Evidence map›Paper›PMID 42164876›Full record

ReviewEnvironment & health (Washington, D.C.)2026

From Target-Nontarget to Nontarget Screening: A Review on Screening Methods for Organic Pollutants Based on High-Resolution Mass Spectrometry.

Runmin Yuan, Hongrui Zhang, Jiyan Liu, Miao Yu, Xingwang Hou, Guibin Jiang

Abstract readReview
In one paragraph

Review in Environment & health (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

6 authors.

Runmin YuanState Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.
Hongrui ZhangState Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.
Jiyan LiuState Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.ORCID https://orcid.org/0000-0002-7553-3325
Miao YuThe Jackson Laboratory, Farmington, Connecticut 06032, United States.
Xingwang HouState Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.ORCID https://orcid.org/0000-0002-4675-4364
Guibin JiangState Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.ORCID https://orcid.org/0000-0002-6335-3917

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although certain substances exist in the environment at trace concentrations, their ecological risks remain significant, making nontarget screening (NTS) one of the most important topics in the fields of environmental and analytical chemistry today. In recent years, with the development of high-resolution mass spectrometry (HRMS), the higher precision and accuracy have provided mass spectrometry (MS) characteristics with deeper application significance, offering new perspectives for NTS. This review briefly introduces the principles and methodologies of different MS characteristics and summarizes emerging advancements in screening strategies based on these characteristics. We innovatively propose a target-nontarget to nontarget screening workflow, which links different MS characteristics to achieve comprehensive NTS. This workflow prioritizes the currently applied MS characteristics, aiming to integrate various analytical steps to expand the screening coverage while maintaining reliability. The prioritization includes 1) mass defect (MD) analysis, 2) isotope patterns, 3) characteristic fragments, 4) construction of MS characteristics through chemical reactions, and 5) intermolecular connectivity MS characteristics mining. Subsequently, we provided an overview of the mining and prediction of MS characteristics based on machine learning. Finally, we evaluated the challenges of transitioning from target-nontarget to nontarget workflows and proposed future perspectives for NTS.

Indexed as

data mininghigh-resolution mass spectrometrymass spectrometry characteristicsnontarget screeningorganic pollutants

Identifiers

PMID42164876
PMCPMC13185067

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.