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Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/44906
Título

Multivariate curve resolution applied to temperature-modulated metal oxide gas sensors

AutorMontoliu, Ivan; Tauler, Romà; Padilla, Marta; Pardo, Antonio; Marco, Santiago
Palabras claveTemperature modulation
Multivariate curve resolution
MCR-ALS
Metal oxide sensors
Fecha de publicación2010
EditorElsevier
CitaciónSensors and Actuators - B - Chemical Biochemical Sensors
ResumenMetal oxide (MOX) gas sensors have been widely used for years. Temperature modulation of gas sensors is as an alternative to increase their sensitivity and selectivity to different gas species. In order to enhance the extraction of useful information from this kind of signals, data processing techniques are needed. In this work, the use of self-modelling curve resolution techniques, in particular multivariate curve resolution-alternating least squares (MCR-ALS), is presented for the analysis of these signals. First, the performance of MCR in a synthetic dataset generated from temperature-modulated gas sensor response models has been evaluated, showing good results both in the resolution of gas mixtures and in the determination of concentration/sensitivity profiles. Secondly, experimental confirmation of previously obtained conclusions is attempted using temperature-modulated MOX sensors together with MCR-ALS for the analysis of carbon monoxide (CO) and methane (CH4) gas mixtures in dry air. Results allow confirming the possibility of using the proposed approach as a quantitative technique for gas mixtures analysis, and also reveal some limitations.
Versión del editorhttp://dx.doi.org/10.1016/j.snb.2009.12.051
URIhttp://hdl.handle.net/10261/44906
DOI10.1016/j.snb.2009.12.051
ISSN0925-4005
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