{"entity": "researcher", "timestamp": "2026-08-20T21:26:06.090Z", "family": "Kruve", "given": "Anneli", "initials": "A", "orcid": "0000-0001-9725-3351", "affiliations": ["Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius v\u00e4g 16, 106 91 Stockholm, Sweden.", "Department of Environmental Science, Stockholm University, Svante Arrhenius v\u00e4g 8, 106 91 Stockholm, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/09b2fdaf67e44efab3cf3fa747dfa992.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/09b2fdaf67e44efab3cf3fa747dfa992"}}, "publications": [{"entity": "publication", "iuid": "9b16247f2153447b89929c94e8c57c10", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/9b16247f2153447b89929c94e8c57c10.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/9b16247f2153447b89929c94e8c57c10"}}, "title": "Evaluation of Nontargeted Mass Spectral Data Acquisition Strategies for Water Analysis and Toxicity-Based Feature Prioritization by MS2Tox.", "authors": [{"family": "Peets", "given": "Pilleriin", "initials": "P", "orcid": "0000-0002-7095-661X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bf5a7ed1ac024e36bc6c658e0ac82a53.json"}}, {"family": "Rian", "given": "May Britt", "initials": "MB", "orcid": "0000-0001-6153-2164", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/191d5d1c23714beb95be36a7a9140da6.json"}}, {"family": "Martin", "given": "Jonathan W", "initials": "JW", "orcid": "0000-0001-6265-4294", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/02199c0d99674e2ea5e96e6589c07a00.json"}}, {"family": "Kruve", "given": "Anneli", "initials": "A", "orcid": "0000-0001-9725-3351", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/09b2fdaf67e44efab3cf3fa747dfa992.json"}}], "type": "journal article", "published": "2024-10-01", "journal": {"title": "Environ. Sci. Technol.", "issn": "1520-5851", "volume": "58", "issue": "39", "pages": "17406-17418", "issn-l": "0013-936X"}, "abstract": "The machine-learning tool MS2Tox can prioritize hazardous nontargeted molecular features in environmental waters, by predicting acute fish lethality of unknown molecules based on their MS2 spectra, prior to structural annotation. It has yet to be investigated how the extent of molecular coverage, MS2 spectra quality, and toxicity prediction confidence depend on sample complexity and MS2 data acquisition strategies. We compared two common nontargeted MS2 acquisition strategies with liquid chromatography high-resolution mass spectrometry for structural annotation accuracy by SIRIUS+CSI:FingerID and MS2Tox toxicity prediction of 191 reference chemicals spiked to LC-MS water, groundwater, surface water, and wastewater. Data-dependent acquisition (DDA) resulted in higher rates (19-62%) of correct structural annotations among reference chemicals in all matrices except wastewaters, compared to data-independent acquisition (DIA, 19-50%). However, DIA resulted in higher MS2 detection rates (59-84% DIA, 37-82% DDA), leading to higher true positive rates for spectral library matching, 40-73% compared to 34-72%. DDA resulted in higher MS2Tox toxicity prediction accuracy than DIA, with root-mean-square errors of 0.62 and 0.71 log-mM, respectively. Given the importance of MS2 spectral quality, we introduce a \"CombinedConfidence\" score to convey relative confidence in MS2Tox predictions and apply this approach to prioritize potentially ecotoxic nontargeted features in environmental waters.", "doi": "10.1021/acs.est.4c02833", "pmid": "39297340", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11447898"}], "notes": [], "created": "2026-08-20T08:09:54.234Z", "modified": "2026-08-20T08:09:54.377Z"}, {"entity": "publication", "iuid": "1a46fdab0754486fba0ce7bfe33eb95a", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1a46fdab0754486fba0ce7bfe33eb95a.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1a46fdab0754486fba0ce7bfe33eb95a"}}, "title": "Bypassing the Identification: MS2Quant for Concentration Estimations of Chemicals Detected with Nontarget LC-HRMS from MS2 Data.", "authors": [{"family": "Sepman", "given": "Helen", "initials": "H"}, {"family": "Malm", "given": "Louise", "initials": "L"}, {"family": "Peets", "given": "Pilleriin", "initials": "P"}, {"family": "MacLeod", "given": "Matthew", "initials": "M", "orcid": "0000-0003-2562-7339", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/442bc3b108d848a0843e092a7582ca76.json"}}, {"family": "Martin", "given": "Jonathan", "initials": "J", "orcid": "0000-0001-6265-4294", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/02199c0d99674e2ea5e96e6589c07a00.json"}}, {"family": "Breitholtz", "given": "Magnus", "initials": "M", "orcid": "0000-0002-4984-8323", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1e2f3c4f62444d3c80f6bd1222c44ca5.json"}}, {"family": "Kruve", "given": "Anneli", "initials": "A", "orcid": "0000-0001-9725-3351", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/09b2fdaf67e44efab3cf3fa747dfa992.json"}}], "type": "journal article", "published": "2023-08-22", "journal": {"title": "Anal. Chem.", "issn": "1520-6882", "volume": "95", "issue": "33", "pages": "12329-12338", "issn-l": "0003-2700"}, "abstract": "Nontarget analysis by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation (MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI:FingerID software. The root mean square errors of the training and test sets were 0.55 (3.5\u00d7) and 0.80 (6.3\u00d7) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2\u00d7 to 6\u00d7. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4\u00d7, a geometric mean of 4.5\u00d7, and a median of 4.0\u00d7. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5\u00d7, a geometric mean of 5.6\u00d7, and a median of 5.2\u00d7 on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. The MS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.", "doi": "10.1021/acs.analchem.3c01744", "pmid": "37548594", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10448440"}], "notes": [], "created": "2026-08-20T08:08:37.794Z", "modified": "2026-08-20T08:08:37.935Z"}]}