang et al., 2020 ; Konde et al., 2023 ; Maes et al., 2017 ; Pan et al., 2023 ; Shim et al., 2016 ; Sturm et al., 2021 ; Valine, 2019; Vermeiren et al., 2020 ). To reliably exploit the strengths of the cmRs and the NRafm in dual-method detection approach for higher sample throughputs, this study (1) qualitatively and quantitatively evaluates the streamlining of MP detections by evaluating the percentage differences (%DIFs) of 100 environmental samples across five selected MP particle sizes (5000–25 µm). We (2) compare the reproduction of the detected morphological features (particles or fibres) of both, NRafm and cmRs techniques to better understand methodological strengths and limitations of the presented dual-method approach. The overall ambition of this study is to contribute to the ongoing development of suitable MP detection methods for high-throughput field campaigns that can more reliably capture the nature of MP environmental fate and transport through the analysis of representative sample quantities. 2. Materials and methods 2.1 Extraction of MP from river sediment samples A total of twenty frozen sediment cube samples (SCS with a volume of 125 ccm and an edge length of 5x5x5 cm; The total volume consisting of 20 SCSs corresponds to 2500 ccm) were cut from two frozen sediment cores obtained at the Ruwer, a gravel bed tributary of the river Moselle near Trier in southwest Germany. The cores were extracted using the freeze-core sampling method with liquid nitrogen as a coolant (Hauer et al., 2020 ; Straßer et al., 2014 ). The catchment area is located within the Rhenish Massif, which is primarily characterised by shale rock. As described below, each sample was fractionated into sub-samples encompassing five particle sizes ranging from 5000–25 µm, which resulted in a total of 100 samples. First, all sediment samples were placed in glass vessels and submerged in deionized water. The samples were sonicated (160 W for 20 min.) to break up sedimentary agglomerations. Particles with a diameter smaller than 25 µm were isolated by sieving from the samples to prevent rapid clogging of the finest sieves (25 µm). Next, density separation using sodium polytungstate (SPT-3) (TC-Tungsten Compounds) with a density of 1.8 g/cm 3 . Due to differences in sedimentation rates based on in grain size, fractions smaller than 250 µm were treated separately from those larger than 250 µm. For the > 250 µm fractions, density separation was carried by placing them in a crystallization dish (Ø 90 mm, 300 ml), covering the sample with SPT-3 and stirring with a spatula. Floating material was decanted and collected in a 125 µm sieve. This process was repeated until no visible floating material remained. The < 250 µm fractions, density separation was performed by placing them in a smaller crystallization dish (Ø 70 mm, 100 ml). After covering the samples with SPT-3 and stirring, the samples were left to rest in a refrigerator for 24 hours. After resting, the samples were decanted and collected in a 25 µm mesh sieve. Following density separation, both 250 µm fractions were cleaned with deionized water and oven-dried at 50°C for 4 hours. Subsequently, the sample fractions were recombined to remove organic matter through digestion using Fenton's reagent (30% H 2 O 2 + 20 g/L FeSO 4 , pH = 3) in a 500 ml beaker. Upon completion of the reaction, the sample was transferred to a 25 µm sieve and cleaned with deionized water to remove residual chemicals (Al-Azzawi et al., 2020 ; Campo et al., 2019 ; Frei et al., 2019 ; Hu et al., 2022 ; Hurley et al., 2018 ; Kabir et al., 2022 ). Finally, the samples were fractionated into five selected particle sizes (> 630 µm, > 250 µm, > 125 µm, > 75 µm and > 25 µm) using sieve cascades. The sample weights were measured to estimate the number of particles on each filter membranes. 2.2 Semi-automatic confocal micro-Raman spectroscopy For the cmRs-based qualification and quantification, the particle size fractions > 250 µm were placed centrally on a glass plate equipped with a graph paper (Fig. 7 C) and evenly distributed by gently tapping from the underside before being spectroscopically measured. Composite microscopic imaging stacks of the samples were recorded to obtain the coordinates of the objects on the glass plate, enabling semi-automatic measurements using the Sample Raster feature in WITec Control 4.1 software. The particle size fractions < 250 µm were homogenously transferred onto glass fibre (GF) filter membranes (Macherey-Nagel GF-2, Ø 45 mm, pore site 0.5 µm) using vacuum filtration and subsequently dried in an oven at 50°C. A portion of the filter membranes comprising 49.76% (2.5 cm x 2.5 cm with an area of 6.25 cm²) of the total filter membrane was microscopically mapped to locate the objects (Objective: Zeiss EC Epiplan-NEOFLUAR 10x magnification) before qualitatively and quantitatively measured using cmRs (WITec alpha 300 R; Operating system: WITec Control 4.1). Within the mapped area of the filter membranes, a maximum of 400–500 particles were selected, a process that took approximately 1 hour. It should be noted that the portion of the area required for selecting 400–500 particles decrease relatively to the total mapped area of the filter membrane as particle size decreases. Once the maximum number of particles was reached, the portion of the area occupied by the selected particles was estimated as a percentage and extrapolated to the total filter membranes (12.56 cm²) (e.g., McCormick et al., 2016 ; Käppler et al., 2016 ; Xu et al., 2019 ). For particle detection, a 532 nm laser with laser intensity of 4 mW was used. A single spectral scan was performed with an integration time of 10 seconds and one accumulation, resulting in measurement durations of approximately 3 hours. All recorded spectra were visually inspected and potential MP spectra were manually re-measured with variable laser intensities (0.1 to 15 mW) to improve quality for comparison with a reference library. 2.3 Nile Red application and acquisition After applying the cmRs, particle larger than 250 µm were placed in a drilled notch of a piece of aluminium holder and covered with NR solution at a concentration of 1 mg/ml (Tamminga et al., 2017) in a chloroform-acetone mixture (3:1). The NR solution was allowed to fully evaporate. Subsequently, the fluorescence-labelled particles (> 250 µm) were evenly distributed on a glass plate and illuminated with blue LED light (~ 470–475 nm). All NR-labelled particles were manually counted using a magnifying glass and a blue bandpass filter (orange foil) to evaluate differences in the number of MP counts between cmRs and NR staining (Konde et al., 2020 ; Prata et al., 2019b ). The smaller particle sizes (< 250 µm) were stained with NR directly on the filter membranes by applying the dye dropwise. Three mapping areas, measuring 0.83 cm x 0.83 cm and covering an area of 2.07 cm², were analysed. After recording using the confocal Raman microscope, the results were extrapolated to represent the total filter membrane. To optimise sample illumination, three blue light LED flashlights were positioned around the microscope. During fluorescence imaging, the objective was equipped with a blue bandpass filter attached to the 10x magnification objective. The room was darkened and the monitor was switched off to minimise interference from external light sources. To improve visibility of darker areas of the fluorescence images, the contrast was enhanced by at least 75% for all images. All NR-stained observations were manually counted. The counting procedure was performed twice and the mean number was used for further analysis. The detected MP counts, MPP and MPF of both methods were analysed for significant differences. The variation in the detected MP counts was quantified using the percentage difference (% DIF), calculated according to the formula provided below. $$\:\left(\text{E}\text{q}.\:1\right)\:\:\:\:\:\:\:\text{%}\:\text{D}\text{I}\text{F}=\left(\frac{\text{H}\text{i}\text{g}\text{h}\text{e}\text{r}\:\text{M}\text{P}\:\text{c}\text{o}\text{u}\text{n}\text{t}\text{s}-\text{L}\text{o}\text{w}\text{e}\text{r}\:\text{M}\text{P}\:\text{c}\text{o}\text{u}\text{n}\text{t}\text{s}}{\text{L}\text{o}\text{w}\text{e}\text{r}\:\text{M}\text{P}\:\text{c}\text{o}\text{u}\text{n}\text{t}\text{s}}\right)\text{*}100$$ 2.4 Data analysis MS Office programs were used to visualize the data. The nonparametric Wilcoxon signed-rank test (p = 0.05) was applied to determine whether the total detected MP counts, MPP and MPF, including all selected particle sizes, differed significantly between cmRs and NRafm methods. 