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Affecting Factors of the Electronic Nose Performance and Application in Human Odor Recognition
MAO Liwen, SONG Ge, DONG Linpei, REN Xinxin, HU Qingmin, XU Jiaqiang
2025(1):
45-54.
DOI: 10.3969/j.issn.1671-2072.2025.01.006
Objective This study aims to evaluate the performance of a self-developed electronic nose under different experimental conditions and explore its application in human odor recognition, particularly for gender classification using convolutional neural network (CNN) algorithms. Methods Firstly, the performance of the electronic nose was evaluated using acetone, ethanol, and isoprene as target gases with a concentration of 2×10⁻⁶ mol/m³, under gas flow rates ranging from 500 to 2 500 mL/min. Multiple factors, including flow rate, enrichment time, storage medium, and storage time, were considered in the experiments. Secondly, odor samples were collected from 25 male and 25 female participants, and gender classification of the gas samples was performed using feature data obtained from the electronic nose, combined with a CNN algorithm. Results The electronic nose performed best at gas flow rates of 1 500-2 000 mL/min. The use of aluminum foil bags resulted in minimal gas attenuation and better preservation. The optimal enrichment time for detection was 10 minutes. The gender classification accuracy based on hand and foot odors was as follows: left hand 96.55%, right hand 93.10%, left foot 100%, and right foot 96.55%. Conclusion This study demonstrates that optimizing experimental conditions significantly improves the performance of the electronic nose. Furthermore, the integration of CNN algorithms proves effective in human odor recognition, achieving high classification accuracy and providing a feasible solution for practical application of electronic noses in human odor analysis.
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