Improved Microplastic Identification from Simultaneously Collected Photothermal Infrared and Raman Spectra Using Multiview Conformal Prediction

Microplastics (MPs) have been documented in urban and remote locations across the globe. Further identification and quantification of MPs are required to determine how widespread they are in the environment. One of the most popular chemometric methods for identifying microplastics (MPs) is database matching, in which an unknown spectrum is compared with reference library spectra by calculating likeness scores. Threshold minima determine if the score is high enough to consider the reference spectrum as a potential match, yet these thresholds are frequently set arbitrarily. There is a growing consensus that MP identification should involve multiple measurement techniques, but statistically robust methods to relate multiple database matching scores are lacking. Herein, multiview conformal prediction (MVCP) incorporates two views (i.e., photothermal infrared (PTIR) and Raman spectra) to calculate multidimensional thresholds for MP identification with statistical confidence. The chemical identities returned for an unknown particle have a statistical assurance that one of the identities is the correct match based on a user-defined uncertainty parameter. The average number of potential matches returned by MVCP was closer to one chemical identity─the ideal number of identities returned─when compared to single-view CP methods that used either the PTIR or Raman spectra. Moreover, MVCP was less affected than its single-view counterparts when one of the two spectra was difficult, or impossible, to identify. To show the utility of MVCP for real-world samples, an ambient particle sample with MPs deposited on it was used to demonstrate that an MVCP threshold at 73% theoretical confidence maximized the fraction of MP particles correctly identified as plastic (0.80 ± 0.07), while limiting the fraction of non-MP environmental particles misidentified as plastic (0.10 ± 0.06). This initial application of MVCP to spectroscopy demonstrates the benefits of utilizing multiple spectral methods with data analysis routines for identifying MPs with statistical confidence.