3. Results Out of the 100 samples analysed, the comparison between cmRs and NRafm reveals similar detection results in 8% and dissimilar detection results in 92% of the cases. Using NRafm, the total detected MP counts are 4792 ± 69 MP, with total MPP at 4625 ± 68 and total MPF at 167 ± 3. In contrast, cmRs shows a different detection performance, capturing 920 ± 19 total MP counts, with 405 ± 10 for total MPP and 515 ± 15 for total MPF. This leads to percentage differences (%DIFs, Eq. 1) between both methods ranging over two orders of magnitude for total MP counts (421%, Fig. 2 A), total MPP (1042%, Fig. 2 B) and total MPF (208%, Fig. 2 C). The distribution of detected MP based on morphological characteristics shows that NRafm yields higher observations for total MP counts and total MPP compared to cmRs, while cmRs reports higher numbers for total MPF. The nonparametric Wilcoxon signed-rank test (p = 0.05) confirmed significant differences between the methods for total MP counts and total MPP (Fig. 2 B, C). However, no significant difference was found for total MPF (Fig. 2 A). The result for MP counts across the particle size fractions reveal that the particle distributions observed with the two methods tend to show similar trends. Both methods demonstrate consistent detection patterns, with fewer MP detections observed for larger particle sizes and higher MP detections for smaller particle sizes. However, this does not apply to the detected MPF, as the number of NRafm-based detections stagnates for particle sizes 630 µm), 774 ± 24 (> 250 µm), 1417 ± 98 (> 125 µm), 874 ± 46 (> 75 µm) and 1311 ± 102 (> 25 µm). In comparison, the detected MP counts observed through cmRs are 17 ± 2 (> 630 µm), 18 ± 2 (> 250 µm), 86 ± 3 (> 125 µm), 259 ± 16 (> 75 µm) and 540 ± 34 (> 25 µm). Therefore, MP counts detected through NRafm exceed those observed by cmRs for all particle sizes. The %DIFs in MP counts per particle size between the two methods show a decreasing trend the smaller the particle size, ranging from 143% (> 25 µm) up to 4169% (> 250 µm). The nonparametric Wilcoxon signed-rank test (p = 0.05) indicates significant differences in MP counts between both methods for all particle sizes, except for particles smaller 25 µm. The morphological evaluation of NRafm-based observations (Fig. 3 B) shows consistently higher detections across all particle sizes for MPP: 416 ± 20 (> 630 µm), 753 ± 24 (> 250 µm), 1369 ± 97 (> 125 µm), 829 ± 45 (> 75 µm) and 1260 ± 99 (> 25 µm). In comparison, cmRs-based detections are continuously lower with 4 ± 1 (> 630 µm), 4 ± 1 (> 250 µm), 43 ± 3 (> 125 µm), 114 ± 8 (> 75 µm) and 240 ± 20 (> 25 µm). The %DIF in the number of detected MPP per particle size between the two methods tends to decrease the smaller the particle size ranging from 424% (> 25 µm) up to 18623% (> 250 µm). The nonparametric Wilcoxon signed-rank test (p = 0.05) indicates significant differences between both methods for MPP for all particle sizes, except for particles larger 25 µm. NFafm-based MPF detections are 0 ± 0 (> 630 µm), 21 ± 2 (> 250 µm), 49 ± 4 (> 125 µm), 46 ± 3 (> 75 µm) and 52 ± 5 (> 25 µm) in comparison to cmRs-based detection with 13 ± 1 (> 630 µm), 14 ± 2 (> 250 µm), 44 ± 2 (> 125 µm), 145 ± 15 (> 75 µm) and 299 ± 27 (> 25 µm). The %DIF of detected MPF per particle size varies between the two methods, depending on the particle size and range from 11% (> 125 µm) up to 480% (> 25 µm). For particles larger than 125 µm, detections are similar between NRafm and cmRs. However, for particles smaller than 125 µm, cmRs shows higher MPF detections compared to NRafm. The nonparametric Wilcoxon signed-rank test (p = 0.05) indicates no significant differences between both methods for MPF for all particle sizes, except for the particle size > 650 µm. 4. Discussion 4.1 Reduced organic matter removal by Fenton's Reagent increases false positive MP observations The results of the present study show disparate MP counts between NRafm and cmRs in 92 out of 100 cases and an overall %DIF of 421%, attributed to higher MP observations detected through NRafm. Stanton et al. ( 2019 ) described similar observations when comparing NRafm with detections obtained by another dye (4′,6-diamidino-2-phenylindole) that binds to biological materials. They found that the use of NRafm alone can lead to over-predictions in MP abundance ranging between 10.8% and 100%. De Guzman et al. ( 2022 ) further reported NR staining-induced over-predictions by determining the difference in the number of MP counts between NR staining and micro-FTIR spectroscopy. They calculated the overestimation as a percentage difference based on NR-stained MP counts and found numbers ranging from 17.9–686.2%, primarily attributed to undigested biological residues from Manila mussels. Furthermore, the NRafm- and cmRs-based evaluation of MP counts over five selected particle size fractions indicates that the probability of detecting false positive MP detections decreases for smaller particle sizes ( 75 µm), which can be attributed to a higher efficiency in the degradation of organic matter by the applied Fenton reagent-based protocol, as the %DIFs tend to decrease linearly between the two methods at smaller particle sizes (R² = 0.637). This aligns with the theoretical expectation that the removal efficiency of particulate organic matter using the Fenton reagent-based digestion protocol (adapted from Al Azzawi et al. (2020) with reduced exposure times to prevent particle degradation) will vary depending on the size and content of organic matter in the sample. The share of Δ (%) of detected MP counts between both methods (Fig. 4 C) shows that approximately 80% is attributed to particle sizes > 75 µm. This is primarily attribute to NR-stained false positive MP findings, as the number of detections observed through cmRs is lower compared to those obtained using NRafm (Fig. 3 A, B). These findings suggest that larger organic matter of environmental samples still limits higher accuracy of NR staining-assisted analysis of MP, despite the application of the Fenton reagent-based digestion protocol applied in this study. Regarding particle sizes smaller than 75 µm, Maw et al. ( 2022 ) demonstrated high Fenton reagent-based organic matter removal efficiency, ranging between 81.5% and 87.1%, for loamy and muddy sludge from a wastewater treatment plant, which partially covers the smaller particle sizes of the present study. In addition, consistently high organic matter removal efficiency by Fenton`s reagent applied to fine and/or suspended organic contaminants and textile dyes from wastewater has been demonstrated (Barbusiński and Filipek, 2001 ; Ebrahiem et al, 2013; Hurley et al., 2018 ; Jain et al., 2018 ; Liu et al., 2007 ; Pérez et al., 2002 ; Tagg et al., 2017 ). Smaller %DIFs in the number of detected MP counts between both methods (Fig. 4 A) for particle sizes (< 75 µm) indicate that the Fenton reagent-based digestion protocol applied in this study may achieve comparable high efficiency in removing organic matter from the samples. Concluding, we formalise that the probability of detecting organic matter-induced false positive MP counts using the methodological setup of the present study is lower for smaller particle sizes (< 75 µm). This is because, NR-stained organic residues remaining in the samples after the application of the Fenton reagent-based digestion protocol are less likely to contribute to misinterpretations in the results of MP detection results for these smaller particle sizes using NRafm. In attempting to explain the %DIFs in the number of detected MP counts between both methods across the selected particle sizes based on the types of plastic polymers detected through cmRs, no significant correlations were observed. This suggest that the MP types do not affect the NR staining-assisted detections of MPs in the current study (Fig. 5 ). 4.2 Morphological particle analysis reveals impact of Nile Red-assisted confocal microscopy to MP results quality Aside from organic residuals remained in the pre-treated samples after the application of the Fenton reagent-based digestion protocol, contributing to misinterpretations and the generation of NR-stained false positive MP detections, three methodological factors (1–3, see below) were identified that cause morphological characteristic-related %DIFs in MPP and MPF counts between both methods. The detection performance of cmRs shows similar detections for MPP and MPF counts with a %DIF of 27%. The detection performance of NRafm shows different detections for MPP and MPF counts with a %DIF 2671%, which is a remarkable gap compared to the cmRs (Fig. 2 B, C). The combination of NR staining and confocal microscopy poorly detects MPFs, as further evidenced by the stagnating number of MPF observations for particle sizes smaller than 125 µm, in contrast to MPF observations obtained using cmRs. These findings indicate that the ability to detect morphological features using the applied NR staining protocol can strongly affect the quality of the results. (1) The NR-stained method was unsuccessful in detecting hydrophobic polyacrylonitrile (PAN) fibres, which are known, along with other plastic polymers such as polyethylene terephthalate (PET), polycarbonate (PC), polyurethane (PUR) and polyvinyl chloride (PVC) to emit inherently weak fluorescence signals when analysed with NRafm (Erni-Cassola et al., 2017 ; Karakolis et al., 2019 ). No fluorescence signals were visible for PAN fibers, underscoring methodological limits in the MP detection (Fig. 6 A). The weak fluorescence of hydrophobic polymers, combined with the use of blue LED flashlights positioned around the confocal microscope, may not provide sufficient excitation energy to detect fluorescence signals effectively, as the light of blue LED flashlights is broadly scattered before reaching the sample. Karakolis et al. ( 2019 ) suggested that using alternative textile dyes, such as iDye Poly or Rit DyeMore Synthetic, could produce more intense fluorescence signals. When coupled with controlled heat application and longer exposure times (24 hours) during staining, these protocols have been shown to yield stronger fluorescence, potentially enhancing the detection of NR-stained hydrophobic polymers using the methodological setup employed in this study. (2) Inadequate depiction of NR-stained MPF resulted in a fluorescence image where isolated light spots are detected instead of a complete NR-stained fibre (Fig. 6 B). These isolated spots are likely due the accumulation of NR at the fibre ends, causing more intense light emissions compared to the rest of the fibre (e.g., Cole, 2016 ; Erni-Cassola et al., 2017 ; Karakolis et al., 2019 ). This phenomenon may lead to the misclassification of MPFs in the present study. (3) Displacements of polypropylene fibres following the dropwise application of the NR solution (Fig. 6 C), potentially leads to the transportation of MPP and MPF beyond the mapping area of the fluorescence images. This displacement could contribute to inaccuracies and misinterpretations in the detection of MP in the present study. 