Evidence map›Paper›PMID 37725115›Full record

ArticleAnalytical and bioanalytical chemistry2024

Suspect screening for chemical residues in aquacultured shrimp and fish using liquid chromatography-high resolution mass spectrometry: comparison of data evaluation approaches.

Sherri B Turnipseed, Christine R Casey

Abstract read
In one paragraph

Article in Analytical and bioanalytical chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

2 authors.

Sherri B TurnipseedAnimal Drugs Research Center, Office of Regulatory Affairs, U.S. Food and Drug Administration, P.O. Box 25087, Denver, CO, 80225-0087, USA. Sherri.Turnipseed@fda.hhs.gov.ORCID http://orcid.org/0000-0002-0556-7781
Christine R CaseyDenver Laboratory, Office of Regulatory Affairs, U.S. Food and Drug Administration, P.O. Box 25087, Denver, CO, 80225-0087, USA.

Funding

Intramural FDA HHS FD999999
6 · The paper itself

Abstract

High-resolution mass spectrometry (HRMS) has become an important tool for monitoring chemical residues in food, but the time and effort required to evaluate the large amount of data generated by HRMS can be a limiting factor in the widespread application of this tool. Suspect screening, i.e., searching HRMS data against large compound databases or mass lists, represents a practical compromise between using HRMS data to only look for target compounds and performing full non-target analysis. Several different approaches for suspect screening using HRMS data were tested using data from shrimp and eel spiked with veterinary drugs and pesticides as well as from imported aquaculture samples. Most of the analytes (>70%) in the spiked samples were detected and identified by searching against compound databases. To query larger databases and on-line resources such as mzCloud, it was necessary to use software capable of differential analysis and selective filtering, such as Compound Discoverer. Using selective filtering, the number of compounds detected in fish sample extracts could be reduced from tens of thousands to a few hundred by subtracting method blanks and comparing to matrix blank extracts. This smaller list of potential compounds could be further evaluated and compared to available databases and libraries. Analysis of imported aquaculture samples resulted in detection of unexpected contaminants including the dewormer levamisole, the insecticide buprofezin, and potentially the plant alkaloid ricinine.

Indexed as

FishesTandem Mass SpectrometryAnimalsChromatography, High Pressure LiquidLiquid Chromatography-Mass SpectrometrySeafoodAquacultureChemical residuesData evaluationLC-HRMSSuspect screening

Identifiers

PMID37725115
PMCPMC10984254

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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.