Evidence map›Paper›PMID 40830591›Full record

ReviewMetabolomics : Official journal of the Metabolomic Society2025

Analytical greenness metrics for metabolomics.

Ren-Qi Wang, Yun Wang, Juan-Na Song, Huai-Dong Yu, Xi-Zhi Niu, Elize Smit

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In one paragraph

Review in Metabolomics : Official journal of the Metabolomic Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ren-Qi WangSchool of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an, 710021, P. R. China. wangrq@sust.edu.cn.
Yun WangSchool of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an, 710021, P. R. China.
Juan-Na SongSchool of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an, 710021, P. R. China.
Huai-Dong YuGezhu Bio Co., Ltd, Beijing, 100037, P. R. China.
Xi-Zhi NiuEnvironmental Engineering Program, Department of Chemical and Environmental Engineering, University of Cincinnati, Cincinnati, OH, 45221, USA.
Elize SmitCenter for Natural Product Research, Department of Chemical Sciences, University of Johannesburg, Johannesburg, South Africa. esmit@uj.ac.za.ORCID http://orcid.org/0000-0001-8679-2034

Funding

State Tobacco Monopoly Administration 402023AWHZ03
6 · The paper itself

Abstract

backgroundMetabolomics is rapidly evolving, addressing analytical chemistry challenges in the qualification and quantitation of metabolites in extremely complex samples. Targeted metabolomics involves the extraction and analysis of target compounds, often present at extremely low concentrations, whilst untargeted metabolomics requires the use of sophisticated analytical techniques to deal with the simultaneous identification or quantitation of hundreds of compounds. Given the high energy consumption and excessive amounts of waste generated by metabolomics studies, greenness metrics are essential to account for sustainable development. AIM OF REVIEW: To determine the applicability of the Analytical GREEnness calculator (AGREE) in evaluating the analytical greenness of metabolomics methods. Specifically, the analytical protocols of 16 state-of-art metabolomics studies, including nine targeted and seven untargeted metabolomics studies, are fully dissected, and detailed greenness parameters for each procedure are rationally estimated. KEY SCIENTIFIC CONCEPTS OF REVIEW: The calculated AGREE metrics unequivocally show the main weaknesses of greenness in current research, and guidelines for sustainable practices in metabolomics are provided. The results indicate that offline sample preparation and the lack of automation and miniaturization are key areas that must be addressed to make metabolomics more sustainable. Important aspects that should be considered include the complexity of sample preparation procedures, the use of toxic reagents and derivatizing agents, the amount of waste generated, and sample throughput.

Indexed as

Green Chemistry TechnologyMetabolomicsHumansGreen analytical chemistryGreenness metricsMass spectrometryMetabolomicsNMR

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Registered trials

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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.