4.3 Smaller grain sizes lead to higher uncertainty during extrapolation Linearly extrapolating cmRs-based MP detections (< 250 µm) to the entire filter membrane introduces increasing uncertainties in the MP observations, as the area of detection decreases with smaller particle sizes and no data is available for validation outside the detection area (Fig. 7 B) (Käppler et al., 2016 ). As the particle size decreases, the ratio between the total estimated particles on the membrane and the measured particles rises exponentially (Fig. 7 A), compromising the robustness of the extrapolation. The difference between the number of estimated and measured particles spans over two orders of magnitude, particularly for particle sizes smaller than 125 µm, underscoring the impact of the increasing ratio on these fractions. Enhanced comparability of the extrapolated results can be achieved by aligning the Nile Red mapping areas with the average Raman mapping areas (Fig. 7 B). Additionally, the extended measurement times required for cmRs limits the processing of larger sample quantities. In this study, only two membranes could be analysed between 6 a.m. and 4 p.m. In contrast, the number of NR-treated membranes analysed within the same timeframe was substantially higher, emphasizing the importance of co-applying complementary MP detection techniques. Such an approach enables the processing of representative sample quantities from diverse natural systems, addressing the need for both efficiency and accuracy in MP analyses. Conclusions The study highlights that differences in MP detections between NRafm and cmRs occur in 92% of the cases. While the present dual-method MP detection approach significantly enhances sample throughput, it also highlights the need for further refinement to mitigate uncertainties and potential misinterpretations in quantitative and qualitative MP analyses. Key findings are: Particle size influences the detected percentage differences and reveal that both MP detection methods deliver better matching results at smaller particle sizes at smaller particle sizes. Detected plastic types show no significant influence on the observed percentage differences between the two methods. Challenges in degrading organic matter and reproducing the morphological characteristics of the detected MP between both methods highlight the urgent need for better organic matter removal techniques for larger objects (> 75 µm) and a better understanding of organic solvent-induced degradation of smaller MPs (< 125 µm). Overcoming the susceptibility of Nile Red staining to the detection of false positives (incompletely removed natural organics) by combining the Nile Red staining approach with Raman spectroscopy's reliability to distinguish organic material from MP, highlights the potential for cross-validation and data correction between of both techniques. Future studies should prioritize the application of multiple MP detection techniques to enhance sample throughput and data validity. The development of effective protocols for removing larger-sized organic matter from samples and understanding MP degradation caused by the exposure to organic solvents during sample preparation is a necessary step for an improved understanding of MP fate in the environment. Data validation to enhance precision and reliability is essential, particularly when applying Nile Red staining for MP detection in natural systems. Declarations Author Contribution Author contributionsSteve Utecht: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Visualization; Roles/Writing - original draft. Stefan Krause: Supervision; Writing - review & editing. Tobias Schuetz: Supervision; Writing - review & editing. Acknowledgement Acknowledgements We would like to express our gratitude to the employees of WITec (Oxford Instruments Group) for their support in conducting the measurements on the confocal micro-Raman microscope, and to the Soil Science Department of the Trier University for providing access to the Raman microscope. Our thanks also go to Mariam Selim, Leon Ludwig, Patrick Pfeiffer, and Sebastian Kleis for their assistance in the field experiments and laboratory procedures. Data Availability Data availabilityThe data used in this study are available upon request from the corresponding author. References Abusafia, A., Scheid, C., Meurer, M., Altmann, K., Dittmer, U., & Steinmetz, H. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5803470","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":403854497,"identity":"3939f120-08f1-4b2c-8b28-fee43106a7d3","order_by":0,"name":"Steve Utecht","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYJACZgYGGzDJwGAAIhIIqGcDKU5IA5GMDaRoOQxiArUwEKHF4H7zwc+FP84nbmdnYH9cUWCTz8CefAC/lmNsydIzEm4n7mxmYGw8Y5Bm2cDzDL81Bsd4zJh5gFo2HOb/2NhgcNiAQSLHgIAW/m9ALeeAWoC2NBj8B2rJ/0DIFjaglgMwLQdAtuDVwSB5LM1Ymict2RikZWaDQbIBG88z/A7jO3z44WceGzvZDecPMHxs+GNnwM+e/AC/NRiAjUT1o2AUjIJRMAqwAAApSkE6qmbXwAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Trier","correspondingAuthor":true,"prefix":"","firstName":"Steve","middleName":"","lastName":"Utecht","suffix":""},{"id":403854498,"identity":"c6053bc9-b93a-48e1-92d5-b841fe1b01b7","order_by":1,"name":"Stefan Krause","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Krause","suffix":""},{"id":403854500,"identity":"d47b64e3-92b3-480a-89c6-7422cbc3f4f4","order_by":2,"name":"Tobias Schuetz","email":"","orcid":"","institution":"University of Trier","correspondingAuthor":false,"prefix":"","firstName":"Tobias","middleName":"","lastName":"Schuetz","suffix":""}],"badges":[],"createdAt":"2025-01-10 11:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5803470/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5803470/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-025-14317-7","type":"published","date":"2025-07-21T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74236723,"identity":"b2203d58-4374-499f-b71f-22acfe5005bf","added_by":"auto","created_at":"2025-01-20 09:00:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71588,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of pre-treatment flow strategy (1-9) for the extraction of MP from environmental samples adopted in this study\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5803470/v1/6ea3c504d2be699d8e660d0a.png"},{"id":74237245,"identity":"1bcd79ab-8d92-46c4-bd6b-f56871a29997","added_by":"auto","created_at":"2025-01-20 09:08:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41326,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Nile Red-assisted fluorescence microscopy (yellow) and confocal micro-Raman spectroscopy (grey) total microplastic (MP) counts from 20 sediment cube samples (Volume\u003csub\u003eTotal\u003c/sub\u003e = 2500 ccm). (\u003cstrong\u003eB\u003c/strong\u003e) Total microplastic particles (MPP) and (\u003cstrong\u003eC\u003c/strong\u003e) total microplastic fibres (MPF) including percentage differences (%DIF) between both methods, p-value (p) derived from Wilcoxon signed-rank test (p = 0.05) using the total MP counts per sediment cube sample and the number of samples (n). Each point in the box plot represents MP counts compiled from five particle size fractions.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5803470/v1/1690c205b837ce33d213ca7a.png"},{"id":74237247,"identity":"ce80555b-2875-4c2f-a4d1-181a19546a0c","added_by":"auto","created_at":"2025-01-20 09:08:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82535,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) Nile Red-assisted fluorescence microscopy (yellow) and confocal micro-Raman spectroscopy (grey) observed microplastic (MP) counts from 20 sediment cube samples (Volume\u003c/em\u003e\u003csub\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e = 2500 ccm) across five selected particle sizes. (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) Microplastic particles (MPP) and (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) microplastic fibres (MPF) including tabular representation of the percentage differences (%DIFs) for MP, MPP and MPF counts between both methods and the p-value (p) derived from Wilcoxon signed-rank test (p = 0.05) using MP detections per sediment cube sample and particle size. Each bar graph represents 20 observations.