ArticleSensors (Basel, Switzerland)2017
The Regular Interaction Pattern among Odorants of the Same Type and Its Application in Odor Intensity Assessment.
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.
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Who cites it
12 citing papers in PubMed.
- Utilisation of QSPR ODT modelling and odour vector modelling to predict Cannabis sativa odour.PloS one · 2023Article
- Odor-Based Nanomechanical Discrimination of Fuel Oils Using a Single Type of Designed Nanoparticles with Nonlinear Viscoelasticity.ACS omega · 2021Article
- Objective odor analysis of incidentally emitted compounds using the Langage des Nez® method: application to the industrial zone of Le Havre.Environmental science and pollution research international · 2021Article
- A Multi-Sensor System for Sea Water Iodide Monitoring and Seafood Quality Assurance: Proof-of-Concept Study.Sensors (Basel, Switzerland) · 2021Article
- Visual Analysis of Odor Interaction Based on Support Vector Regression Method.Sensors (Basel, Switzerland) · 2020Article
- Evaluation of Perceptual Interactions between Ester Aroma Components in Langjiu by GC-MS, GC-O, Sensory Analysis, and Vector Model.Foods (Basel, Switzerland) · 2020Article
- Integrated model for estimating odor emissions from civil wastewater treatment plants.Environmental science and pollution research international · 2020Article
- 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
- Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks.Sensors (Basel, Switzerland) · 2018Article
- Complementary use of GCxGC-TOF-MS and statistics for differentiation of variety in biosolid samples.Monatshefte fur chemie · 2018Article
- Determination of Odour Interactions of Three-Component Gas Mixtures Using an Electronic Nose.Sensors (Basel, Switzerland) · 2017Article
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Authors and funding
5 authors.
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Abstract
The olfactory evaluation function (e.g., odor intensity rating) of e-nose is always one of the most challenging issues in researches about odor pollution monitoring. But odor is normally produced by a set of stimuli, and odor interactions among constituents significantly influenced their mixture's odor intensity. This study investigated the odor interaction principle in odor mixtures of aldehydes and esters, respectively. Then, a modified vector model (MVM) was proposed and it successfully demonstrated the similarity of the odor interaction pattern among odorants of the same type. Based on the regular interaction pattern, unlike a determined empirical model only fit for a specific odor mixture in conventional approaches, the MVM distinctly simplified the odor intensity prediction of odor mixtures. Furthermore, the MVM also provided a way of directly converting constituents' chemical concentrations to their mixture's odor intensity. By combining the MVM with usual data-processing algorithm of e-nose, a new e-nose system was established for an odor intensity rating. Compared with instrumental analysis and human assessor, it exhibited accuracy well in both quantitative analysis (Pearson correlation coefficient was 0.999 for individual aldehydes (
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