Drift compensation of gas sensor array data by Orthogonal Signal Correction

Padilla, Marta; Perera, Alexandre; Montoliu, Ivan; Chaudry, A.; Persaud, Krishna; Marco, S.

doi:10.1016/j.chemolab.2009.10.002

Cited by 209 publications

(110 citation statements)

References 37 publications

Supporting

Mentioning

110

Contrasting

Order By: Relevance

“…Indeed, in spite of constant improvements in micro/nanofabrication techniques that allowed the production of sensing devices with superior stability, it is still impossible to fabricate chemical sensors without drift. This issue is currently approached on one hand by improving the sensors performances with novel sensing materials [59], on the other hand by adopting various approaches for compensating sensors drift to increase pattern recognition accuracy [60,61].…”

Section: Discussion and Issuesmentioning

confidence: 99%

“…These can be both thick-film commercial Taguchi sensors (TGS2611 and TGS2442 were employed for instance by Cagnasso et al [13]) or home-made thin-film sensors (further details can be found in Comini et al [60] and references therein) among which tin oxide, often catalyzed with noble metals (e.g., Au, Ag, Pt, Pd, or Mo), and other metal oxides like tungsten oxide are commonly used.…”

Section: Eos Electronic Nose Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Electronic Nose for Microbiological Quality Control of Food Products

Falasconi

Concina

Gobbi

et al. 2012

International Journal of Electrochemistry

View full text Add to dashboard Cite

Electronic noses (ENs) have recently emerged as valuable candidates in various areas of food quality control and traceability, including microbial contamination diagnosis. In this paper, the EN technology for microbiological screening of food products is reviewed. Four paradigmatic and diverse case studies are presented: (a)Alicyclobacillusspp. spoilage of fruit juices, (b) early detection of microbial contamination in processed tomatoes, (c) screening of fungal and fumonisin contamination of maize grains, and (d) fungal contamination on green coffee beans. Despite many successful results, the high intrinsic variability of food samples together with persisting limits of the sensor technology still impairs ENs trustful applications at the industrial scale. Both advantages and drawbacks of sensor technology in food quality control are discussed. Finally, recent trends and future directions are illustrated.

show abstract

Section: Discussion and Issuesmentioning

confidence: 99%

Section: Eos Electronic Nose Descriptionmentioning

confidence: 99%

Electronic Nose for Microbiological Quality Control of Food Products

Falasconi

Concina

Gobbi

et al. 2012

International Journal of Electrochemistry

View full text Add to dashboard Cite

show abstract

“…Nevertheless, these methods can sometimes mistakenly update the pattern by following the wrong reference class. Component Correction-(CC-) based methods are said to have good results [25][26][27]. However, they assume that the gases behave in a similar way in the drift process, while the truth is quite the opposite.…”

Section: Analytical Model For Drift Compensationmentioning

confidence: 99%

Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E‐Nose Systems

Ma¹,

Luo²,

Qin³

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Machine learning approaches have been widely used to tackle the problem of sensor array drift in E-Nose systems. However, labeled data are rare in practice, which makes supervised learning methods hard to be applied. Meanwhile, current solutions require updating the analytical model in an offline manner, which hampers their uses for online scenarios. In this paper, we extended Target Domain Adaptation Extreme Learning Machine (DAELM T) to achieve high accuracy with less labeled samples by proposing a Weighted Domain Transfer Extreme Learning Machine, which uses clustering information as prior knowledge to help select proper labeled samples and calculate sensitive matrix for weighted learning. Furthermore, we converted DAELM T and the proposed method into their online learning versions under which scenario the labeled data are selected beforehand. Experimental results show that, for batch learning version, the proposed method uses around 20% less labeled samples while achieving approximately equivalent or better accuracy. As for the online versions, the methods maintain almost the same accuracies as their offline counterparts do, but the time cost remains around a constant value while that of offline versions grows with the number of samples.

show abstract

“…The common methods used for extracting drift signals include Principal Component Analysis (PCA), (6)(7)(8) Independent Component Analysis (ICA), (9) and Partial Least-Squares (PLS). (10) Aside from these methods, the Orthogonal Signal Correction (OSC) is superior to drift correction as shown in a recent study by Padilla et al (11) Generally speaking, all the component correction methods aim to find one preferable direction to eliminate drift. However, the drift direction varies with time and gas type in the mapping space, (6) and consecutive groups of samples are needed for predicting each drift direction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Retraining Method of Multiple Self-Organizing Maps for Gas Sensor Drift Compensation

2013

Sensors and Materials

View full text Add to dashboard Cite

The drift effect of gas sensors severely affects the performance of an electronic nose, because the primarily built pattern recognition models degrade over time. A multiple self-organizing map (MSOM) network is an adaptive approach to compensate for gas sensor drift by self-retraining during the test phase. However, the conventional local retraining method of multiple self-organizing maps may lose drift information if the retraining is carried out with successive homogeneous samples for a long time. In this paper, we propose a novel global retraining method to keep each retraining vector (RV) fresh over time. Compared with the local retraining approach, the new method updates all the retraining vectors after one of them has been replaced. Experimental results demonstrate that the global retraining method retains the network recognition ability on drift effect, whereas the local retraining and adaptive resonance theory methods show high error rates. Finally, a discussion on the retraining rate is given to optimize the process speed of the MOSM network with the global retraining strategy.

show abstract

Drift compensation of gas sensor array data by Orthogonal Signal Correction

Cited by 209 publications

References 37 publications

Electronic Nose for Microbiological Quality Control of Food Products

Electronic Nose for Microbiological Quality Control of Food Products

Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E‐Nose Systems

A Novel Retraining Method of Multiple Self-Organizing Maps for Gas Sensor Drift Compensation

Contact Info

Product

Resources

About