The use of an electronic nose to characterize emissions from a highly polluted river

Lamagna, A.; Reich, S.L.; Rodríguez, Daniel García; Boselli, A.; Cicerone, Daniel

doi:10.1016/j.snb.2007.12.026

Cited by 55 publications

(23 citation statements)

References 7 publications

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“…They are effective in dealing with odor analysis problems [1,2,3], and have been introduced to many fields such as environmental monitoring [4,5], food engineering [6,7,8], disease diagnosis [9,10,11,12], explosives detection [13] and spaceflight applications [14]. …”

Section: Introductionmentioning

confidence: 99%

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

Jia

Huang

Duan

et al. 2016

Sensors

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When an electronic nose (E-nose) is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde). Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training) is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.

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Section: Introductionmentioning

confidence: 99%

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

Jia

Huang

Duan

et al. 2016

Sensors

View full text Add to dashboard Cite

show abstract

“…This approach has the advantage of non-destructive analysis because this method only evaluates the eigen gases emitted by samples [8]. E-nose has been widely used in abundant occasions, such as agriculture [9], pharmaceutical [10], environmental control [11][12][13], and clinical diagnostics [14,15]. Also, e-nose has been widely applied in aquatic product quality analysis.…”

Section: Introductionmentioning

confidence: 99%

Rapid freshness analysis of mantis shrimps (Oratosquilla oratoria) by using electronic nose

Jiang

Zheng

et al. 2015

Food Measure

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In this paper, rapid freshness analysis method of mantis shrimps (MSs) (Oratosquilla oratoria) by using electronic nose (e-nose) was investigated. Shrimps were stored at 4°and -20°. E-nose responses to samples were measured. Meanwhile, appearance of MS was recorded. Total volatile basic nitrogen (TVB-N) index was examined to provide a standard freshness indicator for samples according to Chinese national standard protocol. E-nose measurement data was processed by principal component analysis (PCA) and non-linear bistable stochastic resonance (SR). Experimental results demonstrated that e-nose sensitively responded to shrimps. PCA results failed to discriminate all shrimps. SR signal-to-noise ratio (SNR) spectrum successfully discriminated all shrimp samples stored at 4°and -20°. E-nose MS freshness degree forecasting models were developed using SNR maximums. Combining TVB-N examination results, the forecasting accuracy of developed freshness analysis model is 91.67 %. The proposed method had some advantages including rapid analysis, easy operating, non-destructive, low cost, etc.

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“…It is effective in dealing with problems about odor analysis (Ciosek and Wroblewski, 2006;Bicego et al, 2002) and has already been introduced to food engineering (Cevoli et al, 2011;Trihaas and Nielsen, 2005;Di Natale et al, 1997), disease diagnosis (D'Amico et al, 2008;Anh et al, 2005;Yu et al, 2005;Pavlou et al, 2002) and environmental controls (Ameer and Adeloju, 2005;Lamagna et al, 2008), etc.…”

Section: Introductionmentioning

confidence: 99%

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

et al. 2014

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Purpose -The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array's optimization and parameters' setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach -An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings -The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier's parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications -To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications -In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value -The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

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The use of an electronic nose to characterize emissions from a highly polluted river

Cited by 55 publications

References 7 publications

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

Rapid freshness analysis of mantis shrimps (Oratosquilla oratoria) by using electronic nose

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

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