Open AccessImpact of a Diverse Combination of Metal Oxide Gas Sensors on Machine Learning-Based Gas Recognition in Mixed Gases
Author(s) -
Garam Bae,
Minji Kim,
Wooseok Song,
Sung Myung,
Su Yeon Lee,
KiSeok An
Publication year - 2021
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.1c02721
Subject(s) - selectivity , support vector machine , work (physics) , correlation , biological system , computer science , artificial intelligence , chemistry , mathematics , engineering , mechanical engineering , biochemistry , geometry , biology , catalysis
A challenge for chemiresistive-type gas sensors distinguishing mixture gases is that for highly accurate recognition, massive data processing acquired from various types of sensor configurations must be considered. The impact of data processing is indeed ineffective and time-consuming. Herein, we systemically investigate the effect of the selectivity for a target gas on the prediction accuracy of gas concentration via machine learning based on a support vector machine model. The selectivity factor S (X) of a gas sensor for a target gas "X" is introduced to reveal the correlation between the prediction accuracy and selectivity of gas sensors. The presented work suggests that (i) the strong correlation between the selectivity factor and prediction accuracy has a proportional relationship, (ii) the enhancement of the prediction accuracy of an elemental sensor with a low sensitivity factor can be attained by a complementary combination of the other sensor with a high selectivity factor, and (iii) it can also be boosted by combining the sensor having even a low selectivity factor.