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.

Methods
This data set includes a series of uFTIR spectra collected from 22 known natural and synthetic materials gathered from various sources including manufacturer samples, textile suppliers, the Hawaii Pacific University Polymer Identification Test Kit (CMDR 2020), and readily available materials. Fibers were cut to approximately 100-200 μm length using a razor knife. Fragments were placed in a -26.7 °C freezer for a minimum of 24 hours, ground in a stainless-steel coffee grinder, and wet-sieved to isolate particles in the 125 – 335 μm size range. A subsample from each material type was placed on a stainless-steel filter with an aperture of 100 μm. Next, 30 µFTIR spectra were collected from different particles of each material type using a Nicolet iN10 MX FTIR microscope (Thermo Nicolet Analytical Instruments, Madison, WI) with a liquid nitrogen-cooled mercury cadmium telluride (MCT) detector. Spectra were collected in transmission mode with an aperture size of 100 μm by 100 μm. Each sample point was scanned 8 times with a spectral resolution of 8 cm-1. The wavenumber range of collected spectra was 675-4000 cm-1 and a Beer-Norton apodization filter was used to reduce noise. Additionally, spectra were collected in groups of 25 or less and background correction measurements were taken immediately before each group (within 5 minutes of any individual spectra collection).