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5803470/v1/93e1bfc0df7f2de16cecb203.png"},{"id":74236739,"identity":"feebbcfe-2aa8-4ef5-bc86-2bd0466448cb","added_by":"auto","created_at":"2025-01-20 09:00:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":298218,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Percentage differences (%DIFs) for MP, MPP and MPF counts. (\u003cstrong\u003eB\u003c/strong\u003e) Smartphone recorded fluorescence image of an NR-stained environmental sample showing NR-stained PVC and false positives caused by residues of organic matter using a blue LED flashlight and a magnifying glass equipped with a blue bandpass filter. (\u003cstrong\u003eC\u003c/strong\u003e) The share of Δ (%) distributions for MP, MPP and MPF counts across five selected particle size.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5803470/v1/866cb06b13d357cebc630e10.png"},{"id":74236733,"identity":"3d3b0752-8b0c-4325-a9cc-dcb64a7c1434","added_by":"auto","created_at":"2025-01-20 09:00:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55434,"visible":true,"origin":"","legend":"\u003cp\u003eTotal number (n = 100) of detected microplastic (MP) types for all selected particle sizes (5000 - 25 µm) qualified using cmRs.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5803470/v1/b0c7e4ff6d809600c39ea9b4.png"},{"id":74236734,"identity":"d469812b-db87-4664-9f72-70e8a1f23bec","added_by":"auto","created_at":"2025-01-20 09:00:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":555185,"visible":true,"origin":"","legend":"\u003cp\u003eConfocal microscopic bright field images (left) and confocal fluorescence microscopic images (right) of the same imaging sequence prepared for demonstrating sources of misinterpretations in the results for polyester (PET), polyacrylnitrile (PAN) and polypropylene (PP) fibres.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5803470/v1/817af4c570512340209c2b32.png"},{"id":74236731,"identity":"7932b3b8-e64f-4fd5-9a98-e3bfe2a06059","added_by":"auto","created_at":"2025-01-20 09:00:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":400955,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Exponentially increasing (R\u003csup\u003e2\u003c/sup\u003e = 0.9636) ratio between total estimated (est.) and measured (meas.) particles (PTCL). (B) Mean mapped detection area (red shaded) per particle size [µm] and the total microscopically recorded detecting area (red outlined) with 6.25 cm\u003csup\u003e2\u003c/sup\u003e. The membrane in the background (red dashed line) shows a homogenously distributed sample (\u0026gt; 125 µm) transferred by using vacuum filtration. (C) Distributed sample (\u0026gt; 250 μm) on the glass plate equipped with graph paper.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5803470/v1/4478b81eeaa96171d9b0490b.png"},{"id":87757363,"identity":"7c07c359-c6ea-446c-b3c3-f72a9ff61b21","added_by":"auto","created_at":"2025-07-28 16:10:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2551464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5803470/v1/5f666f50-ed70-43bc-9947-5b1d74e68618.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative and Qualitative Differences of Common Microplastic Detection Procedures: Nile Red- assisted Fluorescence Microscopy and Confocal Micro-Raman Spectroscopy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAlthough pollution by MP (\u0026lt;\u0026thinsp;5 mm in size) has been evidenced around the globe, there is still a critical lack of understanding the underlying fate and transport mechanisms that control MP occurrence and exposures in the environment (K\u0026auml;ppler et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tagg et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thompson et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The lack of inter-comparability of results from existing studies contributes to the absence of comprehensive monitoring programs and standardized methods for the assessment of MPs in natural systems (M\u0026uuml;ller et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Analytical techniques frequently used for quantitative and qualitative detections of MP samples such as confocal micro-Raman spectroscopy (cmRs), Fourier-transform infrared spectroscopy (FTIR) or pyrolysis-gas chromatography-mass spectrometry (pyro-GC-MS) show high detection accuracy but come at high instrumental and staffing cost (Shim et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sturm et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vaškov\u0026aacute;, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe cmRs is usually considered as a robust technique for the detection of MP offering high precision quantitative and qualitative measurements of a wide range of polymer types (Anger et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chakraborty et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dąbrowska, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sobhani et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, there is high certainty in separating organic matter detections from the data, as the obtained spectra do not coincide with plastic spectra.\u003c/p\u003e \u003cp\u003eIn the case of exclusively applying cmRs for the detection of MPs, very long measurement times severely limit high-throughput analyses required to process larger sample quantities. Other limitations include susceptibility to spectral fluorescence interference, the analytical detection limit of 1 \u0026micro;m, weak or altered signals from solid samples due to natural-related modifications and the lack of free available reference libraries (Ivleva et al., 2021; Shim et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wirnkor et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Hence, the scope of studies aiming at MP detection is often limited by the restricted number of samples that can be analysed in a reasonable timeframe (Xu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Addressing this critical limiting factor, more efficient methodological approaches that take into account simplicity in application, time and cost efficiency, as well as accuracy, reliability and repeatability for the detection of MP from natural samples are urgently needed (Abusafia et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hengstmann and Fischer, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hurley et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jain et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Prata et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Renner et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tagg et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBecause of its ease of use, the fluorescent dye Nile Red (NR) in combination with fluorescence microscopy is frequently used as an inexpensive, time-efficient and widely accessible method for the quantification of MPs (Maes et al., 2016; Meyers et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Patchaiyappan et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sancataldo et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shim et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Many pristine plastic polymer types stain reasonably well with NR across different particle sizes, ranging from a few nanometres up to several centimetres, generating high-intensity NR signals that can be efficiently detected by fluorescence microscopy (Bianco et al., 2022; Carter et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cole, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tamminga et al., 2017; Zhang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Besides that, NR-dyed samples spiked with known quantities of MP show high recovery rates, proving that NRafm is an efficient MP detection method (Pan et al., 2022; Shim et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Stanton et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, the application of NR-based staining procedures reduces measurement times and in combination with spectroscopic MP detection techniques such as cmRs, a reliably plastic polymer identified can be achieved. It should be noted that the Nile Red staining method, when used as a standalone method for detecting MP, is not recommended (Shim et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent developments in the interpretation of photographic (NR) fluorescence (pixel brightness) images are promising and support the development of semi-automatic threshold-based separations between MP and organic material (Kukkola et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sancataldo et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, photographic (NR-) fluorescence images show that organic matter and MP can emit similar light intensities indicating that the presence of organic matter in natural samples may limit the detection accuracy of MPs (Konde et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sturm et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e \u0026amp; \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, the use of organic solvents in the NR staining procedures of MPs carries the risk of overlooking organic solvent-induced morphological modifications as shown by Tamminga et al. (2017) and Kang et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for polystyrene for instance. In an unpublished experiment, we determined a weight loss of 1% after 5 minutes and 7% after 20 minutes for PMMA (d\u0026thinsp;=\u0026thinsp;2 mm) when exposed to an acetone-ethanol mixture (50:50). This limits the use of NR as a standalone tool for the detection of MP from natural systems. Both, the inadvertently labelling of residues of organic matter and potentially organic solvent-induced modifications of