Raman imaging and MALDI-MS towards identification of microplastics generated when using stationery markers

• Published in: Journal of hazardous materials Vol. 424; no. Pt B; p. 127478
• Main Authors: Luo, Yunlong, Sobhani, Zahra, Zhang, Zixing, Zhang, Xian, Gibson, Christopher T., Naidu, Ravi, Fang, Cheng
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.02.2022
• Subjects: Algorithm; algorithms; Biomarkers; Marker ink; Matrix-assisted laser desorption/ionisation-mass spectrometry; Microplastics; Plastics; Principal Component Analysis; Raman imaging; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Matrix-assisted laser desorption/ionisation-mass spectrometry; Microplastics; Marker ink; Raman imaging; Algorithm
• ISSN: 0304-3894; 1873-3336; 1873-3336
• DOI: 10.1016/j.jhazmat.2021.127478

The characterisation of microplastics is still a challenge, particularly when the sample is a mixture with a complex background, such as an ink mark on paper. To address this challenge, we developed and compared two approaches, (i) Raman imaging, combined with logic-based and principal component analysis (PCA)-based algorithms, and (ii) matrix-assisted laser desorption/ionisation-mass spectrometry (MALDI-MS). We found that, accordingly, (i) if the Raman signal of plastics is identifiable and not completely shielded by the background, Raman imaging can extract the plastic signals and visualise their distribution directly, with the help of a logic-based or PCA-based algorithm, via the “fingerprint” spectrum; (ii) when the Raman signal is shielded and masked by the background, MALDI-MS can effectively capture and identify the plastic polymer, via the “barcode” of the mass spectrum linked with the monomer. Overall, both Raman imaging and MALDI-MS have benefits and limitations for microplastic analysis; if accessible, the combined use of these two techniques is generally recommended, especially when assessing samples with strong background interference. [Display omitted] •Raman imaging can directly visualise microplastics.•Logic-/PCA-based algorithms can analyse plastics via fingerprint of Raman spectrum.•MALDI-MS can identify plastics via “barcode” of mass spectrum.•Microplastics are generated in our daily lives when we use stationery markers.