Journal
ANALYTICAL CHEMISTRY
Volume 94, Issue 7, Pages 3150-3157Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.1c04498
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Funding
- CRC CARE
- University of Newcastle, Australia
- Flinders University, South Australia
- Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, P. R. China
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This study proposes a dual-principal component analysis (PCA) approach for analyzing microplastics and nanoplastics. By performing two rounds of PCA analysis on the raw spectrum data from the Raman scanning matrix and merging it with the standard spectra of common plastics, it enables digital imaging and analysis of microplastics.
As emerging contaminants, microplastics are challenging to characterize, particularly when their size is at the nanoscale. While imaging technology has received increasing attention recently, such as Raman imaging, decoding the scanning spectrum matrix can be difficult to achieve result digitally and automatically via software and usually requires the involvement of personal experience and expertise. Herewith, we show a dual-principal component analysis (PCA) approach, where (i) the first round of PCA analysis focuses on the raw spectrum data from the Raman scanning matrix and generates two new matrices, with one containing the spectrum profile to yield the PCA spectrum and the other containing the PCA intensity to be mapped as an image; (ii) the second round of PCA analysis merges the spectrum from the first round of PCA with the standard spectra of eight common plastics, to generate a correlation matrix. From the correlation value, we can digitally assign the principal components from the first round of PCA analysis to the plastics toward imaging, akin to dataset indexing. We also demonstrate the effect of the data pretreatment and the wavenumber variations. Overall, this dual-PCA approach paves the way for machine learning to analyze microplastics and particularly nanoplastics.
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