Abstract:Raman spectral libraries specific to microplastics demonstrated improved spectral matching results when weathered plastics and a variety of particle colors and morphologies were included. Here, we explore if this is true for Fourier transform infrared (FTIR) spectroscopy as well. We present two novel databases specific to microplastics using attenuated total reflection (μATR-FTIR): (1) an FTIR library of plastic particles (FLOPP), containing 186 spectra from common plastic items, across 14 polymer types and (2… Show more
“…The spectra obtained from the randomly chosen microplastics is illustrated in Figure 5. FTIR-ATR analyses indicated that the microplastics found are made of two main polymers due to the correlation matching ratio with the standard spectrum from De Frond et al (2021): Polyester ([polyethylene terephthalate, PET] :1714,1243,1097,726 cm −1 ) with a correlation rate between .91 and .96, and Polyethylene-vinyl acetate (PEVA:2243,1737,1452,1238 cm −1 ) with a correlation rate around .89 (Figure 5).…”
In northern Peru, the mangrove crab Ucides occidentalis is of great importance due to its ecological, economic, and social role. In this study, we reported for the first time the presence of microplastics in the gills and digestive tract of the mangrove crab U. occidentalis derived from local markets in Tumbes. Microplastics were identified in 100% of the crabs analyzed with a total of 921 items, 475 items (52.57%) found in the gills, and 446 (48.43%) found in the digestive tract. The size range was established in 2 to 250 µm, 250 to 500 µm, 500 to 1 mm, and 1 to 5 mm, microplastics with sizes between 2 and 250 µm were the most common with 53.79% in the digestive tract and 90% in the gills. A total of six different types of microplastic were recorded; The highest percentages for each tissue were fibers (59.64%–61.05%) and films (19.28%−36.63%), with clear fibers being the most prevalent microplastic type in both tissues. Microplastics with less than 250 µm size were found 90% in the gills and 53.79% in the crab digestive tract. Although the present study is a baseline for rapid identification of microplastics in mangrove crab, we suggested that these findings provided more information on the state of contamination as well as food security alert for local markets.
“…The spectra obtained from the randomly chosen microplastics is illustrated in Figure 5. FTIR-ATR analyses indicated that the microplastics found are made of two main polymers due to the correlation matching ratio with the standard spectrum from De Frond et al (2021): Polyester ([polyethylene terephthalate, PET] :1714,1243,1097,726 cm −1 ) with a correlation rate between .91 and .96, and Polyethylene-vinyl acetate (PEVA:2243,1737,1452,1238 cm −1 ) with a correlation rate around .89 (Figure 5).…”
In northern Peru, the mangrove crab Ucides occidentalis is of great importance due to its ecological, economic, and social role. In this study, we reported for the first time the presence of microplastics in the gills and digestive tract of the mangrove crab U. occidentalis derived from local markets in Tumbes. Microplastics were identified in 100% of the crabs analyzed with a total of 921 items, 475 items (52.57%) found in the gills, and 446 (48.43%) found in the digestive tract. The size range was established in 2 to 250 µm, 250 to 500 µm, 500 to 1 mm, and 1 to 5 mm, microplastics with sizes between 2 and 250 µm were the most common with 53.79% in the digestive tract and 90% in the gills. A total of six different types of microplastic were recorded; The highest percentages for each tissue were fibers (59.64%–61.05%) and films (19.28%−36.63%), with clear fibers being the most prevalent microplastic type in both tissues. Microplastics with less than 250 µm size were found 90% in the gills and 53.79% in the crab digestive tract. Although the present study is a baseline for rapid identification of microplastics in mangrove crab, we suggested that these findings provided more information on the state of contamination as well as food security alert for local markets.
“…Each spectrum was acquired from 4000 to 600 cm −1 with a resolution of 4 cm −1 , 25 acquisitions being performed for each item. Particles were identified by comparing spectra and Flopp/Flopp-e databases [6] . Fig.…”
Section: Preparation Of In-house Microplastics Fragmentsmentioning
“…Signal intensities change, and additional peaks appear due to the formation of new functional groups (e.g., hydroxyl) in the polymers . This problem can be addressed by adding spectra of weathered polymers to the database. , However, it is also important to quantify the minimal number of samples required by numeric approaches to distinguished polymers …”
Section: Introductionmentioning
confidence: 99%
“…12 This problem can be addressed by adding spectra of weathered polymers to the database. 13,14 However, it is also important to quantify the minimal number of samples required by numeric approaches to distinguished polymers. 15 Machine learning (ML) models, especially deep neural network (DNN) models, have gained popularity and proven their efficacy in identifying the properties of microplastics, 16−20 minerals, 21−24 and organic samples.…”
Identifying environmental polymers and microplastics is crucial for the scientific world, environmental agencies, and water authorities to estimate their environmental impact and increase efforts to decrease emissions. On the basis of different spectroscopy techniques, e.g., laser-directed infrared imaging and Raman spectroscopy, polymers can be observed and represented as spectroscopic signals. The latter can be further analyzed and classified by data science, in particular, machine learning (ML). Past studies applied a variety of ML models to identify polymers from small or large data sets. However, a comprehensive comparison of multiple models across different data set sizes is still needed, which is presented in this study. Furthermore, we also provide a practical data augmentation technique to generate synthetic samples when only a limited number of samples are available. Our results show that the ensemble ML model, compared to neural network models, takes the least training time to achieve the best performance, i.e., a classification accuracy of 99.5%. This study provides a generic framework for selecting ML models and boosting model performance to accurately identify polymers.
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