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Abstract 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 h 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. 

Monitoring plastic litter in the environment is critical to understanding the amount, sources, transport, fate, and environmental impact of this pollutant. However, few studies have monitored plastic litter on lakebeds which are potentially important environments for determining the fate and transport of plastic litter in freshwater basins. In this study, a self-contained underwater breathing apparatus was used for litter collection at the lakebed along five transects in Lake Tahoe, United States. Litter was brought to the surface and characterized by litter type. Plastic litter was subsampled, and polymer composition was determined using attenuated total reflection Fourier transform infrared spectroscopy. The average plastic litter from the lakebed for the five dive transects was 83 ± 49 items per kilometer. The top plastic litter categories were other plastic litter (plastic litter that did not fall in another category), followed by food containers, bottles <2 L, plastic bags, and toys. These results are in line with prior studies on submerged litter, and intervention approaches or ongoing education are needed. The six polymers most frequently detected in the subsamples were polyvinyl chloride, polystyrene/expanded polystyrene, polyethylene terephthalate/polyester, polyethylene, polypropylene, and polyamide. These observations reflect global plastic production and microplastic studies from lake surface water and sediments. We found that some litter subcategories were primarily comprised of a single polymer type, therefore, in studies where the polymer type cannot be measured but litter is categorized, these results could provide an estimate of the total polymer composition for select litter categories.