On the calibration of sensor arrays for pattern recognition using the minimal number of experiments

Published

October 26, 2013

Author(s)

Alexander (. Vergara Tinoco, Rodriguez-Lujan Irene, Huerta Ramon, Fonollosa Jordi, Margie Homer

Abstract

We investigate optimal experiment selection to train a classi er on gas sensor arrays to get the maximal possible performance in a limited number of experiments. In gas sensing, while collecting data for a particular sensor array, one has to choose what gas and concentration level is going to be presented in the next experiment. It is an active decision by the operator selecting the experiments and training the classi ers. Can the algorithm be trained sooner rather than later? Can we minimize the costs of collecting the data in terms of the man-hour of the operator and the expenses of the experiment itself? Active control sampling provides a way to deal with the challenge of minimizing the calibration costs and is applicable to any situation where experimental selection is parametrized by an external control variable. Our results indicate that active sampling strategies perform better than a random selection of experiments over a wide range of concentration of gases. However, random or uninformed selection is fairly close. When there is no prior knowledge about the range of concentrations to which the sensor will be exposed during real operation, it is especially important to include low concentrations in the calibration since the lack of these values dramatically decreases the performance of the system.

Citation

Chemometrics and Intelligent Laboratory Systems

Volume

130

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.chemolab.2013.10.012

Keywords

electronic nose, support vector machines, fast calibration, pattern recognition, gas discrimination, active learning

Citation

Vergara, A. , Irene, R. , Ramon, H. , Jordi, F. and Homer, M. (2013), On the calibration of sensor arrays for pattern recognition using the minimal number of experiments, Chemometrics and Intelligent Laboratory Systems, [online], https://doi.org/10.1016/j.chemolab.2013.10.012 (Accessed July 27, 2024)