Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze)

Kaur, Ramneek; Kumar, Ritesh; Gulati, Ashu; Ghanshyam, C.; Kapur, Pawan; Bhondekar, Amol P.

doi:10.1016/j.snb.2012.02.067

Cited by 50 publications

(30 citation statements)

References 35 publications

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“…The fundamentals of this method, and various applications for predicting chemical compound concentration, have been presented elsewhere [6,8,[11][12][13][14][15]. The algorithm attracted huge interest among scientists because it is based on a very simple idea and leads to high performance in numerous practical applications [14,[16][17][18]. The algorithm was developed originally for pattern recognition by learning from exemplary data belonging to two opposite sets.…”

Section: Svm Algorithmmentioning

confidence: 99%

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Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

Lentka¹,

Smulko²,

Ionescu³

et al. 2015

Metrology and Measurement Systems

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This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.

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Section: Svm Algorithmmentioning

confidence: 99%

“…There are many papers discussing the use of the SVM method for data processing in gas sensing [6,12,[16][17][18]. We should emphasize the necessity of careful selection of LS-SVM model parameters to obtain high accuracy of gas concentration prediction.…”

Section: Application Of the Ls-svm Algorithmmentioning

confidence: 99%

Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

Lentka¹,

Smulko²,

Ionescu³

et al. 2015

Metrology and Measurement Systems

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show abstract

“…The SVM method, proposed by Vapnik [30], has proved to be an useful tool in case of analyzing data from gas sensors [31,32,33]. The SVM maps the input data onto a higher dimensional feature space, which is non-linearly related to the input space.…”

Section: Pls-da and Svm Classifiersmentioning

confidence: 99%

Efficiency of Linear and Non-Linear Classifiers for Gas Identification from Electrocatalytic Gas Sensor

Kalinowski¹,

Woźniak²,

Strzelczyk³

et al. 2013

Metrology and Measurement Systems

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Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling.

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“…The general application areas are in samples of tea, juice (Liu et al 2013), wine and water containing HMI. Prominent works done by Bhondekar et al (2011), (Kaur et al 2012) and (Kumar et al 2012a, b) in the field of optimum classification of tea have used techniques such as social impact theory-based optimizer and support vector machines. Similarly, Gutiérrez et al (2011) used principal component analysis (PCA) and soft independent modeling by class analogy (SIMCA) for the quantification of grape varieties.…”

Section: Introductionmentioning

confidence: 99%

Classification of heavy metal ions present in multi-frequency multi-electrode potable water data using evolutionary algorithm

et al. 2016

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Access to potable water for the common people is one of the most challenging tasks in the present era. Contamination of drinking water has become a serious problem due to various anthropogenic and geogenic events. The paper demonstrates the application of evolutionary algorithms, viz., particle swan optimization and genetic algorithm to 24 water samples containing eight different heavy metal ions (Cd, Cu, Co, Pb, Zn, Ar, Cr and Ni) for the optimal estimation of electrode and frequency to classify the heavy metal ions. The work has been carried out on multi-variate data, viz., single electrode multi-frequency, single frequency multi-electrode and multi-frequency multi-electrode water samples. The electrodes used are platinum, gold, silver nanoparticles and glassy carbon electrodes. Various hazardous metal ions present in the water samples have been optimally classified and validated by the application of Davis Bouldin index. Such studies are useful in the segregation of hazardous heavy metal ions found in water resources, thereby quantifying the degree of water quality.

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Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze)

Cited by 50 publications

References 35 publications

Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

Efficiency of Linear and Non-Linear Classifiers for Gas Identification from Electrocatalytic Gas Sensor

Classification of heavy metal ions present in multi-frequency multi-electrode potable water data using evolutionary algorithm

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