Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/22/5002 |
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| author | Anna Herrera-Chacón Farzad Torabi Farnoush Faridbod Jahan B. Ghasemi Andreu González-Calabuig Manel del Valle |
| author_facet | Anna Herrera-Chacón Farzad Torabi Farnoush Faridbod Jahan B. Ghasemi Andreu González-Calabuig Manel del Valle |
| author_sort | Anna Herrera-Chacón |
| collection | DOAJ |
| description | 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 WO<sub>3</sub>, 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. |
| first_indexed | 2024-04-11T22:21:44Z |
| format | Article |
| id | doaj.art-451369f389de4527a3c355b3dfa3174b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:21:44Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-451369f389de4527a3c355b3dfa3174b2022-12-22T04:00:05ZengMDPI AGSensors1424-82202019-11-011922500210.3390/s19225002s19225002Voltammetric Electronic Tongue for the Simultaneous Determination of Three BenzodiazepinesAnna Herrera-Chacón0Farzad Torabi1Farnoush Faridbod2Jahan B. Ghasemi3Andreu González-Calabuig4Manel del Valle5Sensors and Biosensors Group, Department of Chemistry Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, SpainSensors and Biosensors Group, Department of Chemistry Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, SpainCenter of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, Tehran 1417466191, IranSchool of Chemistry, College of Science, University of Tehran, Tehran 1417466191, IranSensors and Biosensors Group, Department of Chemistry Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, SpainSensors and Biosensors Group, Department of Chemistry Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, SpainThe 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 WO<sub>3</sub>, 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.https://www.mdpi.com/1424-8220/19/22/5002electronic tonguenanoparticle modifiersdiazepamlorazepamflunitrazepamartificial neural networks |
| spellingShingle | Anna Herrera-Chacón Farzad Torabi Farnoush Faridbod Jahan B. Ghasemi Andreu González-Calabuig Manel del Valle Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines Sensors electronic tongue nanoparticle modifiers diazepam lorazepam flunitrazepam artificial neural networks |
| title | Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines |
| title_full | Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines |
| title_fullStr | Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines |
| title_full_unstemmed | Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines |
| title_short | Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines |
| title_sort | voltammetric electronic tongue for the simultaneous determination of three benzodiazepines |
| topic | electronic tongue nanoparticle modifiers diazepam lorazepam flunitrazepam artificial neural networks |
| url | https://www.mdpi.com/1424-8220/19/22/5002 |
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