Evidence map›Paper›PMID 30100921›Full record

ArticlePlant methods2018

A comparison of semi-quantitative methods suitable for establishing volatile profiles.

Victoria Ruiz-Hernández, María José Roca, Marcos Egea-Cortines, Julia Weiss

Abstract read
In one paragraph

Article in Plant methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

23 citing papers in PubMed.

  1. Article
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  4. Chitosan Coatings with Essential Oil ofFoods (Basel, Switzerland) · 2026
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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

4 authors.

Victoria Ruiz-Hernández1Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Cartagena, Spain.
María José Roca3Servicio de Apoyo a la Investigación Tecnológica, Universidad Politécnica de Cartagena, Cartagena, Spain.
Marcos Egea-Cortines1Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Cartagena, Spain.
Julia Weiss1Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Cartagena, Spain.ORCID 0000-0003-2432-2613

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFull scent profiles emitted by living tissues can be screened by using total ion chromatograms generated in full scan mode and gas chromatography-mass spectrometry technique using Headspace Sorptive Extraction. This allows the identification of specific compounds and their absolute quantification or relative abundance. Quantifications ideally should be based on calibration curves using standards for each compound. However, the unpredictable composition of Volatile Organic Compounds (VOCs) and lack of standards make this approach difficult. Researchers studying scent profiles therefore concentrate on identifying specific scent footprints i.e. relative abundance rather than absolute quantities. We compared several semi-quantitative methods: external calibration curves generated in the sampling system and by liquid addition of standards to stir bars, total integrated peak area per fresh weight (FW), normalized peak area per FW, semi-quantification based on internal standard abundance, semi-quantification based on the nearest

resultsAny of the semi-quantification methods based on a standard produced similar or even identical results compared to quantification by a true-standard for a compound, except for the method based on standard addition. Each method beholds advantages and disadvantages regarding level of accuracy, experimental variability, acceptance and retrieved quantities.

conclusionsOur data shows that, except for the method of standard addition to the biological sample, the rest of the semi-quantification methods studied give highly similar statistical results. Any of the methodologies presented here can therefore be considered as valid for scent profiling. Regarding relative proportions of VOCs, the generation of calibration curves for each compound analysed is not necessary.

Indexed as

Calibration curveGC–MSHSSEInternal standardQuantificationScent profileStir barsTwistersVOCs

Identifiers

PMID30100921
PMCPMC6083509

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LicenceCC BY
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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.