Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics.
Guadalupe Yoselin Aguilar-LiraJesús Eduardo López-BarriguetePrisciliano HernandezGiaan Arturo Álvarez-RomeroJuan Manuel GutiérrezPublished in: Sensors (Basel, Switzerland) (2022)
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 -1 was prepared using a complete factorial design (3 4 ) 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 ( n = 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.
Keyphrases
- anti inflammatory drugs
- molecularly imprinted
- carbon nanotubes
- neural network
- solid phase extraction
- high resolution
- gold nanoparticles
- label free
- solid state
- electronic health record
- cardiovascular disease
- optical coherence tomography
- magnetic resonance
- high throughput
- convolutional neural network
- mass spectrometry
- weight loss
- coronary artery disease
- magnetic resonance imaging
- antiplatelet therapy
- atrial fibrillation
- electron microscopy
- blood glucose
- deep learning
- insulin resistance
- liquid chromatography
- human health