Evidence map›Paper›PMID 37447662›Full record

ArticleSensors (Basel, Switzerland)2023

Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques.

Sandra Viciano-Tudela, Lorena Parra, Paula Navarro-Garcia, Sandra Sendra, Jaime Lloret

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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
1.9field-weighted citation impact, top 15% 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

3 citing papers in PubMed, 17 citations in OpenAlex.

  1. Molecules (Basel, Switzerland) · 2025
    Review
  2. Article
  3. 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

5 authors at 1 institution in 1 country.

Sandra Viciano-TudelaInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain.ORCID 0000-0001-6621-0148
Lorena ParraInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain.ORCID 0000-0001-9215-8734
Paula Navarro-GarciaInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain.
Sandra SendraInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain.ORCID 0000-0001-9556-9088
Jaime LloretInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain.ORCID 0000-0002-0862-0533
Universitat Politècnica de València · ES

Funding

Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-114467RR-C33/AEI/10.13039/501100011033
6 · The paper itself

Abstract

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including

Indexed as

Oils, VolatileAlgorithmsBayes TheoremMachine LearningNeural Networks, ComputerSupport Vector MachineOils, Volatileadulterationartificial neural networkclassification algorithmsdiscriminant analysiseNosek-nearest neighboursMQ sensorsmulticlass classificationnaive Bayes classifiersupport vector machine

Identifiers

PMID37447662
PMCPMC10347007
OpenAlexW4381800654

What OpenQuestion holds

Textmetadata
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.