Literature DB >> 32120106

Major factors influencing the quantification of Nile Red stained microplastics and improved automatic quantification (MP-VAT 2.0).

Joana C Prata1, Jorge R Alves2, João P da Costa3, Armando C Duarte4, Teresa Rocha-Santos5.   

Abstract

Automated count of Nile Red fluorescent microplastics allows fast and reliable quantification. However, factors involving staining, digital camera conditions and settings introduce variability to the results. The objective of this paper is to identify and propose solutions to these factors and improve on the previous MP-VAT script. While removal of digital sensor defects had little influence on results and staining can be reduced to 5 min, Nile Red concentrations cannot be reduced <0.01 mg mL-1, the 470 nm LED lantern emission must be >1600 lx, and photographic conditions should be maintained as stable as possible ideally improving the filter membrane area and using the recommended settings of 2 s, ISO100, F5.6. It was also found that Nile Red can be removed from microplastics using acetone or hydrogen peroxide with iron. More importantly, both particles and fluorescent are lost with time and thus quantification should be conducted within a week. Finally, MP-VAT 2.0 was developed to remove unselected areas and to identify only red particles, excluding white reflections from quantification. This updated version of MP-VAT produced improved recovery rates of 98.2 ± 6.9 for spiked samples and 95.9 ± 10.3 on actual environmental samples, presenting a cheap and reliable complementary method for microplastic identification.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Digital camera sensors; MP-VAT; Microplastics samples; Nile Red; Staining dyes

Year:  2020        PMID: 32120106     DOI: 10.1016/j.scitotenv.2020.137498

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  5 in total

Review 1.  Photoluminescence-Based Techniques for the Detection of Micro- and Nanoplastics.

Authors:  Chiara Capolungo; Damiano Genovese; Marco Montalti; Enrico Rampazzo; Nelsi Zaccheroni; Luca Prodi
Journal:  Chemistry       Date:  2021-10-21       Impact factor: 5.020

2.  A Low-Cost Microfluidic Method for Microplastics Identification: Towards Continuous Recognition.

Authors:  Pedro Mesquita; Liyuan Gong; Yang Lin
Journal:  Micromachines (Basel)       Date:  2022-03-23       Impact factor: 3.523

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

Authors:  Ho-Min Park; Sanghyeon Park; Maria Krishna de Guzman; Ji Yeon Baek; Tanja Cirkovic Velickovic; Arnout Van Messem; Wesley De Neve
Journal:  PLoS One       Date:  2022-06-15       Impact factor: 3.752

4.  Microplastics in Internal Tissues of Companion Animals from Urban Environments.

Authors:  Joana C Prata; Ana L Patrício Silva; João P da Costa; Patrícia Dias-Pereira; Alexandre Carvalho; António José Silva Fernandes; Florinda Mendes da Costa; Armando C Duarte; Teresa Rocha-Santos
Journal:  Animals (Basel)       Date:  2022-08-04       Impact factor: 3.231

5.  Identification of microplastics using 4-dimethylamino-4'-nitrostilbene solvatochromic fluorescence.

Authors:  Giuseppe Sancataldo; Vittorio Ferrara; Francesco Paolo Bonomo; Delia Francesca Chillura Martino; Mariano Licciardi; Bruno Giuseppe Pignataro; Valeria Vetri
Journal:  Microsc Res Tech       Date:  2021-05-28       Impact factor: 2.893

  5 in total

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