Novel methodology to isolate microplastics from vegetal-rich samples

Herrera, Alicia; Garrido-Amador, Paloma; Martínez, Ico; Samper, María Dolores; López‐Martínez, Juan; Gómez, May; Packard, Theodore T.

doi:10.1016/j.marpolbul.2018.02.015

Cited by 103 publications

(58 citation statements)

References 55 publications

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“…Before describing the proposed computer vision-based processing of samples, it is worth preliminarily and briefly outlining the specific collection methodology adopted for this work. In the Canary Islands Archipelago, the standardized methodology in use follows the manual Guidance on Monitoring of Marine Litter in European Seas [7], slightly modified in [42]. The overall procedure for the sample preparation entails a number of steps to analyze the larger fraction of microplastic particles (1 − 5 mm).…”

Section: A Methodology For Sample Extraction and Preparationmentioning

confidence: 99%

SMACC: A System for Microplastics Automatic Counting and Classification

et al. 2020

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The management of plastic debris is a serious issue due to its durability. Unfortunately, million tons of plastic end up in the sea becoming one of the biggest current environmental problems. One way to monitor the amount of plastic in beaches is to collect samples and visually count and sort the plastic particles present in them. This is a very time-consuming task. In this work, we present a Computer Vision-based system which is able to automatically count and classify microplastic particles (1-5 mm) into five different visual classes. After cleaning a collected sample in the lab, the proposed system makes use of a pair of its images with different characteristics. The procedure includes a segmentation step, which is based on the Sauvola thresholding method, followed by a feature extraction and classification step. Different features and classifiers are evaluated as well as a deep learning approach. The system is tested on 12 different beach samples with a total of 2507 microplastic particles. The particles of each sample were manually counted and sorted by an expert. This data represents the ground truth, which is compared later with the results of the automatic processing proposals to evaluate their accuracy. The difference in the number of particles is 34 (1.4%) and the error in their classification is less than 4% for all types except for the line shapes particles. These results are obtained in less than half of the time needed by the human expert doing the same task manually. This implies that it is possible to process more than twice as many samples using the same time, allowing the biologists to monitor wider areas and more frequently than doing the process manually. INDEX TERMS Computer vision, deep learning, microplastics classification.

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Section: A Methodology For Sample Extraction and Preparationmentioning

confidence: 99%

SMACC: A System for Microplastics Automatic Counting and Classification

et al. 2020

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“…They also reported no effect on the abundance or diversity of polymers able to be recovered. Similarly, Herrera et al (2018) utilized EtOH for the separation of MPs from vegetal material with no effect on the recovered polymers. In the current study, recovery of rayon and PE from spiked farmed barramundi tissue was 93.3%.…”

Section: Discussionmentioning

confidence: 99%

“…Digestion periods typically range from 24 hr (Dehaut et al, 2016) up to 2-3 weeks (Foekema et al, 2013;Munno et al, 2018), with tested reaction temperatures ranging from 18 • C (Kühn et al, 2017) through to 60 • C (Dehaut et al, 2016;Karami et al, 2017;Munno et al, 2018). If significant indigestible material remains following the KOH digestion step, the solution can be further clarified with a density separation (Coppock et al, 2017;Karami et al, 2017;Herrera et al, 2018). The solution is then passed through a suitable pore-sized filter to isolate the intact putative MPs for subsequent physical and chemical analysis (Hermsen et al, 2018;Lusher and Hernandez-Milian, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…In this study, two digestion methods, one using room temperature (Foekema et al, 2013) and the other using 40 • C heating (Karami et al, 2017), were optimized for lipid-rich fish tissue. Ethanol (EtOH) is a versatile polar solvent capable of dissolving polar (due to its hydroxyl group) and many non-polar (due to its ethyl group) substances, but has been shown to have minimal impact on MPs (Courtene-Jones et al, 2017;Herrera et al, 2018). Therefore, a simple treatment with EtOH post-KOH digestion was investigated as a means of dispersing and redissolving the resultant gelatinous soap suspension.…”

Section: Introductionmentioning

confidence: 99%

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Solving a Sticky Situation: Microplastic Analysis of Lipid-Rich Tissue

Dawson

Motti

Kroon

2020

Front. Environ. Sci.

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Given current concerns regarding the extent of microplastic contamination in the environment, routine monitoring for microplastics in biological tissues is becoming increasingly common place. However, complex sample matrices, such as lipid-rich tissues, require multiple pre-treatment steps which may lead to increased sample processing time and costs, and a reduction in microplastic recovery rates thereby hindering monitoring efforts. Lipid-rich (fat) tissues often pose difficulties for traditional potassium hydroxide (KOH) digestion methods due to saponification. This reaction produces a suspension of glycerol and fatty acids (soaps), which may entrap microplastics inhibiting their recovery and clog filters thus reducing the efficiency of the filtration or inhibiting it altogether. In this study, the incorporation of 100% ethanol (EtOH) to existing KOH digestion methods was found to completely redissolve the viscous saponified gel formed in these reactions, with a digestion efficiency greater than 97% for all treated lipid-rich tissue samples. Recovery of spiked polyethylene and polystyrene fragments, and rayon and polyester fibers, ranged from 93% to 100%. The addition of EtOH did not induce physical or chemical degradation on these polymers. The inclusion of an ad hoc decision-making tool within the digestion workflow reduced pre-processing time for samples and allowed for solid saponified samples to be completely redissolved. This validated workflow facilitates high through-put sampling of biota, by enabling lipidrich tissues to be filtered with a high degree of efficiency thereby successfully separating microplastics from their gelatinous matrix.

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“…Among this production, 61.8 million pertain only to Europe, another 64.6 million to North America and about 14.4 million to South America 1 . Even though plastics have demonstrated to be a versatile, durable and low‐cost material, the consequences of their consumption are the production of a lot of waste 2 . Until now 8300 million metric tons of plastics have been produced in the world since its beginnings 3 .…”

Section: Introductionmentioning

confidence: 99%

Ionizing radiation as adjuvant for the abiotic degradation of plastic bags containing pro‐oxidant additives

Aldás

Valle

Aguilar

et al. 2020

J of Applied Polymer Sci

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The effects of ionizing radiation in promoting the abiotic degradation mechanisms in oxo‐degradable plastic bags were studied. Commercial plastic bags, containing pro‐oxidant additives, were irradiated with gamma photons and accelerated electrons at doses of 5 and 10 kGy. Then, the irradiated bags were exposed to abiotic degradation, either thermal aging or accelerated weathering. Mechanical and microstructural characterizations were performed to follow the degradation. Plastic bags containing additives did not show important changes in their structure after ionizing radiation and thermal aging. Meanwhile, accelerated weathering combined with gamma photons irradiation produce fragmentation of all the samples after 120 h of degradation. It was concluded that the combination of ionizing radiation and accelerated weathering allows the breakage of polyethylene chains trough accelerating the activation of pro‐oxidant additive. Additionally, polyethylene molecules are degraded with pre‐treatment of ionizing radiation even if they do not contain the pro‐oxidant additive.

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Novel methodology to isolate microplastics from vegetal-rich samples

Cited by 103 publications

References 55 publications

SMACC: A System for Microplastics Automatic Counting and Classification

SMACC: A System for Microplastics Automatic Counting and Classification

Solving a Sticky Situation: Microplastic Analysis of Lipid-Rich Tissue

Ionizing radiation as adjuvant for the abiotic degradation of plastic bags containing pro‐oxidant additives

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