Towards a harmonized identification scoring system in LC-HRMS/MS based non-target screening (NTS) of emerging contaminants

Alygizakis, Nikiforos; Lestremau, François; Gago-Ferrero, Pablo; Gil-Solsona, Rubén; Arturi, Katarzyna Ratajczyk; Hollender, Juliane; Schymanski, Emma L.; Dulio, Valeria; Slobodnı́k, Jaroslav; Τhomaidis, Νikolaos S.

doi:10.1016/j.trac.2023.116944

Cited by 55 publications

(33 citation statements)

References 39 publications

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“… a Ionization mode (IM). b m / z for the ionized chemical in positive IM (POS) or in negative IM (NEG). c Identification points (IP): values represent the confidence level for compound identification, with a score closer to 1 indicating higher certainty. Derived from the scoring system proposed by Alygizakis et al d To calculate the average and median, values <LOQ were considered LOQ/2. e Detection frequency expressed in percentage (DF%). Abbreviations: perfluorobutanesulfonic acid (PFBS), per- and polyfluoroalkyl substances (PFAS), perfluorooctanoic acid (PFOA), perfluorooctanesulfonate (PFOS), diethyltoluamide (DEET), transformation product (TP), dodecyl sulfate (C12-LAS), and tetradecyl sulfate (C14-LAS). …”

Section: Resultsmentioning

confidence: 99%

“… c Identification points (IP): values represent the confidence level for compound identification, with a score closer to 1 indicating higher certainty. Derived from the scoring system proposed by Alygizakis et al …”

Section: Resultsmentioning

confidence: 99%

“…For chemicals not included in the validation, a matrix-matched 3. The IP system was applied to convey the confidence level of the results in Table 3, as described by Alygizakis et al 110 The complete information regarding the concentration of each chemical in the samples, along with a chromatogram, is available in Supplementary SI-6, Table S4 and Figure S1.…”

Section: Wide-scope Screening Of Polar and Semipolar Organic Chemical...mentioning

confidence: 99%

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Exploring the Occurrence of Organic Contaminants in Human Semen through an Innovative LC-HRMS-Based Methodology Suitable for Target and Nontarget Analysis

Sánchez-Resino,

Marquès,

Gutiérrez-Martín

et al. 2023

Environ. Sci. Technol.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Wide-scope Screening Of Polar and Semipolar Organic Chemical...mentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Occurrence of Organic Contaminants in Human Semen through an Innovative LC-HRMS-Based Methodology Suitable for Target and Nontarget Analysis

Sánchez-Resino,

Marquès,

Gutiérrez-Martín

et al. 2023

Environ. Sci. Technol.

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“…Moreover, application on larger data sets, including spiked samples with analytical standards, will provide better refinement on the boundary and robustness of the developed approaches. Machine learning approaches can also be used to implement a simplified scoring model …”

Section: Discussionmentioning

confidence: 99%

Development and Implementation of Automated Qualification Processes for the Identification of Pollutants in an Aquatic Environment from High-Resolution Mass Spectrometric Nontarget Screening Data

Lestremau

Levesque²,

Lahssini³

et al. 2023

ACS EST Water

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Environmental pollution monitoring represents a major challenge due to the growing presence of a large and diverse number of potential contaminants. In complement to target analysis, nontarget analysis, via liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS), is increasingly used to provide a more comprehensive characterization of pollution. The challenge associated with this type of analysis is particularly related to the data treatment for substance identification. One of the main limitations is that all data must be manually reviewed, which is tedious and time-consuming. Machine learning algorithms aim to reproduce human behavior, and their capabilities were therefore evaluated to automatically identify substances in suspect screening approaches. After selecting the relevant features produced from LC/HRMS, seven different machine learning models were evaluated for each of the three different databases, which resulted in the selection of logistic regression (LR) and random forest (RF)-based algorithms. An interface was built to rank the identified substances and to assess the performance of the developed models. The LR model provided the best results when retention times were available. The developed LR and RF models were determined complementarily, particularly when no retention times were available. However, limitations were noticed when using a database containing different HRMS technologies.

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“…To map the complexity of global pollution, sophisticated nontarget screening (NTS) data processing workflows − have been developed for HRMS/MS. These approaches employ numerous computational and machine learning (ML) tools − to identify and quantify novel organic pollutants in the environment based on their MS1 (abundance of parent ions) and MS2 (fragmentation spectra). Although thousands of molecular HRMS/MS features in complex aquatic mixtures can be routinely discerned and processed via NTS, a complete elucidation (unequivocal identification and quantification) of a large number of signals is not yet feasible due to the necessity of manual validation and confirmation with reference standards .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data

Arturi

Hollender

2023

Environ. Sci. Technol.

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Nontarget high-resolution mass spectrometry screening (NTS HRMS/MS) can detect thousands of organic substances in environmental samples. However, new strategies are needed to focus time-intensive identification efforts on features with the highest potential to cause adverse effects instead of the most abundant ones. To address this challenge, we developed MLinvitroTox, a machine learning framework that uses molecular fingerprints derived from fragmentation spectra (MS2) for a rapid classification of thousands of unidentified HRMS/MS features as toxic/nontoxic based on nearly 400 target-specific and over 100 cytotoxic endpoints from ToxCast/Tox21. Model development results demonstrated that using customized molecular fingerprints and models, over a quarter of toxic endpoints and the majority of the associated mechanistic targets could be accurately predicted with sensitivities exceeding 0.95. Notably, SIRIUS molecular fingerprints and xboost (Extreme Gradient Boosting) models with SMOTE (Synthetic Minority Oversampling Technique) for handling data imbalance were a universally successful and robust modeling configuration. Validation of MLinvitroTox on MassBank spectra showed that toxicity could be predicted from molecular fingerprints derived from MS2 with an average balanced accuracy of 0.75. By applying MLinvitroTox to environmental HRMS/MS data, we confirmed the experimental results obtained with target analysis and narrowed the analytical focus from tens of thousands of detected signals to 783 features linked to potential toxicity, including 109 spectral matches and 30 compounds with confirmed toxic activity.

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Towards a harmonized identification scoring system in LC-HRMS/MS based non-target screening (NTS) of emerging contaminants

Cited by 55 publications

References 39 publications

Exploring the Occurrence of Organic Contaminants in Human Semen through an Innovative LC-HRMS-Based Methodology Suitable for Target and Nontarget Analysis

Exploring the Occurrence of Organic Contaminants in Human Semen through an Innovative LC-HRMS-Based Methodology Suitable for Target and Nontarget Analysis

Development and Implementation of Automated Qualification Processes for the Identification of Pollutants in an Aquatic Environment from High-Resolution Mass Spectrometric Nontarget Screening Data

Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data

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