Evidence map›Paper›PMID 35164176›Full record

ArticleMolecules (Basel, Switzerland)2022

Hydrophobic Eutectic Solvent-Based Dispersive Liquid-Liquid Microextraction Applied to the Analysis of Pesticides in Wine.

Chiara Dal Bosco, Francesca Mariani, Alessandra Gentili

Open access · goldAbstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2022. 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
2.6field-weighted citation impact, top 10% of its field
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, 27 citations in OpenAlex.

  1. Article
  2. Review
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  4. Article
  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

3 authors at 1 institution in 1 country.

Chiara Dal BoscoDepartment of Chemistry, Sapienza University of Rome, 00185 Rome, Italy.
Francesca MarianiDepartment of Chemistry, Sapienza University of Rome, 00185 Rome, Italy.
Alessandra GentiliDepartment of Chemistry, Sapienza University of Rome, 00185 Rome, Italy.
Sapienza University of Rome · IT

Funding

Sapienza University of Rome RG11916B6451D44A
6 · The paper itself

Abstract

A green solvent-based DLLME/HPLC-MS method for the determination of 19 pesticides in wine samples has been developed. The extractant solvent is a hydrophobic eutectic mixture composed of L-menthol and butylated hydroxytoluene in a molar ratio of 3:1. The endogenous ethanol of wine has been used as dispersive solvent, in order to avoid the solidification of the extracts under 19 °C. The mobile phase composition, the elution gradient and the sample injection volume were optimized in order to make this hydrophobic mixture compatible with conventional reversed phase chromatography and electrospray ionization. The method was validated in matrix, using a wine free from the target compounds. Average recovery as high as 80%, precision between 3 and 14%, and limits of detection and quantification much lower than the maximum residue levels (MRLs) for grapes and wines fixed by the EU regulation, make this multiresidue method fitted for the purpose, with the further advantages of being quick, cheap and in compliance with the green analytical chemistry. From the analysis of 11 commercial wines it was found that just in a bio sample the target compounds were not detectable or lower than quantification limit; as for the other samples, the most widespread and abundant pesticides were methoxyfenozide and boscalid, but their levels were much lower than the relative MRLs.

Indexed as

dispersive liquid–liquid microextractioneutectic solventLC-MSpesticideswine

Identifiers

PMID35164176
PMCPMC8839483
OpenAlexW4213136993

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.