2022
DOI: 10.1007/s00216-022-04371-2
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A high-throughput, automated technique for microplastics detection, quantification, and characterization in surface waters using laser direct infrared spectroscopy

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Cited by 23 publications
(11 citation statements)
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“…Values ranged from 0 to 1, where larger values indicate a higher probability of specific microplastics. In this study, we included microplastics with a matching degree of ≥0.70 according to several previous LDIR studies. Alternatively, because of the subtle difference in the infrared radiation absorption spectrum between polyamides and natural proteins, polyamide was classified as a nonmicroplastic herein.…”
Section: Methodsmentioning
confidence: 99%
“…Values ranged from 0 to 1, where larger values indicate a higher probability of specific microplastics. In this study, we included microplastics with a matching degree of ≥0.70 according to several previous LDIR studies. Alternatively, because of the subtle difference in the infrared radiation absorption spectrum between polyamides and natural proteins, polyamide was classified as a nonmicroplastic herein.…”
Section: Methodsmentioning
confidence: 99%
“…A newer analytical method for water using laser direct infrared spectroscopy (LDIR) shows the potential for reducing analysis times considerably as compared to FTIR or Raman spectroscopy while still maintaining the detection limits of these methods (20 μm) (Whiting et al 2022). NPs are more difficult to quantify, but methods development are underway (Stine et al 2023).…”
Section: Sampling and Analysis Methodsmentioning
confidence: 99%
“…The detection technology for microplastics is currently advancing at a rapid pace. The successful application and refinement of advanced detection techniques, such as mass spectrometry (MS), micro-Fourier-transform infrared (μ-FTIR) spectroscopy, micro-Raman (μ-Raman) spectroscopy, laser direct infrared (LD-IR) spectroscopy, and Nile Red (NR) fluorescent staining, as well as their combinations, in microplastic research have significantly enhanced the efficiency of identifying and quantifying microplastics in complex biological and environmental samples. Furthermore, with the evolution of artificial intelligence (AI), the automatic identification and classification of microplastics based on machine learning are also becoming increasingly sophisticated. The swift progress in microplastic detection and analysis technology facilitates the acquisition of extensive microplastic data. In this context, it becomes particularly vital to approach microplastic research with a holistic perspective to unveil the distribution, fate, and transport patterns of microplastics, as well as their impacts on the environment and organisms.…”
Section: Introductionmentioning
confidence: 99%