Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics
This work presents the simultaneous quantification of four non-steroidal anti-inflammatory drugs (NSAIDs), paracetamol, diclofenac, naproxen, and aspirin, in mixture solutions, by a laboratory-made working electrode based on carbon paste modified with multi-wall carbon nanotubes (MWCNT-CPE) and Diff...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/1/421 |
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| author | Guadalupe Yoselin Aguilar-Lira Jesús Eduardo López-Barriguete Prisciliano Hernandez Giaan Arturo Álvarez-Romero Juan Manuel Gutiérrez |
| author_facet | Guadalupe Yoselin Aguilar-Lira Jesús Eduardo López-Barriguete Prisciliano Hernandez Giaan Arturo Álvarez-Romero Juan Manuel Gutiérrez |
| author_sort | Guadalupe Yoselin Aguilar-Lira |
| collection | DOAJ |
| description | This work presents the simultaneous quantification of four non-steroidal anti-inflammatory drugs (NSAIDs), paracetamol, diclofenac, naproxen, and aspirin, in mixture solutions, by a laboratory-made working electrode based on carbon paste modified with multi-wall carbon nanotubes (MWCNT-CPE) and Differential Pulse Voltammetry (DPV). Preliminary electrochemical analysis was performed using cyclic voltammetry, and the sensor morphology was studied by scanning electronic microscopy and electrochemical impedance spectroscopy. The sample set ranging from 0.5 to 80 µmol L<sup>−1</sup> was prepared using a complete factorial design (3<sup>4</sup>) and considering some interferent species such as ascorbic acid, glucose, and sodium dodecyl sulfate to build the response model and an external randomly subset of samples within the experimental domain. A data compression strategy based on discrete wavelet transform was applied to handle voltammograms’ complexity and high dimensionality. Afterward, Partial Least Square Regression (PLS) and Artificial Neural Networks (ANN) predicted the drug concentrations in the mixtures. PLS-adjusted models (<i>n</i> = 12) successfully predicted the concentration of paracetamol and diclofenac, achieving correlation values of R ≥ 0.9 (testing set). Meanwhile, the ANN model (four layers) obtained good prediction results, exhibiting R ≥ 0.968 for the four analyzed drugs (testing stage). Thus, an MWCNT-CPE electrode can be successfully used as a potential sensor for voltammetric determination and NSAID analysis. |
| first_indexed | 2024-03-09T09:40:41Z |
| format | Article |
| id | doaj.art-5755a9c981dd4e5b9cfaf417a69c35fc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:40:41Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5755a9c981dd4e5b9cfaf417a69c35fc2023-12-02T00:56:44ZengMDPI AGSensors1424-82202022-12-0123142110.3390/s23010421Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and ChemometricsGuadalupe Yoselin Aguilar-Lira0Jesús Eduardo López-Barriguete1Prisciliano Hernandez2Giaan Arturo Álvarez-Romero3Juan Manuel Gutiérrez4Laboratory of Analytical Chemistry, Academic Area of Chemistry, Institute of Basic Sciences and Engineering, Autonomous University of the State of Hidalgo, Pachuca 42076, Hidalgo, MexicoBioelectronics Section, Department of Electrical Engineering, CINVESTAV-IPN, Mexico City 07360, MexicoEngineering and Energy Laboratory, Energy Area, Polytechnic University of Francisco I. Madero, Pachuca 42640, Hidalgo, MexicoLaboratory of Analytical Chemistry, Academic Area of Chemistry, Institute of Basic Sciences and Engineering, Autonomous University of the State of Hidalgo, Pachuca 42076, Hidalgo, MexicoBioelectronics Section, Department of Electrical Engineering, CINVESTAV-IPN, Mexico City 07360, MexicoThis work presents the simultaneous quantification of four non-steroidal anti-inflammatory drugs (NSAIDs), paracetamol, diclofenac, naproxen, and aspirin, in mixture solutions, by a laboratory-made working electrode based on carbon paste modified with multi-wall carbon nanotubes (MWCNT-CPE) and Differential Pulse Voltammetry (DPV). Preliminary electrochemical analysis was performed using cyclic voltammetry, and the sensor morphology was studied by scanning electronic microscopy and electrochemical impedance spectroscopy. The sample set ranging from 0.5 to 80 µmol L<sup>−1</sup> was prepared using a complete factorial design (3<sup>4</sup>) and considering some interferent species such as ascorbic acid, glucose, and sodium dodecyl sulfate to build the response model and an external randomly subset of samples within the experimental domain. A data compression strategy based on discrete wavelet transform was applied to handle voltammograms’ complexity and high dimensionality. Afterward, Partial Least Square Regression (PLS) and Artificial Neural Networks (ANN) predicted the drug concentrations in the mixtures. PLS-adjusted models (<i>n</i> = 12) successfully predicted the concentration of paracetamol and diclofenac, achieving correlation values of R ≥ 0.9 (testing set). Meanwhile, the ANN model (four layers) obtained good prediction results, exhibiting R ≥ 0.968 for the four analyzed drugs (testing stage). Thus, an MWCNT-CPE electrode can be successfully used as a potential sensor for voltammetric determination and NSAID analysis.https://www.mdpi.com/1424-8220/23/1/421non-steroidal anti-inflammatory drugcarbon paste electrodedifferential pulse voltammetrypartial least square regressionartificial neural networkquantification |
| spellingShingle | Guadalupe Yoselin Aguilar-Lira Jesús Eduardo López-Barriguete Prisciliano Hernandez Giaan Arturo Álvarez-Romero Juan Manuel Gutiérrez Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics Sensors non-steroidal anti-inflammatory drug carbon paste electrode differential pulse voltammetry partial least square regression artificial neural network quantification |
| title | Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics |
| title_full | Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics |
| title_fullStr | Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics |
| title_full_unstemmed | Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics |
| title_short | Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics |
| title_sort | simultaneous voltammetric determination of non steroidal anti inflammatory drugs nsaids using a modified carbon paste electrode and chemometrics |
| topic | non-steroidal anti-inflammatory drug carbon paste electrode differential pulse voltammetry partial least square regression artificial neural network quantification |
| url | https://www.mdpi.com/1424-8220/23/1/421 |
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