Identification and quantification of microplastics in table sea salts using micro-NIR imaging methods

Zhang, Jixiong; Tian, Kuangda; Lei, Chunli; Min, Shungeng

doi:10.1039/c8ay00125a

Cited by 32 publications

(7 citation statements)

References 36 publications

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“…Focal Plane Array detectors (FPA) and FTIR microscopy, combined with a commercial image analysis software, are used by [31]. The presence of microplastics in an abiotic product, namely table sea salt, is studied by [33]. The authors developed an approach that reproduces eye counting results capturing micro-FT-NIR images and identifying Polyethylene Terephthalate (PET) microplastics using spectral similarity methods.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

SMACC: A System for Microplastics Automatic Counting and Classification

et al. 2020

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“…A microplastic analysis method using near IR (NIR) spectroscopy has also been tried. FTIR analyzes the spectrum between 600 and 4000 cm −1 , but NIR analyzes the spectrum over 4000-15,000 cm −1 [78]. NIR spectra are generally analyzed based on vibration combinations of molecular vibrations of X-H, such as C-H, O-H, and N-H.…”

Section: Near Ir Spectra Analysis Methodsmentioning

confidence: 99%

Methods of Analyzing Microsized Plastics in the Environment

et al. 2021

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Microplastics are found in various environments with the increasing use of plastics worldwide. Several methods have been developed for the sampling, extraction, purification, identification, and quantification of microplastics in complex environmental matrices. This study intends to summarize recent research trends on the subject. Large microplastic particles can be sorted manually and identified through chemical analysis; however, sample preparation for small microplastic analysis is usually more difficult. Microplastics are identified by evaluating the physical and chemical properties of plastic particles separated through extraction and washing steps from a mixture of inorganic and organic particles. This identification has a high risk of producing false-positive and false-negative results in the analysis of small microplastics. Currently, a combination of physical (e.g., microscopy), chemical (e.g., spectroscopy), and thermal analyses is widely used. We aim to summarize the best strategies for microplastic analysis by comparing the strengths and limitations of each identification method.

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“…30,31 The most frequently employed methodology is based on spectroscopic measurements that provide information on the specific chemical bonds of particles: FT-IR, 17,32,33 μRaman, 20,34,35 and μFT-NIR near-infrared spectroscopy. 36 Actually, most of the mentioned spectroscopic techniques, despite their high specificity, are performed with dried samples after one or more cumbersome filtration steps. Such pre-processing has some drawbacks such as the overlapping of samples on the filters, the clogging of the filters with consequent partial loss of the sample and, above all, the contamination from the environment.…”

Section: ■ Introductionmentioning

confidence: 99%

“…At present, there is no standard method to characterize MPs, so several approaches and techniques, ranging from visual identification to μFT-IR analyses, have been proposed. Analytical procedures for MPs in environmental samples consist of extraction, isolation, purification, identification, and quantification. − Samples can be separated by densities , or filtered through different sieves, and the quantification of MPs can be performed by the gravimetric method. ,− Generally, using microscopy provides a detailed surface texture, the use of scanning electronic microscopy (SEM) gives extremely clear and high magnification images of plastic particles, and energy-dispersive X-ray spectroscopy (EDS) offers the elemental composition. , Some destructive analytical techniques, such as pyrolysis-gas, chromatography-mass spectrometry, and liquid chromatography coupled with high-resolution mass spectrometry, are also used to assess the chemical composition of MP particles by analyzing their thermal degradation products. − Thermo-analytical analysis, that is, differential scanning calorimetry (DSC) and thermal gravimetric analysis (TGA), which measures changes in the physical and chemical properties of polymers depending on their thermal stability, has been used for identification. , The most frequently employed methodology is based on spectroscopic measurements that provide information on the specific chemical bonds of particles: FT-IR, ,, μRaman, ,, and μFT-NIR near-infrared spectroscopy . Actually, most of the mentioned spectroscopic techniques, despite their high specificity, are performed with dried samples after one or more cumbersome filtration steps.…”

Section: Introductionmentioning

confidence: 99%

Toward an All-Optical Fingerprint of Synthetic and Natural Microplastic Fibers by Polarization-Sensitive Holographic Microscopy

et al. 2022

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Microplastic fibers from synthetic textiles have been indicated as a major source of pollution because millions of fibers are released into the environment by laundry washing. In fact, such types of microplastics usually bypass wastewater treatment processes and filters, thus reaching the oceans and other water natural reservoirs in huge quantities. Nowadays, several approaches are available for characterizing microplastics, but unfortunately, there is no unique and standard method for this aim. Among the various methodologies, several microscopy techniques such as optical microscopy (OM), scanning electron microscopy coupled with energy dispersive X-ray (SEM−EDX) analysis, or OM combined with molecular spectroscopy can be used for visual detection and measurements. Our proposal is a non-destructive method based on a digital holographic microscope in a configuration fully sensitive to the polarization of light transmitted by the fibers with a micron dimension. Our aim is to prove that the proposed approach allows for the precise characterization of synthetic and natural fibers in water. The method exploits all the advantages of digital holography such as numerical refocusing, non-invasive testing, and quantitative measurements of the complex wave field by adding access to the polarization of light that conveys meaningful information about materials. We intend to show that a unique all-optical fingerprint can be retrieved using the Jones-matrix formalism for the major classes of synthetic microfilaments used in the textile industry (i.e., polyamide 6, polyamide 6.6, polypropylene, and polyester) and for the most common natural fibers (i.e., wool and cotton) prepared according to previously developed appropriate protocols. Our results show that the proposed technology identifies new features for micron-sized fibers based on optical anisotropy through quantitative digital holography that could open future routes for automatic and all-optical identification of textile contaminants in water.

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Identification and quantification of microplastics in table sea salts using micro-NIR imaging methods

Abstract: A method for the identification and quantification of microplastics in table sea salts. The method meets the need for detecting and automatic counting microplastics simultaneously.

Cited by 32 publications

References 36 publications

SMACC: A System for Microplastics Automatic Counting and Classification

SMACC: A System for Microplastics Automatic Counting and Classification

Methods of Analyzing Microsized Plastics in the Environment

Toward an All-Optical Fingerprint of Synthetic and Natural Microplastic Fibers by Polarization-Sensitive Holographic Microscopy

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