Development of an electronic tongue to distinguish cutting agents in cocaine samples to understand drug trafficking

Silva, Thalita G.; Paixão, Thiago R.L.C.

doi:10.1109/isoen.2017.7968925

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…The features were decorrelated using PCA and first two PCs are used to detect optimum fermentation time for black Crush Tear Curl (CTC) tea. (Silva and Paixa˜o, 2017) were reported in studies for classification purpose. The proposed feature extraction method is applied to electronic tongue data to discriminate and classify milk samples stored for four consecutive days.…”

Section: Referencementioning

confidence: 99%

Stationary wavelet singular entropy based electronic tongue for classification of milk

Kaushal

Mudhalwadkar

2020

Transactions of the Institute of Measurement and Control

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Electronic tongue mimics human gustatory sensation and is used to characterize and discriminate beverages and foods. Feature extraction plays a key role in improving the classification accuracy by preserving the distinct characteristics while reducing high dimensionality of data generated from electronic tongue. This paper presents a new feature extraction method based on stationary wavelet singular entropy for a developed electronic tongue system to classify pasteurized cow milk. The electronic tongue consists of an array of five working electrodes along with a reference and a counter electrode to characterize milk sample. The feature extraction of acquired data is done by computing stationary wavelet transform to obtain detail and approximate coefficients at different level of decomposition. These coefficients are processed using singular value decomposition followed by calculation of entropy to obtain stationary wavelet singular entropy values. These values form the feature set and feed to two classifiers, k-nearest neighbor and back propagation artificial neural network, and their classification accuracy is evaluated with variation in their model parameters. The proposed method is compared with other wavelet transform-entropy methods in terms of classification accuracy, which indicates that the proposed method is more effective in discriminating milk samples.

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Section: Referencementioning

confidence: 99%

Stationary wavelet singular entropy based electronic tongue for classification of milk

Kaushal

Mudhalwadkar

2020

Transactions of the Institute of Measurement and Control

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“…Os parâmetros extraídos foram processados usando PCA e PLS. Outra aplicação envolvendo caracterização químicaé apresentada em Silva and Paixão (2017), onde uma LE voltamétrica foi usada sobre agentes de corte adicionadosà cocaína (como, procaína, benzocaína, fenacetina, levamisol e cafeína) para fins de discriminação e classificação. As informações foram analisadas com PCA e K-Nearest Neighbor (KNN).…”

Section: Estado Da Arteunclassified

Análise de Dados de Língua Eletrônica baseada em redes SOM 1D

Sousa

Ferreira

Fukushima

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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The design of devices that resemble biological recognition systems, inspired by the functioning of human organs, have made important advances in recent decades. As a result, interest in the concept of electronic languages has increased considerably in recent years. This study uses this type of device in order to identify different samples of flavor enhancers, such as Glutamate, Guanylate and Inosinate. The applied methodology involves self-organizing maps, an unusual tool in this area. The best results reached 95,4% accuracy. Resumo: O projeto de dispositivos que se assemelham a sistemas de reconhecimento biológico, inspirados no funcionamento deórgãos humanos, tiveram avanços importantes nasúltimas décadas. Em consequência disso, o interesse no conceito de línguas eletrônicas aumentou consideravelmente nosúltimos anos. Este estudo utiliza esse tipo de dispositivo com o intuito de identificar diferentes amostras de realçadores de sabor, como Glutamato, Guanilato e Inosinato. A metodologia aplicada envolve redes do tipo mapas auto-organizáveis, ferramenta pouco usual nessaárea. Os melhores resultados atingiram 95,4% de precisão.

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Evolution of E-Sensing Technology

Das

Pramanik

2020

Advances in Intelligent Systems and Computing

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Development of an electronic tongue to distinguish cutting agents in cocaine samples to understand drug trafficking

Cited by 4 publications

References 1 publication

Stationary wavelet singular entropy based electronic tongue for classification of milk

Stationary wavelet singular entropy based electronic tongue for classification of milk

Análise de Dados de Língua Eletrônica baseada em redes SOM 1D

Evolution of E-Sensing Technology

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