Evidence map›Paper›PMID 30830349›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2018

Neuronal network analyses reveal novel associations between volatile organic compounds and sensory properties of tomato fruits.

Pablo R Cortina, Ana N Santiago, María M Sance, Iris E Peralta, Fernando Carrari, Ramón Asis

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

12 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Article
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  7. Article
  8. Comparing the Flavor Characteristics of 71 Tomato (Frontiers in plant science · 2020
    Article
  9. Article
  10. The application of artificial neural networks in metabolomics: a historical perspective.Metabolomics : Official journal of the Metabolomic Society · 2019
    Review
  11. Crop metabolomics: from diagnostics to assisted breeding.Metabolomics : Official journal of the Metabolomic Society · 2018
    Review
  12. Production system influences volatile biomarkers in tomato.Metabolomics : Official journal of the Metabolomic Society · 2018
    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 at 4 institutions in 2 countries.

Pablo R CortinaINFIQC, Departamento de Química Orgánica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, 5000, Córdoba, Argentina.
Ana N SantiagoINFIQC, Departamento de Química Orgánica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, 5000, Córdoba, Argentina.
María M SanceIADIZA, CCT-CONICET Mendoza, Parque General San Martín, 5500, Mendoza, Argentina.
Iris E PeraltaIADIZA, CCT-CONICET Mendoza, Parque General San Martín, 5500, Mendoza, Argentina.
Fernando CarrariInstituto de Biotecnología, Instituto Nacional de Tecnología Agropecuaria (IB-INTA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), PO Box 25, B1686WAA, Castelar, Argentina.
Ramón AsisCIBICI, Departamento de Bioquímica Clínica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, 5000, Córdoba, Argentina. rasis@fcq.unc.edu.ar.ORCID 0000-0001-9177-2214
Universidad Nacional de Córdoba · ARCentro Científico Tecnológico - Mendoza · ARConsejo Nacional de Investigaciones Científicas y Técnicas · ARUniversidad Nacional de Cuyo · AR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe process of tomato (Solanum lycopersicum) breeding has affected negatively the fruit organoleptic properties and this is evident when comparing modern cultivars with heirloom varieties. Flavor of tomato fruit is determined by a complex combination of volatile and nonvolatile metabolites that is not yet understood.

objectivesThe aim of this work was to provide an alternative approach to exploring the relationship between tomato odour/taste and volatile organic compounds (VOCs).

methodsVOC composition and organoleptic properties of seven Andean tomato landraces along with an edible wild species (Solanum pimpinellifolium) and four commercial varieties were characterized. Six hedonic traits were analyzed by a semitrained sensory panel to describe the organoleptic properties. Ninety-four VOCs were analyzed by headspace solid phase microextraction/gas chromatography-mass spectrometry (HS/SPME/GC-MS). The relationship between sensory data and VOCs was explored using an Artificial Neural Networks model (Kohonen Self Organizing Maps, omeSOM). RESULTS AND

conclusionThe results showed a strong preference by panelists for tomatoes of landraces than for commercial varieties and wild species. The predictive analysis by omeSOM showed 15 VOCs significantly associated to the typical and atypical tomato odour and taste. Moreover, omeSOM was used to predict the relationship of VOC ratios with sensory data. A total of 108 VOC ratios out of 8837 VOC ratios were predicted to be contributing to the typical and atypical tomato odour and taste. The metabolic origin of these flavor-associated VOCs and the metabolic point or target for breeding strategies were discussed.

Indexed as

AdultColombiaFemaleFlavoring AgentsFruitGas Chromatography-Mass SpectrometryHumansMaleMiddle AgedNeural Networks, ComputerOdorantsPlant BreedingPrincipal Component AnalysisSolanum lycopersicumSolid Phase MicroextractionTasteFlavoring AgentsVolatile Organic CompoundsArtificial neural networkGC–MSSPMETomato flavorVOC

Identifiers

PMID30830349
OpenAlexW2795289519

What OpenQuestion holds

Textmetadata
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.