Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 1; p. 421
• Main Authors: Aguilar-Lira, Guadalupe Yoselin, López-Barriguete, Jesús Eduardo, Hernandez, Prisciliano, Álvarez-Romero, Giaan Arturo, Gutiérrez, Juan Manuel
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.12.2022; MDPI
• Subjects: Analgesics; Anti-inflammatory agents; artificial neural network; Ascorbic acid; Aspirin; Carbon; carbon paste electrode; Diclofenac; differential pulse voltammetry; Discrete Wavelet Transform; Electrochemical analysis; Electrochemical impedance spectroscopy; Electrodes; Factorial design; Laboratories; Mandatory drug testing; Mixtures; Naproxen; Neural networks; non-steroidal anti-inflammatory drug; Nonsteroidal anti-inflammatory drugs; Organic acids; partial least square regression; Pharmaceuticals; quantification; Regression analysis; Reproducibility; Sensors; Sodium dodecyl sulfate; Sulfates; Surface active agents; Voltammetry; Wavelet transforms; Mexico; United States > US; artificial neural network; differential pulse voltammetry; quantification; partial least square regression; carbon paste electrode; non-steroidal anti-inflammatory drug
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23010421

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 was prepared using a complete factorial design (3 ) 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 ( = 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.