MPs, contribute to the generation of false positives MP detections. To perform reliable high-throughput MP analyses of natural samples, it is important to evaluate the opportunity of streamlining MP analyses by applying dual-method approaches combining the benefits of different detection techniques, which may facilitate the transition from punctual samplings to larger-scaled measurement campaigns (Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Range et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shim et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shruti et al., 2021). Besides the NRafm coupling with cmRs, other analytical techniques such as FTIR spectroscopy, flow cytometry, PLE spectroscopy, and O-PTIR spectroscopy have also been successfully combined with NRafm (Barett et al., 2020; Bianco et al., 2022; Erni-Cassola et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Konde et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Maes et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shim et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sturm et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Valine, 2019; Vermeiren et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo reliably exploit the strengths of the cmRs and the NRafm in dual-method detection approach for higher sample throughputs, this study (1) qualitatively and quantitatively evaluates the streamlining of MP detections by evaluating the percentage differences (%DIFs) of 100 environmental samples across five selected MP particle sizes (5000\u0026ndash;25 \u0026micro;m). We (2) compare the reproduction of the detected morphological features (particles or fibres) of both, NRafm and cmRs techniques to better understand methodological strengths and limitations of the presented dual-method approach. The overall ambition of this study is to contribute to the ongoing development of suitable MP detection methods for high-throughput field campaigns that can more reliably capture the nature of MP environmental fate and transport through the analysis of representative sample quantities.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Extraction of MP from river sediment samples\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of twenty frozen sediment cube samples (SCS with a volume of 125 ccm and an edge length of 5x5x5 cm; The total volume consisting of 20 SCSs corresponds to 2500 ccm) were cut from two frozen sediment cores obtained at the Ruwer, a gravel bed tributary of the river Moselle near Trier in southwest Germany. The cores were extracted using the freeze-core sampling method with liquid nitrogen as a coolant (Hauer et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Stra\u0026szlig;er et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The catchment area is located within the Rhenish Massif, which is primarily characterised by shale rock. As described below, each sample was fractionated into sub-samples encompassing five particle sizes ranging from 5000\u0026ndash;25 \u0026micro;m, which resulted in a total of 100 samples.\u003c/p\u003e \u003cp\u003eFirst, all sediment samples were placed in glass vessels and submerged in deionized water. The samples were sonicated (160 W for 20 min.) to break up sedimentary agglomerations. Particles with a diameter smaller than 25 \u0026micro;m were isolated by sieving from the samples to prevent rapid clogging of the finest sieves (25 \u0026micro;m). Next, density separation using sodium polytungstate (SPT-3) (TC-Tungsten Compounds) with a density of 1.8 g/cm\u003csup\u003e3\u003c/sup\u003e. Due to differences in sedimentation rates based on in grain size, fractions smaller than 250 \u0026micro;m were treated separately from those larger than 250 \u0026micro;m. For the \u0026gt;\u0026thinsp;250 \u0026micro;m fractions, density separation was carried by placing them in a crystallization dish (\u0026Oslash; 90 mm, 300 ml), covering the sample with SPT-3 and stirring with a spatula. Floating material was decanted and collected in a 125 \u0026micro;m sieve. This process was repeated until no visible floating material remained. The \u0026lt;\u0026thinsp;250 \u0026micro;m fractions, density separation was performed by placing them in a smaller crystallization dish (\u0026Oslash; 70 mm, 100 ml). After covering the samples with SPT-3 and stirring, the samples were left to rest in a refrigerator for 24 hours. After resting, the samples were decanted and collected in a 25 \u0026micro;m mesh sieve.\u003c/p\u003e \u003cp\u003eFollowing density separation, both \u0026lt;\u0026thinsp;250 \u0026micro;m and \u0026gt;\u0026thinsp;250 \u0026micro;m fractions were cleaned with deionized water and oven-dried at 50\u0026deg;C for 4 hours. Subsequently, the sample fractions were recombined to remove organic matter through digestion using Fenton's reagent (30% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;20 g/L FeSO\u003csub\u003e4\u003c/sub\u003e, pH\u0026thinsp;=\u0026thinsp;3) in a 500 ml beaker. Upon completion of the reaction, the sample was transferred to a 25 \u0026micro;m sieve and cleaned with deionized water to remove residual chemicals (Al-Azzawi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Campo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Frei et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hurley et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kabir et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Finally, the samples were fractionated into five selected particle sizes (\u0026gt;\u0026thinsp;630 \u0026micro;m, \u0026gt; 250 \u0026micro;m, \u0026gt; 125 \u0026micro;m, \u0026gt; 75 \u0026micro;m and \u0026gt;\u0026thinsp;25 \u0026micro;m) using sieve cascades. The sample weights were measured to estimate the number of particles on each filter membranes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Semi-automatic confocal micro-Raman spectroscopy\u003c/h2\u003e \u003cp\u003eFor the cmRs-based qualification and quantification, the particle size fractions\u0026thinsp;\u0026gt;\u0026thinsp;250 \u0026micro;m were placed centrally on a glass plate equipped with a graph paper (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) and evenly distributed by gently tapping from the underside before being spectroscopically measured. Composite microscopic imaging stacks of the samples were recorded to obtain the coordinates of the objects on the glass plate, enabling semi-automatic measurements using the Sample Raster feature in WITec Control 4.1 software.\u003c/p\u003e \u003cp\u003eThe particle size fractions\u0026thinsp;\u0026lt;\u0026thinsp;250 \u0026micro;m were homogenously transferred onto glass fibre (GF) filter membranes (Macherey-Nagel GF-2, \u0026Oslash; 45 mm, pore site 0.5 \u0026micro;m) using vacuum filtration and subsequently dried in an oven at 50\u0026deg;C. A portion of the filter membranes comprising 49.76% (2.5 cm x 2.5 cm with an area of 6.25 cm\u0026sup2;) of the total filter membrane was microscopically mapped to locate the objects (Objective: Zeiss EC Epiplan-NEOFLUAR 10x magnification) before qualitatively and quantitatively measured using cmRs (WITec alpha 300 R; Operating system: WITec Control 4.1).\u003c/p\u003e \u003cp\u003eWithin the mapped area of the filter membranes, a maximum of 400\u0026ndash;500 particles were selected, a process that took approximately 1 hour. It should be noted that the portion of the area required for selecting 400\u0026ndash;500 particles decrease relatively to the total mapped area of the filter membrane as particle size decreases. Once the maximum number of particles was reached, the portion of the area occupied by the selected particles was estimated as a percentage and extrapolated to the total filter membranes (12.56 cm\u0026sup2;) (e.g., McCormick et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; K\u0026auml;ppler et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor particle detection, a 532 nm laser with laser intensity of 4 mW was used. A single spectral scan was performed with an integration time of 10 seconds and one accumulation, resulting in measurement durations of approximately 3 hours. All recorded spectra were visually inspected and potential MP spectra were manually re-measured with variable laser intensities (0.1 to 15 mW) to improve quality for comparison with a reference library.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Nile Red application and acquisition\u003c/h2\u003e \u003cp\u003eAfter applying the cmRs, particle larger than 250 \u0026micro;m were placed in a drilled notch of a piece of aluminium holder and covered with NR solution at a concentration of 1 mg/ml (Tamminga et al., 2017) in a chloroform-acetone mixture (3:1). The NR solution was allowed to fully evaporate. Subsequently, the fluorescence-labelled particles (\u0026gt;\u0026thinsp;250 \u0026micro;m) were evenly distributed on a glass plate and illuminated with blue LED light (~\u0026thinsp;470\u0026ndash;475 nm). All NR-labelled particles were manually counted using a magnifying glass and a blue bandpass filter (orange foil) to evaluate differences in the number of MP counts between cmRs and NR staining (Konde et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Prata et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe smaller particle sizes (\u0026lt;\u0026thinsp;250 \u0026micro;m) were stained with NR directly on the filter membranes by applying the dye dropwise. Three mapping areas, measuring 0.83 cm x 0.83 cm and covering an area of 2.07 cm\u0026sup2;, were analysed. After recording using the confocal Raman microscope, the results were extrapolated to represent the total filter membrane. To optimise sample illumination, three blue light LED flashlights were positioned around the microscope. During fluorescence imaging, the objective was equipped with a blue bandpass filter attached to the 10x magnification objective. The room was darkened and the monitor was switched off to minimise interference from external light sources. To improve visibility of darker areas of the fluorescence images, the contrast was enhanced by at least 75% for all images. All NR-stained observations were manually counted. The counting procedure was performed twice and the mean number was used for further analysis.