Extraction and detection methods of microplastics in food and marine systems: A critical review

Sridhar, Adithya; Kannan, D.; Kapoor, Ashish; Sivaraman, P.

doi:10.1016/j.chemosphere.2021.131653

Cited by 77 publications

(16 citation statements)

References 117 publications

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“…In soil, MP contamination was detected using a convolutional neural network (CNN) model that works with ultra violet—near infrared spectra (UV-NIR) [ 13 ]. However, the collection of spectral data is time-consuming and expensive, mostly due to the high cost of specialized equipment and the need for a trained operator [ 14 ]. Because of these constraints, Nile red staining became an attractive alternative for MP visualization and detection.…”

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

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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.

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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

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“…Raman spectroscopy is one of the most widely used techniques for the identification of microplastics, and it has been applied in different types of samples, such as environmental sand samples, [ 103 ] seawater, [ 104 ] freshwater turtle population, [ 105 ] food, [ 106 ] and seafood species such as the green‐lipped mussel Perna viridis. [ 107 ] Furthermore, microplastics have been identified in glaciers of pristine areas in Iceland by using µ‐Raman.…”

Section: Micro‐ and Nanoplastics Detection Using Raman And Sersmentioning

confidence: 99%

Latest Advances and Developments to Detection of Micro‐ and Nanoplastics Using Surface‐Enhanced Raman Spectroscopy

Vélez‐Escamilla

Contreras‐Torres

2022

Part & Part Syst Charact

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The increasing demand for single‐use plastic‐based products worldwide has generated immense waste during the last decade. This review aims to summarize the current developments of surface‐enhanced Raman spectroscopy (SERS) for detecting micro‐ and nanoplastics in a variety of samples and environments. Despite the SERS technique being very recent for this purpose, its robustness has already been discussed in a few analyses focused on well‐known pollutants such as phthalates, plasticizers, and xenobiotic contaminants from commercially available water bottles. Here, the latest advances, obstacles, and perspectives are reviewed using SERS detection as a robust alternative for analyzing complex samples containing nanoplastic particles present in daily consumer products such as wine and vegetables. Moreover, this paper describes different SERS substrates developed to overcome the limitations for identifying polymer particles at low concentrations. Factors contributing to the sensitivity of SERS substrates are discussed to show the advantages and limitations of this technique. The broader role of SERS as a tool in environmental research is currently explored from polluted air and aquatic environments, which can be relevant for other fields, such as clinical monitoring and nanotoxicology.

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“…It is generally acknowledged that Fourier transform infrared (FTIR) and RS are complementary vibrational techniques. Studies have shown that both techniques are suitable for the identification of marine microplastics in the environment, especially the combination of both methods is more comprehensive for the identification and characterization of colored particles (Kappler et al., 2016; Sridhar et al., 2022).…”

Section: Applications Of Rs In Fish Quality and Safetymentioning

confidence: 99%

Recent advances in the application of Raman spectroscopy for fish quality and safety analysis

et al. 2022

Comp Rev Food Sci Food Safe

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Fish is one of the highly demanded aquatic products, and its quality and safety play a pivotal role in daily diet. However, the possible hazardous substance in perishable fish both in pre-and postharvest periods may decrease their values and pose a threat to public health. Laborious and expensive traditional methods drive the need of developing effective tools for detecting fish quality and safety properties in a rapid, nondestructive, and effective manner. Recent advances in Raman spectroscopy (RS) and surface-enhanced Raman scattering (SERS) have shown enormous potential in various aspects, which largely boost their applications in fish quality and safety evaluation. They have incomparable merits such as providing molecule fingerprint information and allowing for rapid, sensitive, and noninvasive detection with simple sample preparation. This review provides a comprehensive overview focusing on the applications of RS and SERS for fish quality assessment and safety inspection, highlighting the hazardous substance and illegal behavior both in preharvest (veterinary drug residues and environmental pollutants) and postharvest (freshness and illegal behavior) particularly. Moreover, challenges and prospects are also proposed to facilitate the vigorous development of RS and SERS. This review is aimed to emphasize potential opportunities for applying RS and SERS as promising techniques for routine food quality and safety detection.

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Extraction and detection methods of microplastics in food and marine systems: A critical review

Cited by 77 publications

References 117 publications

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

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

Latest Advances and Developments to Detection of Micro‐ and Nanoplastics Using Surface‐Enhanced Raman Spectroscopy

Recent advances in the application of Raman spectroscopy for fish quality and safety analysis

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