Prioritization of Unknown LC-HRMS Features Based on Predicted Toxicity Categories DOI Creative Commons

Viktoriia Turkina,

Jelle T. Gringhuis,

Sanne Boot

et al.

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 20, 2025

Complex environmental samples contain a diverse array of known and unknown constituents. While liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) nontargeted analysis (NTA) has emerged as an essential tool for the comprehensive study such samples, identification individual constituents remains significant challenge, primarily due to vast number detected features in each sample. To address this, prioritization strategies are frequently employed narrow focus most relevant further analysis. In this study, we developed novel strategy that directly links fragmentation chromatographic data aquatic toxicity categories, bypassing need compounds. Given not always well-characterized through fragmentation, created two models: (1) Random Forest Classification (RFC) model, which classifies fish categories based on MS1, retention, data─expressed cumulative neutral losses (CNLs)─when information is available, (2) Kernel Density Estimation (KDE) model relies solely retention time MS1 when absent. Both models demonstrated accuracy comparable structure-based prediction methods. We tested pesticide mixture tea extract measured by LC-HRMS, where CNL-based RFC achieved 0.76 KDE reached 0.61, showcasing their robust performance real-world applications.

Language: Английский

Prioritization of Unknown LC-HRMS Features Based on Predicted Toxicity Categories DOI Creative Commons

Viktoriia Turkina,

Jelle T. Gringhuis,

Sanne Boot

et al.

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 20, 2025

Complex environmental samples contain a diverse array of known and unknown constituents. While liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) nontargeted analysis (NTA) has emerged as an essential tool for the comprehensive study such samples, identification individual constituents remains significant challenge, primarily due to vast number detected features in each sample. To address this, prioritization strategies are frequently employed narrow focus most relevant further analysis. In this study, we developed novel strategy that directly links fragmentation chromatographic data aquatic toxicity categories, bypassing need compounds. Given not always well-characterized through fragmentation, created two models: (1) Random Forest Classification (RFC) model, which classifies fish categories based on MS1, retention, data─expressed cumulative neutral losses (CNLs)─when information is available, (2) Kernel Density Estimation (KDE) model relies solely retention time MS1 when absent. Both models demonstrated accuracy comparable structure-based prediction methods. We tested pesticide mixture tea extract measured by LC-HRMS, where CNL-based RFC achieved 0.76 KDE reached 0.61, showcasing their robust performance real-world applications.

Language: Английский

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