Analyzing microplastics with Nile Red: Emerging trends, challenges, and prospects

Shruti, V.C.; Pérez-Guevara, Fermín; Roy, Priyadarsi D.; Kutralam-Muniasamy, Gurusamy

doi:10.1016/j.jhazmat.2021.127171

Cited by 133 publications

(75 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Fluorescent tagging of MP using Nile red (NR) has gained widespread use because of its inexpensive nature and its affinity to a wide range of polymers (Prata et al, 2021). In addition, smaller MP particles (≥3 μm) can be easily detected and visualized due to the high fluorescence intensity of the dye (Shruti et al, 2022). With the advancement in image processing technology, a few tools for automated measurement of number and size of stained MP are now available (Prata et al, 2019(Prata et al, , 2020.…”

Section: Introductionmentioning

confidence: 99%

Comparative profiling and exposure assessment of microplastics in differently sized Manila clams from South Korea by μFTIR and Nile Red staining

Guzman

Andjelković

Jovanović

et al. 2022

Marine Pollution Bulletin

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Comparative profiling and exposure assessment of microplastics in differently sized Manila clams from South Korea by μFTIR and Nile Red staining

Guzman

Andjelković

Jovanović

et al. 2022

Marine Pollution Bulletin

View full text Add to dashboard Cite

“…The aforementioned staining technique relies on the affinity of Nile red to different plastic polymers [ 15 ]. Indeed, thanks to the fluorescent nature of the dye, only a fluorescence microscope is needed to observe the MP, after which images can be captured with a camera, and where these images can then be analyzed to quantify and characterize the MP present in the sample at hand.…”

Section: Related Workmentioning

confidence: 99%

MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams

Park

Guzman

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

Environmental monitoring of microplastics (MP) contamination has become an area of great research interest, given potential hazards associated with human ingestion of MP. In this context, determination of MP concentration is essential. However, cheap, rapid, and accurate quantification of MP remains a challenge to this date. This study proposes a deep learning-based image segmentation method that properly distinguishes fluorescent MP from other elements in a given microscopy image. A total of nine different deep learning models, six of which are based on U-Net, were investigated. These models were trained using at least 20,000 patches sampled from 99 fluorescence microscopy images of MP and their corresponding binary masks. MP-Net, which is derived from U-Net, was found to be the best performing model, exhibiting the highest mean F1-score (0.736) and mean IoU value (0.617). Test-time augmentation (using brightness, contrast, and HSV) was applied to MP-Net for robust learning. However, compared to the results obtained without augmentation, no clear improvement in predictive performance could be observed. Recovery assessment for both spiked and real images showed that, compared to already existing tools for MP quantification, the MP quantities predicted by MP-Net are those closest to the ground truth. This observation suggests that MP-Net allows creating masks that more accurately reflect the quantitative presence of fluorescent MP in microscopy images. Finally, MAP (Microplastics Annotation Package) is introduced, an integrated software environment for automated MP quantification, offering support for MP-Net, already existing MP analysis tools like MP-VAT, manual annotation, and model fine-tuning.

show abstract

“…It effectively dyes the surface of microplastics and represents a novel method for tracking microplastics with fluorescent markers, providing a rapid means for evaluating the abundance of microplastics [ 26 , 27 ]. However, the staining effect depends on factors such as the dye concentration, solvent type, and temperature, and the subsequent changes in fluorescence intensity may affect the repeatability of detection [ 28 ]. Uncontrollable technical deviations are introduced during the plastic measurement process itself or during environmental testing of the sample [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

A Rapid Method for Detecting Microplastics Based on Fluorescence Lifetime Imaging Technology (FLIM)

Fang

Wang

et al. 2022

Toxics

View full text Add to dashboard Cite

With the increasing use and release of plastic products, microplastics have rapidly accumulated in ecological environments. When microplastics enter the food chain, they cause serious harm to organisms and humans. Microplastics pollution has become a growing concern worldwide; however, there is still no standardized method for rapidly and accurately detecting microplastics. In this work, we used fluorescence lifetime imaging technology to detect four kinds of Nile red-stained and unstained microplastics, and the unique phasor fingerprints of different microplastics were obtained by phasor analysis. Tracing the corresponding pixels of the “fingerprint” in the fluorescence lifetime image allowed for the quick and intuitive identification of different microplastics and their location distributions in a mixed sample. In our work, compared with staining the four microplastics with a fluorescent dye, using the phasor “fingerprint library” formed by the autofluorescence lifetimes of the microplastics was more easily distinguished than microplastics in the mixed samples. The feasibility of this method was further tested by adding three single substances—SiO2, chitin and decabromodiphenyl ethane (DBDPE), and surface sediments to simulate interferent in the environment, and the results providing potential applications for the identification and analysis of microplastics in complex environments.

show abstract

Analyzing microplastics with Nile Red: Emerging trends, challenges, and prospects

Cited by 133 publications

References 89 publications

Comparative profiling and exposure assessment of microplastics in differently sized Manila clams from South Korea by μFTIR and Nile Red staining

Comparative profiling and exposure assessment of microplastics in differently sized Manila clams from South Korea by μFTIR and Nile Red staining

MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams

A Rapid Method for Detecting Microplastics Based on Fluorescence Lifetime Imaging Technology (FLIM)

Contact Info

Product

Resources

About