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.