\u003c/p\u003e \u003cp\u003eThe detected MP counts, MPP and MPF of both methods were analysed for significant differences. The variation in the detected MP counts was quantified using the percentage difference (% DIF), calculated according to the formula provided below.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\left(\\text{E}\\text{q}.\\:1\\right)\\:\\:\\:\\:\\:\\:\\:\\text{%}\\:\\text{D}\\text{I}\\text{F}=\\left(\\frac{\\text{H}\\text{i}\\text{g}\\text{h}\\text{e}\\text{r}\\:\\text{M}\\text{P}\\:\\text{c}\\text{o}\\text{u}\\text{n}\\text{t}\\text{s}-\\text{L}\\text{o}\\text{w}\\text{e}\\text{r}\\:\\text{M}\\text{P}\\:\\text{c}\\text{o}\\text{u}\\text{n}\\text{t}\\text{s}}{\\text{L}\\text{o}\\text{w}\\text{e}\\text{r}\\:\\text{M}\\text{P}\\:\\text{c}\\text{o}\\text{u}\\text{n}\\text{t}\\text{s}}\\right)\\text{*}100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cp\u003eMS Office programs were used to visualize the data. The nonparametric Wilcoxon signed-rank test (p\u0026thinsp;=\u0026thinsp;0.05) was applied to determine whether the total detected MP counts, MPP and MPF, including all selected particle sizes, differed significantly between cmRs and NRafm methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eOut of the 100 samples analysed, the comparison between cmRs and NRafm reveals similar detection results in 8% and dissimilar detection results in 92% of the cases. Using NRafm, the total detected MP counts are 4792\u0026thinsp;\u0026plusmn;\u0026thinsp;69 MP, with total MPP at 4625\u0026thinsp;\u0026plusmn;\u0026thinsp;68 and total MPF at 167\u0026thinsp;\u0026plusmn;\u0026thinsp;3. In contrast, cmRs shows a different detection performance, capturing 920\u0026thinsp;\u0026plusmn;\u0026thinsp;19 total MP counts, with 405\u0026thinsp;\u0026plusmn;\u0026thinsp;10 for total MPP and 515\u0026thinsp;\u0026plusmn;\u0026thinsp;15 for total MPF. This leads to percentage differences (%DIFs, Eq.\u0026nbsp;1) between both methods ranging over two orders of magnitude for total MP counts (421%, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), total MPP (1042%, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and total MPF (208%, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The distribution of detected MP based on morphological characteristics shows that NRafm yields higher observations for total MP counts and total MPP compared to cmRs, while cmRs reports higher numbers for total MPF. The nonparametric Wilcoxon signed-rank test (p\u0026thinsp;=\u0026thinsp;0.05) confirmed significant differences between the methods for total MP counts and total MPP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C). However, no significant difference was found for total MPF (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe result for MP counts across the particle size fractions reveal that the particle distributions observed with the two methods tend to show similar trends. Both methods demonstrate consistent detection patterns, with fewer MP detections observed for larger particle sizes and higher MP detections for smaller particle sizes. However, this does not apply to the detected MPF, as the number of NRafm-based detections stagnates for particle sizes\u0026thinsp;\u0026lt;\u0026thinsp;250 \u0026micro;m.\u003c/p\u003e \u003cp\u003eThe detected MP counts per particle size (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) observed through NRafm are 416\u0026thinsp;\u0026plusmn;\u0026thinsp;20 (\u0026gt;\u0026thinsp;630 \u0026micro;m), 774\u0026thinsp;\u0026plusmn;\u0026thinsp;24 (\u0026gt;\u0026thinsp;250 \u0026micro;m), 1417\u0026thinsp;\u0026plusmn;\u0026thinsp;98 (\u0026gt;\u0026thinsp;125 \u0026micro;m), 874\u0026thinsp;\u0026plusmn;\u0026thinsp;46 (\u0026gt;\u0026thinsp;75 \u0026micro;m) and 1311\u0026thinsp;\u0026plusmn;\u0026thinsp;102 (\u0026gt;\u0026thinsp;25 \u0026micro;m). In comparison, the detected MP counts observed through cmRs are 17\u0026thinsp;\u0026plusmn;\u0026thinsp;2 (\u0026gt;\u0026thinsp;630 \u0026micro;m), 18\u0026thinsp;\u0026plusmn;\u0026thinsp;2 (\u0026gt;\u0026thinsp;250 \u0026micro;m), 86\u0026thinsp;\u0026plusmn;\u0026thinsp;3 (\u0026gt;\u0026thinsp;125 \u0026micro;m), 259\u0026thinsp;\u0026plusmn;\u0026thinsp;16 (\u0026gt;\u0026thinsp;75 \u0026micro;m) and 540\u0026thinsp;\u0026plusmn;\u0026thinsp;34 (\u0026gt;\u0026thinsp;25 \u0026micro;m). Therefore, MP counts detected through NRafm exceed those observed by cmRs for all particle sizes. The %DIFs in MP counts per particle size between the two methods show a decreasing trend the smaller the particle size, ranging from 143% (\u0026gt;\u0026thinsp;25 \u0026micro;m) up to 4169% (\u0026gt;\u0026thinsp;250 \u0026micro;m). The nonparametric Wilcoxon signed-rank test (p\u0026thinsp;=\u0026thinsp;0.05) indicates significant differences in MP counts between both methods for all particle sizes, except for particles smaller 25 \u0026micro;m.\u003c/p\u003e \u003cp\u003eThe morphological evaluation of NRafm-based observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) shows consistently higher detections across all particle sizes for MPP: 416\u0026thinsp;\u0026plusmn;\u0026thinsp;20 (\u0026gt;\u0026thinsp;630 \u0026micro;m), 753\u0026thinsp;\u0026plusmn;\u0026thinsp;24 (\u0026gt;\u0026thinsp;250 \u0026micro;m), 1369\u0026thinsp;\u0026plusmn;\u0026thinsp;97 (\u0026gt;\u0026thinsp;125 \u0026micro;m), 829\u0026thinsp;\u0026plusmn;\u0026thinsp;45 (\u0026gt;\u0026thinsp;75 \u0026micro;m) and 1260\u0026thinsp;\u0026plusmn;\u0026thinsp;99 (\u0026gt;\u0026thinsp;25 \u0026micro;m). In comparison, cmRs-based detections are continuously lower with 4\u0026thinsp;\u0026plusmn;\u0026thinsp;1 (\u0026gt;\u0026thinsp;630 \u0026micro;m), 4\u0026thinsp;\u0026plusmn;\u0026thinsp;1 (\u0026gt;\u0026thinsp;250 \u0026micro;m), 43\u0026thinsp;\u0026plusmn;\u0026thinsp;3 (\u0026gt;\u0026thinsp;125 \u0026micro;m), 114\u0026thinsp;\u0026plusmn;\u0026thinsp;8 (\u0026gt;\u0026thinsp;75 \u0026micro;m) and 240\u0026thinsp;\u0026plusmn;\u0026thinsp;20 (\u0026gt;\u0026thinsp;25 \u0026micro;m). The %DIF in the number of detected MPP per particle size between the two methods tends to decrease the smaller the particle size ranging from 424% (\u0026gt;\u0026thinsp;25 \u0026micro;m) up to 18623% (\u0026gt;\u0026thinsp;250 \u0026micro;m). The nonparametric Wilcoxon signed-rank test (p\u0026thinsp;=\u0026thinsp;0.05) indicates significant differences between both methods for MPP for all particle sizes, except for particles larger 25 \u0026micro;m.\u003c/p\u003e \u003cp\u003eNFafm-based MPF detections are 0\u0026thinsp;\u0026plusmn;\u0026thinsp;0 (\u0026gt;\u0026thinsp;630 \u0026micro;m), 21\u0026thinsp;\u0026plusmn;\u0026thinsp;2 (\u0026gt;\u0026thinsp;250 \u0026micro;m), 49\u0026thinsp;\u0026plusmn;\u0026thinsp;4 (\u0026gt;\u0026thinsp;125 \u0026micro;m), 46\u0026thinsp;\u0026plusmn;\u0026thinsp;3 (\u0026gt;\u0026thinsp;75 \u0026micro;m) and 52\u0026thinsp;\u0026plusmn;\u0026thinsp;5 (\u0026gt;\u0026thinsp;25 \u0026micro;m) in comparison to cmRs-based detection with 13\u0026thinsp;\u0026plusmn;\u0026thinsp;1 (\u0026gt;\u0026thinsp;630 \u0026micro;m), 14\u0026thinsp;\u0026plusmn;\u0026thinsp;2 (\u0026gt;\u0026thinsp;250 \u0026micro;m), 44\u0026thinsp;\u0026plusmn;\u0026thinsp;2 (\u0026gt;\u0026thinsp;125 \u0026micro;m), 145\u0026thinsp;\u0026plusmn;\u0026thinsp;15 (\u0026gt;\u0026thinsp;75 \u0026micro;m) and 299\u0026thinsp;\u0026plusmn;\u0026thinsp;27 (\u0026gt;\u0026thinsp;25 \u0026micro;m). The %DIF of detected MPF per particle size varies between the two methods, depending on the particle size and range from 11% (\u0026gt;\u0026thinsp;125 \u0026micro;m) up to 480% (\u0026gt;\u0026thinsp;25 \u0026micro;m). For particles larger than 125 \u0026micro;m, detections are similar between NRafm and cmRs. However, for particles smaller than 125 \u0026micro;m, cmRs shows higher MPF detections compared to NRafm. The nonparametric Wilcoxon signed-rank test (p\u0026thinsp;=\u0026thinsp;0.05) indicates no significant differences between both methods for MPF for all particle sizes, except for the particle size\u0026thinsp;\u0026gt;\u0026thinsp;650 \u0026micro;m.