For decades, targeted analysis methods have been the gold standard for chemical quantification in environmental studies. These methods, while highly accurate, have limited practical use when chemical identities are unknown, or when numbers of chemicals per sample approach thousands. Further, targeted analyses require prior knowledge of the chemicals of interest, and readily obtainable analytical standards. Conversely, non-targeted analysis (NTA) methods aim to identify and quantify thousands of chemicals in any sample of interest without prior knowledge. While NTA chemical identification methods have rapidly matured, ongoing efforts toward NTA quantification have lagged, and generally do not consider statistical confidence about individual predictions. For NTA concentration estimates to be utilized in regulatory contexts, statistically defensible methods of NTA chemical quantitation must be developed. Furthermore, since bounded concentration estimates are generally specific to prepared sample extracts (in solvent), extrapolation methods are needed to extend quantitative predictions to the original sampled media, and eventually, to hazard metrics that can support risk characterization. Here, we detail the mathematical basis of traditional calibration methods used in targeted analyses, and how these methods can be extended to support quantitative estimates for all chemicals observed in NTA experiments. A default one-dimensional method is detailed for bounding concentration estimates based on an observed (and transformed) distribution of instrument response factors. A more complex method is then described, based on modeled ionization efficiencies calculated from machine-learning models using physicochemical descriptors. The uncertainty in both quantitative NTA methods is compared to the uncertainty observed when performing automated NTA experiments with traditional approaches (using calibration curves). We show that, in lieu of analytical standards, statistically defensible quantitative NTA is feasible using either method given a base set of known response factors.

Statistical evaluation of quantitative non-targeted analysis methods using ENTACT data

ACS Fall 2021

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