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Open AccessArticle

Visual Analysis of Odor Interaction Based on Support Vector Regression Method

1
School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1707; https://doi.org/10.3390/s20061707
Received: 18 February 2020 / Revised: 8 March 2020 / Accepted: 16 March 2020 / Published: 19 March 2020
(This article belongs to the Special Issue Electronic Noses)
The complex odor interaction between odorants makes it difficult to predict the odor intensity of their mixtures. The analysis method is currently one of the factors limiting our understanding of the odor interaction laws. We used a support vector regression algorithm to establish odor intensity prediction models for binary esters, aldehydes, and aromatic hydrocarbon mixtures, respectively. The prediction accuracy to both training samples and test samples demonstrated the high prediction capacity of the support vector regression model. Then the optimized model was used to generate extra odor data by predicting the odor intensity of more simulated samples with various mixing ratios and concentration levels. Based on these olfactory measured and model predicted data, the odor interaction was analyzed in the form of contour maps. This intuitive method showed more details about the odor interaction pattern in the binary mixture. We found that that the antagonism effect was commonly observed in these binary mixtures and the interaction degree was more intense when the components’ mixing ratio was close. Meanwhile, the odor intensity level of the odor mixture barely influenced the interaction degree. The machine learning algorithms were considered promising tools in odor researches. View Full-Text
Keywords: odor intensity; odor evaluation; machine learning; prediction model; antagonism effect odor intensity; odor evaluation; machine learning; prediction model; antagonism effect
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MDPI and ACS Style

Yan, L.; Wu, C.; Liu, J. Visual Analysis of Odor Interaction Based on Support Vector Regression Method. Sensors 2020, 20, 1707. https://doi.org/10.3390/s20061707

AMA Style

Yan L, Wu C, Liu J. Visual Analysis of Odor Interaction Based on Support Vector Regression Method. Sensors. 2020; 20(6):1707. https://doi.org/10.3390/s20061707

Chicago/Turabian Style

Yan, Luchun; Wu, Chuandong; Liu, Jiemin. 2020. "Visual Analysis of Odor Interaction Based on Support Vector Regression Method" Sensors 20, no. 6: 1707. https://doi.org/10.3390/s20061707

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