Comparison of machine learning algorithms for concentration detection and prediction of formaldehyde based on electronic nose

Abstract

Purpose

Sensor arrays and pattern recognition-based electronic nose (E-nose) is a typical detection and recognition instrument for indoor air quality (IAQ). The E-nose is able to monitor several pollutants in the air by mimicking the human olfactory system. Formaldehyde concentration prediction is one of the major functionalities of the E-nose, and three typical machine learning (ML) algorithms are most frequently used, including back propagation (BP) neural network, radial basis function (RBF) neural network and support vector regression (SVR).

Design/methodology/approach

This paper comparatively evaluates and analyzes those three ML algorithms under controllable environment, which is built on a marketable sensor arrays E-nose platform. Variable temperature (T), relative humidity (RH) and pollutant concentrations (C) conditions were measured during experiments to support the investigation.

Findings

Regression models have been built using the above-mentioned three typical algorithms, and in-depth analysis demonstrates that the model of the BP neural network results in a better prediction performance than others.

Originality/value

Finally, the empirical results prove that ML algorithms, combined with low-cost sensors, can make high-precision contaminant concentration detection indoor.

Citation

Xu, L., He, J., Duan, S., Wu, X. and Wang, Q. (2016), "Comparison of machine learning algorithms for concentration detection and prediction of formaldehyde based on electronic nose", Sensor Review, Vol. 36 No. 2, pp. 207-216. https://doi.org/10.1108/SR-07-2015-0104