Steady vs. Dynamic Contributions of Different Doped Conducting Polymers in the Principal Components of an Electronic Nose's Response

TL;DR

This study shows that in electronic noses, using early dynamic sensor data rather than steady-state responses can effectively classify gases, optimizing data collection for machine learning applications.

Contribution

It demonstrates that early dynamic responses are sufficient for gas classification, challenging the need for steady-state data in electronic nose systems.

Findings

Early dynamic data enables accurate gas classification.

Reducing data acquisition time does not compromise recognition accuracy.

New figure-of-merits are needed for sensor array evaluation in machine learning contexts.

Abstract

Multivariate data analysis and machine-learning classification become popular tools to extract features without physical models for complex environments recognition. For electronic noses, time sampling over multiple sensors must be a fair compromise between a period sufficiently long to output a meaningful information pattern, and sufficiently short to minimize training time for practical applications. Particularly when reactivity's kinetics differ from thermodynamics' in sensitive materials, finding the best compromise to get the most from data is not obvious. Here, we investigate on the influence of data acquisition to improve or alter data clustering for molecular recognition on a conducting polymer electronic nose. We found out that waiting for the sensors to reach their steady state is not required for classification, and that reducing data acquisition down to the first dynamical information suffice to recognize molecular gases by principal component analysis with the same materials. Particularly for online inference, this study shows that a good sensing array is no array of good sensors, and that new figure-of-merits shall be defined for sensing hardware aiming machine-learning pattern-recognition rather than metrology.