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Reduced organic matter removal by Fenton's Reagent increases false positive MP observations\u003c/h2\u003e \u003cp\u003eThe results of the present study show disparate MP counts between NRafm and cmRs in 92 out of 100 cases and an overall %DIF of 421%, attributed to higher MP observations detected through NRafm. Stanton et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) described similar observations when comparing NRafm with detections obtained by another dye (4\u0026prime;,6-diamidino-2-phenylindole) that binds to biological materials. They found that the use of NRafm alone can lead to over-predictions in MP abundance ranging between 10.8% and 100%. De Guzman et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) further reported NR staining-induced over-predictions by determining the difference in the number of MP counts between NR staining and micro-FTIR spectroscopy. They calculated the overestimation as a percentage difference based on NR-stained MP counts and found numbers ranging from 17.9\u0026ndash;686.2%, primarily attributed to undigested biological residues from Manila mussels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the NRafm- and cmRs-based evaluation of MP counts over five selected particle size fractions indicates that the probability of detecting false positive MP detections decreases for smaller particle sizes (\u0026lt;\u0026thinsp;75 \u0026micro;m) compared to larger particle sizes (\u0026gt;\u0026thinsp;75 \u0026micro;m), which can be attributed to a higher efficiency in the degradation of organic matter by the applied Fenton reagent-based protocol, as the %DIFs tend to decrease linearly between the two methods at smaller particle sizes (R\u0026sup2; = 0.637).\u003c/p\u003e \u003cp\u003eThis aligns with the theoretical expectation that the removal efficiency of particulate organic matter using the Fenton reagent-based digestion protocol (adapted from Al Azzawi et al. (2020) with reduced exposure times to prevent particle degradation) will vary depending on the size and content of organic matter in the sample. The share of Δ (%) of detected MP counts between both methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) shows that approximately 80% is attributed to particle sizes\u0026thinsp;\u0026gt;\u0026thinsp;75 \u0026micro;m. This is primarily attribute to NR-stained false positive MP findings, as the number of detections observed through cmRs is lower compared to those obtained using NRafm (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). These findings suggest that larger organic matter of environmental samples still limits higher accuracy of NR staining-assisted analysis of MP, despite the application of the Fenton reagent-based digestion protocol applied in this study.\u003c/p\u003e \u003cp\u003eRegarding particle sizes smaller than 75 \u0026micro;m, Maw et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated high Fenton reagent-based organic matter removal efficiency, ranging between 81.5% and 87.1%, for loamy and muddy sludge from a wastewater treatment plant, which partially covers the smaller particle sizes of the present study. In addition, consistently high organic matter removal efficiency by Fenton`s reagent applied to fine and/or suspended organic contaminants and textile dyes from wastewater has been demonstrated (Barbusiński and Filipek, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ebrahiem et al, 2013; Hurley et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jain et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; P\u0026eacute;rez et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Tagg et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Smaller %DIFs in the number of detected MP counts between both methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) for particle sizes (\u0026lt;\u0026thinsp;75 \u0026micro;m) indicate that the Fenton reagent-based digestion protocol applied in this study may achieve comparable high efficiency in removing organic matter from the samples.\u003c/p\u003e \u003cp\u003eConcluding, we formalise that the probability of detecting organic matter-induced false positive MP counts using the methodological setup of the present study is lower for smaller particle sizes (\u0026lt;\u0026thinsp;75 \u0026micro;m). This is because, NR-stained organic residues remaining in the samples after the application of the Fenton reagent-based digestion protocol are less likely to contribute to misinterpretations in the results of MP detection results for these smaller particle sizes using NRafm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn attempting to explain the %DIFs in the number of detected MP counts between both methods across the selected particle sizes based on the types of plastic polymers detected through cmRs, no significant correlations were observed. This suggest that the MP types do not affect the NR staining-assisted detections of MPs in the current study (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Morphological particle analysis reveals impact of Nile Red-assisted confocal microscopy to MP results quality\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAside from organic residuals remained in the pre-treated samples after the application of the Fenton reagent-based digestion protocol, contributing to misinterpretations and the generation of NR-stained false positive MP detections, three methodological factors (1\u0026ndash;3, see below) were identified that cause morphological characteristic-related %DIFs in MPP and MPF counts between both methods. The detection performance of cmRs shows similar detections for MPP and MPF counts with a %DIF of 27%. The detection performance of NRafm shows different detections for MPP and MPF counts with a %DIF 2671%, which is a remarkable gap compared to the cmRs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C). The combination of NR staining and confocal microscopy poorly detects MPFs, as further evidenced by the stagnating number of MPF observations for particle sizes smaller than 125 \u0026micro;m, in contrast to MPF observations obtained using cmRs. These findings indicate that the ability to detect morphological features using the applied NR staining protocol can strongly affect the quality of the results.\u003c/p\u003e \u003cp\u003e(1) The NR-stained method was unsuccessful in detecting hydrophobic polyacrylonitrile (PAN) fibres, which are known, along with other plastic polymers such as polyethylene terephthalate (PET), polycarbonate (PC), polyurethane (PUR) and polyvinyl chloride (PVC) to emit inherently weak fluorescence signals when analysed with NRafm (Erni-Cassola et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Karakolis et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). No fluorescence signals were visible for PAN fibers, underscoring methodological limits in the MP detection (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The weak fluorescence of hydrophobic polymers, combined with the use of blue LED flashlights positioned around the confocal microscope, may not provide sufficient excitation energy to detect fluorescence signals effectively, as the light of blue LED flashlights is broadly scattered before reaching the sample. Karakolis et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) suggested that using alternative textile dyes, such as iDye Poly or Rit DyeMore Synthetic, could produce more intense fluorescence signals. When coupled with controlled heat application and longer exposure times (24 hours) during staining, these protocols have been shown to yield stronger fluorescence, potentially enhancing the detection of NR-stained hydrophobic polymers using the methodological setup employed in this study.\u003c/p\u003e \u003cp\u003e(2) Inadequate depiction of NR-stained MPF resulted in a fluorescence image where isolated light spots are detected instead of a complete NR-stained fibre (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These isolated spots are likely due the accumulation of NR at the fibre ends, causing more intense light emissions compared to the rest of the fibre (e.g., Cole, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Erni-Cassola et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Karakolis et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This phenomenon may lead to the misclassification of MPFs in the present study.\u003c/p\u003e \u003cp\u003e(3) Displacements of polypropylene fibres following the dropwise application of the NR solution (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), potentially leads to the transportation of MPP and MPF beyond the mapping area of the fluorescence images. This displacement could contribute to inaccuracies and misinterpretations in the detection of MP in the present study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Smaller grain sizes lead to higher uncertainty during extrapolation\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLinearly extrapolating cmRs-based MP detections (\u0026lt;\u0026thinsp;250 \u0026micro;m) to the entire filter membrane introduces increasing uncertainties in the MP observations, as the area of detection decreases with smaller particle sizes and no data is available for validation outside the detection area (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB) (K\u0026auml;ppler et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As the particle size decreases, the ratio between the total estimated particles on the membrane and the measured particles rises exponentially (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), compromising the robustness of the extrapolation. The difference between the number of estimated and measured particles spans over two orders of magnitude, particularly for particle sizes smaller than 125 \u0026micro;m, underscoring the impact of the increasing ratio on these