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>3.0.co;2-4

Cited by 213 publications

(88 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in 2000 [5]. The drift component of variance is calculated as the principal component p (or for several components P ) of a certain class, namely the reference gas.…”

Section: Component Correction Methodsmentioning

confidence: 99%

“…However multivariate methods capture more complex or non-linear drift effects using the information from several sensors in order to model the drift, at the cost of increasing the number of the parameters involved in the correction. Different multivariate methods based on adaptive filters, Component Correction and System Identification theory can be found in the literature [3][4][5]. Most of the methods are linear, which allows capturing the most drift variance component, but more complex non-linear approaches have also been reported [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…This paper proposes a new multivariate method based on Common Principal Component Analysis (CPCA) for drift compensation. The method is compared with respect to the well-established approach of Arthursson et al as both methods compensate the linear component drift in data by employing a Component Correction (CC) routine [5]. The performance of the algorithms is evaluated on a classification task, with a special attention given to the determination of the variance component of drift, which is a critical point of the methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Drift compensation of gas sensor array data by common principal component analysis

Ziyatdinov

Marco

Chaudry

et al. 2010

Sensors and Actuators B: Chemical

187

View full text Add to dashboard Cite

A new drift compensation method based on Common Principal Component Analysis (CPCA) is proposed. The drift variance in data is found as the principal components computed by CPCA. This method finds components that are common for all gasses in feature space. The method is compared in classification task with respect to the other approaches published where the drift direction is estimated through a Principal Component Analysis (PCA) of a reference gas. The proposed new method -employing no specific reference gas, but information from all gases -has shown the same performance as the traditional approach with the best-fitted reference gas. Results are shown with data lasting 7-months including three gases at different concentrations for an array of 17 polymeric sensors.

show abstract

“…in 2000 [5]. The drift component of variance is calculated as the principal component p (or for several components P ) of a certain class, namely the reference gas.…”

Section: Component Correction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Drift compensation of gas sensor array data by common principal component analysis

Ziyatdinov

Marco

Chaudry

et al. 2010

Sensors and Actuators B: Chemical

187

View full text Add to dashboard Cite

show abstract

“…Artursson proposed in 2001 the Component Correction method (CC) [22]. It is a signal processing technique based on a Principal Component Analysis (PCA) decomposition of a reference class data subset that is later used to correct undesired variance from the rest of the dataset.…”

Section: Component Correctionmentioning

confidence: 99%

“…Provided that the separation of drift from real responses is feasible, this procedure improves posterior classification or regression tasks. Linear methods like Component Correction (CC) based on Principal Component Analysis (PCA) [22,23], or Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS) [24] have also been reported to provide good results. In particular, Component Correction has received considerable attention in the community.…”

Section: Introductionmentioning

confidence: 99%

Drift compensation of gas sensor array data by Orthogonal Signal Correction

Padilla

Perera

Montoliu

et al. 2010

Chemometrics and Intelligent Laboratory Systems

210

103

View full text Add to dashboard Cite

Drift is an important issue that impairs the reliability of gas sensing systems. Sensor aging, memory effects and environmental disturbances produce shifts in sensor responses that make initial statistical models for gas or odor recognition useless after a relatively short period (typically few weeks). Frequent recalibrations are needed to preserve system accuracy. However, when recalibrations involve numerous samples they become expensive and laborious. An interesting and lower cost alternative is drift counteraction by signal processing techniques. Orthogonal Signal Correction (OSC) is proposed for drift compensation in chemical sensor arrays. The performance of OSC is also compared with Component Correction (CC). A simple classification algorithm has been employed for assessing the performance of the algorithms on a dataset composed by measurements of three analytes using an array of seventeen conductive polymer gas sensors over a ten month period.

show abstract

Interest in the Use of an Electronic Nose for Field Monitoring of Odors in the Environment

Nicolas

2004

Water Encyclopedia

View full text Add to dashboard Cite

An electronic nose is a promising technique for field monitoring of malodors in the environment. It is based on an array of nonspecific gas sensors coupled with some form of mathematical data processing method to analyze global response patterns. Some applications concerning the control of water quality are reported in the literature. They often imply the generation of a headspace gas above liquid samples, but they also concern the odor emitted in the air from wastewater. The typical needs of the final user require both qualitative and quantitative approaches, that is, both discriminating between slight variations in complex gas mixtures and monitoring a global odor signal that can be correlated with classical characterization variables of water or air quality. Although potential applications in odor assessment by electronic noses are numerous, a number of challenges still need to be overcome: understanding and controlling the impact of environmental parameters (such as temperature and humidity), improving sensor sensitivity and noise reduction, and developing calibration procedures to account for sensor drift. The present limitations of electronic nose technology restrict its use in the environment to odor monitoring in the surroundings of the emission by instruments specifically designed for the application.

show abstract

Drift correction for gas sensors using multivariate methods

Cited by 213 publications

References 13 publications

Drift compensation of gas sensor array data by common principal component analysis

Drift compensation of gas sensor array data by common principal component analysis

Drift compensation of gas sensor array data by Orthogonal Signal Correction

Interest in the Use of an Electronic Nose for Field Monitoring of Odors in the Environment

Contact Info

Product

Resources

About