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Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines.
Herrera-Chacón, Anna; Torabi, Farzad; Faridbod, Farnoush; Ghasemi, Jahan B; González-Calabuig, Andreu; Del Valle, Manel.
Afiliação
  • Herrera-Chacón A; Sensors and Biosensors Group, Department of Chemistry Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, Spain.
  • Torabi F; Sensors and Biosensors Group, Department of Chemistry Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, Spain.
  • Faridbod F; Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, Tehran 1417466191, Iran.
  • Ghasemi JB; Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, Tehran 1417466191, Iran.
  • González-Calabuig A; School of Chemistry, College of Science, University of Tehran, Tehran 1417466191, Iran.
  • Del Valle M; Sensors and Biosensors Group, Department of Chemistry Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, Spain.
Sensors (Basel) ; 19(22)2019 Nov 16.
Article em En | MEDLINE | ID: mdl-31744128
ABSTRACT
The presented manuscript reports the simultaneous detection of a ternary mixture of the benzodiazepines diazepam, lorazepam, and flunitrazepam using an array of voltammetric sensors and the electronic tongue principle. The electrodes used in the array were selected from a set of differently modified graphite epoxy composite electrodes; specifically, six electrodes were used incorporating metallic nanoparticles of Cu and Pt, oxide nanoparticles of CuO and WO3, plus pristine electrodes of epoxy-graphite and metallic Pt disk. Cyclic voltammetry was the technique used to obtain the voltammetric responses. Multivariate examination using Principal Component Analysis (PCA) justified the choice of sensors in order to get the proper discrimination of the benzodiazepines. Next, a quantitative model to predict the concentrations of mixtures of the three benzodiazepines was built employing the set of voltammograms, and was first processed with the Discrete Wavelet Transform, which fed an artificial neural network response model. The developed model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 0.034 and 0.106 for the training and test subsets, respectively, and coefficient of correlation R ≥ 0.938 in the predicted vs. expected concentrations comparison graph.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Benzodiazepinas / Técnicas Biossensoriais / Técnicas Eletroquímicas Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Espanha

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Benzodiazepinas / Técnicas Biossensoriais / Técnicas Eletroquímicas Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Espanha