fractions. Enhanced comparability of the extrapolated results can be achieved by aligning the Nile Red mapping areas with the average Raman mapping areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eAdditionally, the extended measurement times required for cmRs limits the processing of larger sample quantities. In this study, only two membranes could be analysed between 6 a.m. and 4 p.m. In contrast, the number of NR-treated membranes analysed within the same timeframe was substantially higher, emphasizing the importance of co-applying complementary MP detection techniques. Such an approach enables the processing of representative sample quantities from diverse natural systems, addressing the need for both efficiency and accuracy in MP analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":" \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cp\u003eThe study highlights that differences in MP detections between NRafm and cmRs occur in 92% of the cases. While the present dual-method MP detection approach significantly enhances sample throughput, it also highlights the need for further refinement to mitigate uncertainties and potential misinterpretations in quantitative and qualitative MP analyses. Key findings are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eParticle size influences the detected percentage differences and reveal that both MP detection methods deliver better matching results at smaller particle sizes at smaller particle sizes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDetected plastic types show no significant influence on the observed percentage differences between the two methods.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eChallenges in degrading organic matter and reproducing the morphological characteristics of the detected MP between both methods highlight the urgent need for better organic matter removal techniques for larger objects (\u0026gt;\u0026thinsp;75 \u0026micro;m) and a better understanding of organic solvent-induced degradation of smaller MPs (\u0026lt;\u0026thinsp;125 \u0026micro;m).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOvercoming the susceptibility of Nile Red staining to the detection of false positives (incompletely removed natural organics) by combining the Nile Red staining approach with Raman spectroscopy's reliability to distinguish organic material from MP, highlights the potential for cross-validation and data correction between of both techniques.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFuture studies should prioritize the application of multiple MP detection techniques to enhance sample throughput and data validity. The development of effective protocols for removing larger-sized organic matter from samples and understanding MP degradation caused by the exposure to organic solvents during sample preparation is a necessary step for an improved understanding of MP fate in the environment. Data validation to enhance precision and reliability is essential, particularly when applying Nile Red staining for MP detection in natural systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contributionsSteve Utecht: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Visualization; Roles/Writing - original draft. Stefan Krause: Supervision; Writing - review \u0026amp; editing. Tobias Schuetz: Supervision; Writing - review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgements We would like to express our gratitude to the employees of WITec (Oxford Instruments Group) for their support in conducting the measurements on the confocal micro-Raman microscope, and to the Soil Science Department of the Trier University for providing access to the Raman microscope. Our thanks also go to Mariam Selim, Leon Ludwig, Patrick Pfeiffer, and Sebastian Kleis for their assistance in the field experiments and laboratory procedures.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData availabilityThe data used in this study are available upon request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbusafia, A., Scheid, C., Meurer, M., Altmann, K., Dittmer, U., \u0026amp; Steinmetz, H. (2023). 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Nile red staining as a subsidiary method for microplastic quantifica-tion: a comparison of three solvents and factors influencing application reliability. \u003cem\u003eSDRP Journal of Earth Sciences \u0026amp; Environmental Studies\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(2). https://doi.org/10.15436/JESES.2.2.1\u003c/li\u003e\n \u003cli\u003eThompson, R. C., Moore, C. J., Vom Saal, F. S., \u0026amp; Swan, S. H. (2009). Plastics, the environment and human health: current consensus and future trends. \u003cem\u003ePhilosophical transactions of the royal society B: biological sciences\u003c/em\u003e, \u003cem\u003e364\u003c/em\u003e(1526), 2153-2166. https://doi.org/10.1098/rstb.2009.0053\u003c/li\u003e\n \u003cli\u003eVa\u0026scaron;kov\u0026aacute;, H. (2011). A powerful tool for material identification: Raman spectroscopy. \u003cem\u003eInt. J. Math. Model. Methods Appl. Sci\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, 1205-1212. 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FTIR and Raman imaging for microplastics analysis: State of the art, challenges and prospects. \u003cem\u003eTrAC Trends in Analytical Chemistry\u003c/em\u003e, \u003cem\u003e119\u003c/em\u003e, 115629. https://doi.org/10.1016/j.trac.2019.115629\u003c/li\u003e\n \u003cli\u003eZhang, Y., Zhang, M., \u0026amp; Fan, Y. (2023). Assessment of microplastics using microfluidic approach. \u003cem\u003eEnvironmental Geochemistry and Health\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(3), 1045-1052. https://doi.org/10.1007/s10653-022-01262-4\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Microplastics, Nile Red-assisted fluorescence microscopy, Confocal micro-Raman spectroscopy, Fenton`s reagent, Data validation and correction","lastPublishedDoi":"10.21203/rs.3.rs-5803470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5803470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicroplastics (MPs) are pervasive and widespread pollutants penetrating ecosystems worldwide, including aquatic environments and sediments. The lack of standardised evaluation procedures and limited sample throughput hampers accurate assessment of global MP pollution. High-throughput analytical methods are crucial for advancing our understanding of MP cycling in the environment.\u003c/p\u003e \u003cp\u003eThis study compares MP observations by confocal micro-Raman spectroscopy and Nile Red-assisted fluorescence microscopy to evaluate their effectiveness for high-throughput MP analysis using the percentage differences between the results of the two methods. The results show the influence of particle size on the detected percentage differences and demonstrate that both methods deliver better matching results at smaller particle sizes. The overall percentage difference between the two methods is 421%, with variations ranging over three orders of magnitude depending on morphological characteristics (particles and fibres) and particle size, whereas plastic type did not significantly affect results. The combination of the Fenton reagent's limited organic matter removal and the resulting increased risk of false-positive MP detection, along with Raman spectroscopy's ability to reliably distinguish MPs from organic components, offers opportunities for data validation and correction to enhance accuracy and reliability of the results.\u003c/p\u003e \u003cp\u003eThis study contributes to the development of robust methods for high-throughput MP analysis, enabling improved spatial and temporal monitoring of its fate and transport in natural fluxes.\u003c/p\u003e","manuscriptTitle":"Quantitative and Qualitative Differences of Common Microplastic Detection Procedures: Nile Red- assisted Fluorescence Microscopy and Confocal Micro-Raman Spectroscopy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 09:00:18","doi":"10.21203/rs.3.rs-5803470/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-27T12:15:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-27T10:16:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-18T11:05:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253335363091375101031959022134552890607","date":"2025-02-07T00:22:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287956602356783331007555368951599564384","date":"2025-02-04T02:49:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-03T17:50:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-29T11:57:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-29T11:56:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-01-10T11:47:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e29bfb2c-b666-4ea4-884f-8c2f36989f76","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T16:08:22+00:00","versionOfRecord":{"articleIdentity":"rs-5803470","link":"https://doi.org/10.1007/s10661-025-14317-7","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2025-07-21 15:57:53","publishedOnDateReadable":"July 21st, 2025"},"versionCreatedAt":"2025-01-20 09:00:18","video":"","vorDoi":"10.1007/s10661-025-14317-7","vorDoiUrl":"https://doi.org/10.1007/s10661-025-14317-7","workflowStages":[]},"version":"v1","identity":"rs-5803470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5803470","identity":"rs-5803470","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}