ArticleSensors (Basel, Switzerland)2017
Determination of Odour Interactions of Three-Component Gas Mixtures Using an Electronic Nose.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Modelling the odor profile of fungal Solid-State Fermentation.NPJ science of food · 2026Article
- Odor-Based Nanomechanical Discrimination of Fuel Oils Using a Single Type of Designed Nanoparticles with Nonlinear Viscoelasticity.ACS omega · 2021Article
- On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WOSensors (Basel, Switzerland) · 2021Article
- A Smart Rig for Calibration of Gas Sensor Nodes.Sensors (Basel, Switzerland) · 2020Article
- Integrated model for estimating odor emissions from civil wastewater treatment plants.Environmental science and pollution research international · 2020Article
- Determination of Odor Intensity of Binary Gas Mixtures Using Perceptual Models and an Electronic Nose Combined with Fuzzy Logic.Sensors (Basel, Switzerland) · 2019Article
- Mixed Natural Gas Online Recognition Device Based on a Neural Network Algorithm Implemented by a FPGA.Sensors (Basel, Switzerland) · 2019Article
- Monitoring of Cell Concentration duringSensors (Basel, Switzerland) · 2019Article
- A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification.Sensors (Basel, Switzerland) · 2019Article
- Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine.Sensors (Basel, Switzerland) · 2018Article
- Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks.Sensors (Basel, Switzerland) · 2018Article
- Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The paper presents an application of an electronic nose prototype comprised of six TGS-type sensors and one PID-type sensor to identify odour interaction phenomena in odorous three-component mixtures. The investigation encompassed eight odorous mixtures-toluene-acetone-triethylamine and formaldehyde-butyric acid-pinene-characterized by different odour intensity and hedonic tone. A principal component regression (PCR) calibration model was used for evaluation of predicted odour intensity and hedonic tone. Correctness of identification of odour interactions in the odorous three-component mixtures was determined based on the results obtained with the electronic nose. The results indicated a level of 75-80% for odour intensity and 57-73% for hedonic tone. The average root mean square error of prediction amounted to 0.03-0.06 for odour intensity determination and 0.07-0.34 for hedonic tone evaluation of the odorous three-component mixtures.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.