Optimized Gas Sensor Array with AI for Distinguishing and Classifying Similar Odorants

This study presents a novel approach for odorant classification by integrating advanced machine learning techniques with an electronic nose (e-nose) system. The system's performance was evaluated with four distinct volatile organic compounds (VOCs)-eucalyptol, 2-nonanone, eugenol, and 2-phenyle...

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Bibliographic Details
Published in:IEEE sensors journal p. 1
Main Authors: Cava, Carlos Eduardo, Sun, Helin, Huang, Shirong, Cuniberti, Gianaurelio
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Boosting Algorithms
Electronic Nose System
Gas Sensors
LDA
Machine Learning
Metal Oxide
Odorant Classification
Semiconductor
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:This study presents a novel approach for odorant classification by integrating advanced machine learning techniques with an electronic nose (e-nose) system. The system's performance was evaluated with four distinct volatile organic compounds (VOCs)-eucalyptol, 2-nonanone, eugenol, and 2-phenylethanol-along with odorless air as a reference. Data acquisition was performed using commercial gas sensors, and the collected VOC data were analyzed usingmultiple machine learning algorithms. Among these, Random Forest and CatBoost achieved the highest classification accuracy (97.1%), demonstrating superior performance in balancing precision and recall. Linear Discriminant Analysis (LDA) was employed to visualize odorant class separation in two and three dimensions, revealing distinct clusters with some overlap between eucalyptol and eugenol. Feature importance analysis identified key predictors for classification, while the confusion matrix highlighted potential areas for improvement, particularly in distinguishing chemically similar odors. The results indicate that the proposed system is highly effective for odorant discrimination, and further optimizations could enhance its accuracy in more complex classification scenarios.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2025.3596743