To access the full text documents, please follow this link: http://hdl.handle.net/2445/142658

Title:	Multi-unit calibration rejects inherent device variability of chemical sensor arrays
Author:	Solórzano, Ana; Rodríguez Pérez, Raquel; Padilla, Marta; Graunke, Thorsten; Fernández, Luis; Marco Colás, Santiago; Fonollosa, Jordi
Abstract:	Inherent sensor variability limits mass-production applications for metal oxide (MOX) gas sensor arrays because calibration for replicas of a sensor array needs to be performed individually. Recently, calibration transfer strategies have been proposed to alleviate calibration costs of new replicas, but they still require the acquisition of transfer samples. In this work, we present calibration models that can be extended to uncalibrated replicas of sensor arrays without acquiring new samples, i.e., general or global calibration models. The developed methodology consists in including multiple replicas of a sensor array in the calibration process such that sensor variability is rejected by the general model. Our approach was tested using replicas of a MOX sensor array in the classification task of six gases and synthetic air, presented at different background humidity and concentration levels. Results showed that direct transfer of individual calibration models provides poor classification accuracy. However, we also found that general calibration models kept predictive performance when were applied directly to new copies of the sensor array. Moreover, we explored, through feature selection, whether particular combinations of sensors and operating temperatures can provide predictive performances equivalent to the calibration model with the complete array, favoring thereby the existence of more robust calibration models.
Subject(s):	-Dinàmica de gasos -Calibratge -Gas dynamics -Calibration
Rights:	cc-by-nc-nd (c) Elsevier B.V., 2018 info:eu-repo/semantics/embargoedAccess http://creativecommons.org/licenses/by-nc-nd/3.0/es
Document type:	Article Article - Accepted version
Published by:	Elsevier B.V.
Share:

Show full item record