Drift correction for gas sensors using multivariate methods

Artursson, Tom; Eklöv, Tomas; Lundström, Ingemar; Mårtensson, Per; Sjöstróm, Michael; Holmberg, Martin

doi:10.1002/1099-128x(200009/12)14:5/6<711::aid-cem607>3.3.co;2-w

Cited by 98 publications

(81 citation statements)

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“…Long term instability can be improved with several drift compensation methods [10,17] like baseline manipulation [18], periodic calibration [19,20] or attuning methods [15]. Nevertheless, the study of sensor drift is still a challenging task for the chemical sensor community [11].…”

Section: Introductionmentioning

confidence: 99%

Improving Short Term Instability for Quantitative Analyses with Portable Electronic Noses

Macías

Agudo

García‐Manso

et al. 2014

Sensors

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One of the main problems when working with electronic noses is the lack of reproducibility or repeatability of the sensor response, so that, if this problem is not properly considered, electronic noses can be useless, especially for quantitative analyses. On the other hand, irreproducibility is increased with portable and low cost electronic noses where laboratory equipment like gas zero generators cannot be used. In this work, we study the reproducibility of two portable electronic noses, the PEN3 (commercial) and CAPINose (a proprietary design) by using synthetic wine samples. We show that in both cases short term instability associated to the sensors' response to the same sample and under the same conditions represents a major problem and we propose an internal normalization technique that, in both cases, reduces the variability of the sensors' response. Finally, we show that the normalization proposed seems to be more effective in the CAPINose case, reducing, for example, the variability associated to the TGS2602 sensor from 12.19% to 2.2%.

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Section: Introductionmentioning

confidence: 99%

Improving Short Term Instability for Quantitative Analyses with Portable Electronic Noses

Macías

Agudo

García‐Manso

et al. 2014

Sensors

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“…Moreover, they can also be used to correct drift and nonlinearity issues of the sensors during the modelling stage (Artursson et al 2000;Gómez and Callao 2008).…”

Section: (Bio)sensor Arraysmentioning

confidence: 99%

Bioelectronic tongues: New trends and applications in water and food analysis

Cetó

Voelcker

Prieto‐Simón

2016

Biosensors and Bioelectronics

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“…Irrespective of the signal readout from the sensor (steady-state vs. transient or time-domain vs. frequency domain), another issue faced by almost all chemical sensors that severely limits their long-term use is the issue of sensor drift, or deviation of the response, over time[3, 7, 26, 45-49]. Drift in artificial chemical sensors has been suggested to primarily be an effect of aging or poisoning of the sensing film[5].…”

Section: Introductionmentioning

confidence: 99%

The I/O transform of a chemical sensor

Katta

Meier

Benkstein

et al. 2016

Sensors and Actuators B: Chemical

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A number of sensing technologies, using a variety of transduction principles, have been proposed for non-invasive chemical sensing. A fundamental problem common to all these sensing technologies is determining what features of the transducer's signal constitute a chemical fingerprint that allows for precise analyte recognition. Of particular importance is the need to extract features that are robust with respect to the sensor's age or stimulus intensity. Here, using pulsed stimulus delivery, we show that a sensor's operation can be modeled as a linear input-output (I/O) transform. The I/O transform is unique for each analyte and can be used to precisely predict a temperature-programmed chemiresistor's response to the analyte given the recent stimulus history (i.e. state of an analyte delivery valve being open or closed). We show that the analyte specific I/O transforms are to a certain degree stimulus intensity invariant and can remain consistent even when the sensor has undergone considerable aging. Significantly, the I/O transforms for a given analyte are highly conserved across sensors of equal manufacture, thereby allowing training data obtained from one sensor to be used for recognition of the same set of chemical species with another sensor. Hence, this proposed approach facilitates decoupling of the signal processing algorithms from the chemical transducer, a key advance necessary for achieving long-term, non-invasive chemical sensing.

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Drift correction for gas sensors using multivariate methods

Cited by 98 publications

References 0 publications

Improving Short Term Instability for Quantitative Analyses with Portable Electronic Noses

Improving Short Term Instability for Quantitative Analyses with Portable Electronic Noses

Bioelectronic tongues: New trends and applications in water and food analysis

The I/O transform of a chemical sensor

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