ArticleAnalytical and bioanalytical chemistry2026
Structure-guided parameter optimization of feature-based molecular networking for DIA-HRMS screening of antihistamines in cosmetics.
Article in Analytical and bioanalytical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Feature-based molecular networking (FBMN) can support prioritization of library-absent features in high-resolution mass spectrometric screening, but parameter choices may create chemically misleading links, particularly for data-independent acquisition (DIA) spectra. We developed a structure-guided multi-metric parameter-optimization framework using 51 authenticated antihistamines assigned to structural classes independently of retention time, MS/MS similarity, and network topology. Across 595 GNPS-FBMN parameter combinations, the selected application-specific setting P264 (minimum cosine similarity, 0.475; matched ions, 8; MAX_SHIFT, 150 Da) gave an adjusted Rand index of 0.216, a same-class edge ratio of 0.697, and a cross-class edge ratio of 0.303, compared with 0.101, 0.621, and 0.379, respectively, for the controlled GNPS edge-default setting. In 120 commercial cosmetics, in-house library screening detected diphenhydramine in one sample; authentic-standard comparison confirmed the retention time, precursor ion, and MS/MS evidence, and post-confirmation measurement gave 3.28 mg kg
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