Impact of a Diverse Combination of Metal Oxide Gas Sensors on Machine Learning-Based Gas Recognition in Mixed Gases

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.

Automated summary

This study systematically investigates the impact of selectivity for a target gas on the prediction accuracy of gas concentration using machine learning. The results show a proportional relationship between selectivity factor and prediction accuracy, suggesting that combining sensors with different selectivity factors can enhance the prediction accuracy.