Automated analysis of microplastics based on vibrational spectroscopy: are we measuring the same metrics?

Dong, Mingtan; She, Zhenbing; Xiong, Xiong; Ouyang, Guang; Luo, Zun-Chang

doi:10.1007/s00216-022-03951-6

Cited by 42 publications

(27 citation statements)

References 59 publications

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“…Another approach couples mid-IR QCLs with a thermoelectrically-cooled single-channel detector for rapid chemical imaging with a fully automated workflow. This approach has been compared with FPA-FTIR imaging for microplastic detection 15 . As these FTIR and emerging QCL-based systems detect directly the transmitted or reflected IR radiation of the sample, they can be called direct IR approaches that suffer—besides the above-described resolution in the micrometer range – from Mie scattering and anomalous dispersion effects, in particular for sample sizes near the IR wavelengths 12 to 5.5 µm corresponding to the most informative spectral fingerprint range 800 to 1800 cm −1 .…”

Section: Introductionmentioning

confidence: 99%

Optical photothermal infrared spectroscopy with simultaneously acquired Raman spectroscopy for two-dimensional microplastic identification

Böke

Krafft

2022

Sci Rep

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In recent years, vibrational spectroscopic techniques based on Fourier transform infrared (FTIR) or Raman microspectroscopy have been suggested to fulfill the unmet need for microplastic particle detection and identification. Inter-system comparison of spectra from reference polymers enables assessing the reproducibility between instruments and advantages of emerging quantum cascade laser-based optical photothermal infrared (O-PTIR) spectroscopy. In our work, IR and Raman spectra of nine plastics, namely polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, polycarbonate, polystyrene, silicone, polylactide acid and polymethylmethacrylate were simultaneously acquired using an O-PTIR microscope in non-contact, reflection mode. Comprehensive band assignments were presented. We determined the agreement of O-PTIR with standalone attenuated total reflection FTIR and Raman spectrometers based on the hit quality index (HQI) and introduced a two-dimensional identification (2D-HQI) approach using both Raman- and IR-HQIs. Finally, microplastic particles were prepared as test samples from known materials by wet grinding, O-PTIR data were collected and subjected to the 2D-HQI identification approach. We concluded that this framework offers improved material identification of microplastic particles in environmental, nutritious and biological matrices.

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Section: Introductionmentioning

confidence: 99%

Optical photothermal infrared spectroscopy with simultaneously acquired Raman spectroscopy for two-dimensional microplastic identification

Böke

Krafft

2022

Sci Rep

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“…Fourier transform IR (FTIR), including attenuated total reflection-FTIR (ATR-FTIR) and micro-FTIR, can detect MPs above 10 μm, while Raman can detect MPs above 1 μm. Moreover, focal plane array-FTIR imaging can effectively detect the MPs larger than 20 μm [ 30 ], while Raman imaging can effectively identify MPs larger than 1 μm [ 31 ]. In addition to the inherent chemical information, Raman and IR spectroscopy combined with photothermal techniques provided high-resolution imaging for submicron-scale organic particle determination.…”

Section: Introductionmentioning

confidence: 99%

Identification and Evaluation of Microplastics from Tea Filter Bags Based on Raman Imaging

Mei

Wang

Xiao

et al. 2022

Foods

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Microplastic (MP) contamination is a public issue for the environment and for human health. Plastic-based food filter bags, including polyethylene terephthalate, polypropylene, nylon 6 (NY6), and polyethylene, are widely used for soft drink sub-packaging, increasing the risk of MPs in foods and the environment. Three types of commercially available filter bags, including non-woven and woven bags, were collected, and MPs released after soaking were mapped using Raman imaging combined with chemometrics. Compared with peak area imaging at a single characteristic peak, Raman imaging combined with direct classical least squares calculation was more efficient and reliable for identifying MP features. Up to 94% of the bags released MPs after soaking, and there was no significant correlation with soaking conditions. Most MPs were tiny fragments and particles, and a few were fibrous MPs 620–840 μm in size. Woven NY6 filter bags had the lowest risk of releasing MPs. Source exploration revealed that most MPs originated from fragments and particles adsorbed on the surface of bags and strings. The results of this study are applicable to filter bag risk assessment and provide scientific guidance for regulating MPs in food.

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“…There is a pressing need to explore and develop strategies to accurately and consistently identify plastics using imperfect spectroscopic data. Such strategies should be developed in parallel for both Raman and FTIR spectroscopies as their varied strengths and limitations lead to differences in quantitative capabilities …”

Section: Introductionmentioning

confidence: 99%

“…Such strategies should be developed in parallel for both Raman and FTIR spectroscopies as their varied strengths and limitations lead to differences in quantitative capabilities. 35 The rapid development of machine-learning (ML) algorithms provides many accessible tools that are being explored to improve microplastic analysis protocols. 36 An ML classification model based on the random forest (RF) algorithm trained on 306 Raman spectra from the open-source SLOPP and SLOPP-E databases was found to be capable of 89% classification accuracy.…”

Section: ■ Introductionmentioning

confidence: 99%

Customizable Machine-Learning Models for Rapid Microplastic Identification Using Raman Microscopy

et al. 2022

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Raman spectroscopy is commonly used in microplastics identification, but equipment variations yield inconsistent data structures that disrupt the development of communal analytical tools. We report a strategy to overcome the issue using a database of high-resolution, full-window Raman spectra. This approach enables customizable analytical tools to be easily createda feature we demonstrate by creating machine-learning classification models using open-source random-forest, K-nearest neighbors, and multi-layer perceptron algorithms. These models yield >95% classification accuracy when trained on spectroscopic data with spectroscopic data downgraded to 1, 2, 4, or 8 cm–1 spacings in Raman shift. The accuracy can be maintained even in non-ideal conditions, such as with spectroscopic sampling rates of 1 kHz and when microplastic particles are outside the focal plane of the laser. This approach enables the creation of classification models that are robust and adaptable to varied spectrometer setups and experimental needs.

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Automated analysis of microplastics based on vibrational spectroscopy: are we measuring the same metrics?

Cited by 42 publications

References 59 publications

Optical photothermal infrared spectroscopy with simultaneously acquired Raman spectroscopy for two-dimensional microplastic identification

Optical photothermal infrared spectroscopy with simultaneously acquired Raman spectroscopy for two-dimensional microplastic identification

Identification and Evaluation of Microplastics from Tea Filter Bags Based on Raman Imaging

Customizable Machine-Learning Models for Rapid Microplastic Identification Using Raman Microscopy

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