uFTIR test spectra for known synthetic and natural materials

Infrared spectroscopy is a widely used tool for studying microplastics and
identifying microparticles. Researchers rely on spectral libraries to
differentiate between synthetic and natural materials. Unfortunately,
spectral library matching is not perfect, and best practices require
researchers to use time-consuming, manual peak matching to assess spectral
matches. Moving toward automated matching requires increased confidence in
the matching process. Using spectra-matching software may increase the
efficiency of particle identification, however, some matching strategies
may confuse natural materials such as cotton, silk, and plant matter with
common classes of synthetics such as polyesters and polyamides. In this
experiment, we prepared 22 pristine sample materials from natural and
synthetic sources and measured micro-Fourier transform infrared (µFTIR)
spectra in transmission mode for each sample using a Thermo Nicolet iN10
MX instrument. The collected spectra were then input into two spectral
library matching systems (Omnic Picta and Open Specy), using a total of
five identification routines. Next, we placed a subset of four pristine
microplastic materials in a biologically active river system for two weeks
to simulate environmental samples. These simulated environmental samples
were processed using 10% hydrogen peroxide for 24 hours to remove organic
contamination and then identified using the strongest performing library.
We found that libraries with fewer sample spectra produced lower
correlation matches and that using derivative correction greatly reduced
the number of inaccuracies in identifying materials as either natural or
synthetic. We also found that environmental fouling reduced the
correlation value of library matches when compared to pristine particles,
however, the effect was not consistent across the four materials tested.
Overall, we found that the accuracy of automated library matching in the
tested systems and processing routines varied from 64.1 to 98.0% for
distinguishing between natural and synthetic materials and that a high Hit
Quality Index (HQI) did not always correlate with accuracy. These results
are important for the microplastic field, demonstrating a need to
rigorously test spectral libraries and processing routines with known
materials to ensure identification accuracy.