Evidence map›Paper›PMID 39508740›Full record

ArticleAnalytical chemistry2024

A Semi-Quantitative Approach to Nontarget Compositional Analysis of Complex Samples.

Rhianna L Evans, Daniel J Bryant, Aristeidis Voliotis, Dawei Hu, HuiHui Wu, Sara Aisyah Syafira, Osayomwanbor E Oghama, Gordon McFiggans, Jacqueline F Hamilton, Andrew R Rickard

Abstract read
In one paragraph

Article in Analytical chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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  3. Review
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  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Rhianna L EvansWolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York YO10 5DD, United Kingdom.ORCID 0000-0001-8789-1528
Daniel J BryantWolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York YO10 5DD, United Kingdom.
Aristeidis VoliotisCentre for Atmospheric Science, Department of Earth and Environmental Sciences, School of Natural Sciences, University of Manchester, Manchester M13 9PL, United Kingdom.ORCID 0000-0001-9710-9851
Dawei HuCentre for Atmospheric Science, Department of Earth and Environmental Sciences, School of Natural Sciences, University of Manchester, Manchester M13 9PL, United Kingdom.
HuiHui WuCentre for Atmospheric Science, Department of Earth and Environmental Sciences, School of Natural Sciences, University of Manchester, Manchester M13 9PL, United Kingdom.
Sara Aisyah SyafiraCentre for Atmospheric Science, Department of Earth and Environmental Sciences, School of Natural Sciences, University of Manchester, Manchester M13 9PL, United Kingdom.
Osayomwanbor E OghamaCentre for Atmospheric Science, Department of Earth and Environmental Sciences, School of Natural Sciences, University of Manchester, Manchester M13 9PL, United Kingdom.
Gordon McFiggansCentre for Atmospheric Science, Department of Earth and Environmental Sciences, School of Natural Sciences, University of Manchester, Manchester M13 9PL, United Kingdom.ORCID 0000-0002-3423-7896
Jacqueline F HamiltonWolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York YO10 5DD, United Kingdom.ORCID 0000-0003-0975-4311
Andrew R RickardWolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York YO10 5DD, United Kingdom.ORCID 0000-0003-2203-3471

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nontarget analysis (NTA) by liquid chromatography coupled to high-resolution mass spectrometry improves the capacity to comprehend the molecular composition of complex mixtures compared to targeted analysis techniques. However, the detection of unknown compounds means that quantification in NTA is challenging. This study proposes a new semi-quantitative methodology for use in the NTA of organic aerosol. Quantification of unknowns is achieved using the average ionization efficiency of multiple quantification standards which elute within the same retention time window as the unknown analytes. In total, 110 authentic standards constructed 25 retention time windows for the quantification of oxygenated (CHO) and organonitrogen (CHON) species. The method was validated on extracts of biomass burning organic aerosol (BBOA) and compared to quantification with authentic standards and had an average prediction error of 1.52 times. Furthermore, 70% of concentrations were estimated within a factor of 2 (prediction errors between 0.5 and 2 times) from the authentic standard quantification. The semi-quantification method also showed good agreement for the quantification of CHO compounds compared to predictive ionization efficiency-based methods, whereas for CHON species, the prediction error of the semi-quantification method (1.63) was significantly lower than the predictive ionization efficiency approach (14.94). Application to BBOA for the derivation of relative abundances of CHO and CHON species showed that using peak area underestimated the relative abundance of CHO by 19% and overestimated that of CHON by 11% compared to the semi-quantification method. These differences could lead to significant misinterpretations of source apportionment in complex samples, highlighting the need to account for ionization differences in NTA approaches.

Identifiers

PMID39508740
PMCPMC11579983

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.