Application of the wavelet transform coupled with artificial neural networks for quantification purposes in a voltammetric electronic tongue

Moreno‐Barón, Laura; Cartas, R.; Merkoçi, Arben; Alegret, Salvador; Valle, Manel del; Leija, L.; Hernández, P.R.; Muñoz, Robert

doi:10.1016/j.snb.2005.03.063

Cited by 84 publications

(42 citation statements)

References 39 publications

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“…In this sense, the voltammograms should be firstly analyzed in order to corroborate its analytical content. In order to reduce the huge data amount generated in each measurement, a preprocessing stage employing DWT was used [29]. In this way, the compression of the original sensor information was achieved up to 97.34 % without any lose of relevant information using Daubechies wavelet and a fourth decomposition level.…”

Section: Electronic Tongue Preliminary Recogntitionmentioning

confidence: 99%

Voltammetric Electronic Tongue in the Analysis of Cava Wines

et al. 2010

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This work demonstrates the use of a voltammetric electronic tongue formed by five modified graphite-epoxy electrodes in the qualitative and quantitative analysis of cava wines. The different samples were analyzed using cyclic voltammetry without any sample pretreatment. Recorded data were evaluated by Principal Component Analysis and Discrete Wavelet Transform in order to compress and extract significant features from the voltammetric signals. The preprocessed information was evaluated by an Artificial Neural Network that accomplishes the qualitative classification. Moreover, a preliminary study related to the quantification of sugar amount present was assessed by Second-Order Standard Addition Method.

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Section: Electronic Tongue Preliminary Recogntitionmentioning

confidence: 99%

Voltammetric Electronic Tongue in the Analysis of Cava Wines

et al. 2010

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“…A compression ratio of ca. 10 demonstrated to be the best choice to deconvolute the voltammograms, in cases with high difficulty, as the signal corresponded to the overlapped individual signal plus noise and the oxidation of containing media [71].…”

Section: Data Compression In Voltammetric Electronic Tonguesmentioning

confidence: 99%

Electronic Tongues Employing Electrochemical Sensors

2010

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This review presents recent advances concerning work with electronic tongues employing electroanalytical sensors. This new concept in the electroanalysis sensor field entails the use of chemical sensor arrays coupled with chemometric processing tools, as a mean to improve sensors performance. The revision is organized according to the electroanalytical technique used for transduction, namely: potentiometry, voltammetry/amperometry or electrochemical impedance. The significant use of biosensors, mainly enzyme-based is also presented. Salient applications in real problem solving using electrochemical electronic tongues are commented.

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“…As in nature, the network function is determined largely by the connections between elements and can be trained to perform a particular function by adjusting the values of the connections (weights) between elements. Neural networks have been trained to perform complex functions in various fields of application including pattern recognition, identification and classification [16][17][18][19][20]. Neural networks were also well used in environmental applications from LFG production models to calibration of sensors [17].…”

Section: Neural Networkmentioning

confidence: 99%

Non-linear carbon dioxide determination using infrared gas sensors and neural networks with Bayesian regularization

Lau¹,

Guo²,

Kiernan³

et al. 2009

Sensors and Actuators B: Chemical

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Carbon dioxide gas concentration determination using infrared gas sensors combined with Bayesian regularizing neural networks is presented in this work. Infrared sensor with a measuring range of 0~5% was used to measure carbon dioxide gas concentration within the range 0~15000 ppm. Neural networks were employed to fulfill the nonlinear output of the sensor. The Bayesian strategy was used to regularize the training of the back propagation neural network with a Levenberg-Marquardt (LM) algorithm. By Bayesian regularization (BR), the design of the network was adaptively achieved according to the complexity of the application. Levenberg-Marquardt algorithm under Bayesian regularization has better generalization capability, and is more stable than the classical method. The results showed that the Bayesian regulating neural network was a powerful tool for dealing with the infrared gas sensor which has a large non-linear measuring range and provide precise determination of carbon dioxide gas concentration. In this example, the optimal architecture of the network was one neuron in the input and output layer and two neurons in the hidden layer. The network model gave a relationship coefficient of 0.9996 between targets and outputs. The prediction recoveries were within 99.9~100.0%.

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Application of the wavelet transform coupled with artificial neural networks for quantification purposes in a voltammetric electronic tongue

Cited by 84 publications

References 39 publications

Voltammetric Electronic Tongue in the Analysis of Cava Wines

Voltammetric Electronic Tongue in the Analysis of Cava Wines

Electronic Tongues Employing Electrochemical Sensors

Non-linear carbon dioxide determination using infrared gas sensors and neural networks with Bayesian regularization

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