Non-targeted analysis (NTA) workflows using mass spectrometry are gaining popularity in many disciplines, but universally accepted reporting standards are nonexistent. Current guidance addresses limited elements of NTA reportingmost notably, identification confidenceand is insufficient to ensure scientific transparency and reproducibility given the complexity of these methods. This lack of reporting standards hinders researchers' development of thorough study protocols and reviewers' ability to efficiently assess grant and manuscript submissions. To overcome these challenges, we developed the NTA Study Reporting Tool (SRT), an easy-touse, interdisciplinary framework for comprehensive NTA methods and results reporting. Eleven NTA practitioners reviewed eight published articles covering environmental, food, and health-based exposomic applications with the SRT. Overall, our analysis demonstrated that the SRT provides a valid structure to guide study design and manuscript writing, as well as to evaluate NTA reporting quality. Scores self-assigned by authors fell within the range of peer-reviewer scores, indicating that SRT use for self-evaluation will strengthen reporting practices. The results also highlighted NTA reporting areas that need immediate improvement, such as analytical sequence and quality assurance/quality control information. Although scores intentionally do not correspond to data/results quality, widespread implementation of the SRT could improve study design and standardize reporting practices, ultimately leading to broader use and acceptance of NTA data.

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Polyphenols, prevalent in plants and fungi, are investigated intensively in nutritional and clinical settings because of their beneficial bioactive properties. Due to their complexity, analysis with untargeted approaches is favorable, which typically use high-resolution mass spectrometry (HRMS) rather than low-resolution mass spectrometry (LRMS). Here, the advantages of HRMS were evaluated by thoroughly testing untargeted techniques and available online resources. By applying data-dependent acquisition on real-life urine samples, 27 features were annotated with spectral libraries, 88 with in silico fragmentation, and 113 by MS1 matching with PhytoHub, an online database containing >2000 polyphenols. Moreover, other exogenous and endogenous molecules were screened to measure chemical exposure and potential metabolic effects using the Exposome-Explorer database, further annotating 144 features. Additional polyphenol-related features were explored using various non-targeted analysis techniques including MassQL for glucuronide and sulfate neutral losses, and MetaboAnalyst for statistical analysis. As HRMS typically suffers a sensitivity loss compared to state-of-the-art LRMS used in targeted workflows, the gap between the two instrumental approaches was quantified in three spiked human matrices (urine, serum, plasma) as well as real-life urine samples. Both instruments showed feasible sensitivity, with median limits of detection in the spiked samples being 10–18 ng/mL for HRMS and 4.8–5.8 ng/mL for LRMS. The results demonstrate that, despite its intrinsic limitations, HRMS can readily be used for comprehensively investigating human polyphenol exposure. In the future, this work is expected to allow for linking human health effects with exposure patterns and toxicological mixture effects with other xenobiotics.

Polyphenols, prevalent in plants and fungi, are investigated intensively in nutritional and clinical settings because of their beneficial bioactive properties. Due to their complexity, analysis with untargeted approaches is favorable, which typically use high-resolution mass spectrometry (HRMS) rather than low-resolution mass spectrometry (LRMS). The advantages of HRMS were evaluated here by thoroughly testing untargeted techniques and available online resources. By applying data-dependent acquisition on real-life urine samples, 27 features were annotated with spectral libraries, 88 with in silico fragmentation, and 113 by MS1 using PhytoHub, an online database containing >2000 polyphenols. Moreover, other exogenous and endogenous molecules were screened to measure chemical exposure and a potential metabolic effect using the Exposome-Explorer database, yielding an additional 144 annotated features. Additional polyphenol-related features were explored using various non-targeted analysis techniques including MassQL for glucuronide and sulfate neutral losses, and MetaboAnalyst for statistical analysis. As HRMS typically suffers a sensitivity loss compared to state-of-the-art LRMS used in targeted workflows, this gap between the two instrumental approaches was quantified in three spiked human matrices (urine, serum, plasma) as well as real-life urine samples. Both instruments showed feasible sensitivity, with median limits of detection in the spiked samples being 10 - 18 ng/mL for HRMS and 4.8 - 5.8 ng/mL for LRMS. The results demonstrate that despite its intrinsic limitations, HRMS can readily be used for comprehensively investigating human exposure. In the future, this work is expected to allow for linking human health effects with polyphenol exposure, and toxicological mixture effects with other xenobiotics.

Polyphenols are investigated intensively in nutritional and clinical settings because of their beneficial bioactive properties and prevalence in plant-based foods. Due to their complexity, analysis with untargeted approaches is favorable. These approaches typically use high-resolution mass spectrometry (HRMS) rather than low-resolution mass spectrom-etry (LRMS). The advantages of using HRMS were evaluated in this work by thoroughly testing various untargeted techniques and available online resources. By applying iterative data-dependent acquisition on real-life urine samples, 27 features were identified with spectral libraries, 88 with in silico fragmentation, and 113 features were annotated by their monoisotopic mass with PhytoHub, an online database containing >2000 polyphenols. Moreover, potentially polyphenol-related features were searched for using various non-targeted analysis techniques including MassQL for glucuronide and sulfate neutral losses, and MetaboAnalyst for statistical analysis. As HRMS typically suffers from a sensitivity loss compared to state-of-the-art LRMS used in targeted workflows, the sensitivity gap between the two instrumental approaches was quantified. Both instruments showed feasible sensitivity, with the median limits of detection being 10 - 18 ng/mL for HRMS and 4.8 - 5.8 ng/mL for LRMS in three human matrices (urine, serum, and plas-ma). The applicability of HRMS was further evaluated in real-life urine samples with 24 polyphenols detected. The results demonstrate that despite its intrinsic limitations, HRMS can readily be used for comprehensively investigating human exposure and related health effects of polyphenols and their metabolites as well as toxicological mixture effects with other xenobiotics.