A novel and intelligent chemometric-electrochemical-enzymatic biosensing procedure and mimicking a clinical condition environment to trick the red blood cells for counting them under physiological conditions: A new connection among chemometry, electrochemistry and hematology
| Autor: | Faramarz Jalili, Ali R. Jalalvand |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Sensing and Bio-Sensing Research, Vol 43, Iss , Pp 100613- (2024) |
| Druh dokumentu: | article |
| ISSN: | 2214-1804 82514410 |
| DOI: | 10.1016/j.sbsr.2023.100613 |
| Popis: | Here, a novel electrochemical biosensing procedure has been developed for determination of the number of red blood cells (RBCs) under physiochemical conditions based on chemometric modeling of hydrodynamic differential pulse voltammetric (HDPV), and amperometric data as responses of a modified edge plane pyrolytic graphite electrode (EPPGE). In order to obtain a good sensitivity from the EPPGE, its surface was modified with a thin layer of multiwalled carbon nanotubes-ionic liquid (MWCNTs-IL). Catalase (CAT) was immobilized onto the surface of MWCNTs-IL/EPPGE with help of nafion. The response of the biosensor was based on electrochemical reduction of oxygen of the blood samples which was enhanced by a trick based on addition of hydrogen peroxide (H2O2) to blood samples which can be reduced by the CAT to produce extra oxygen. Prior to experiments, the solution in electrochemical cell was bubbled with pure N2 to purge the oxygen in the solution, but in order to increase the selectivity of the biosensor towards detection of the oxygen obtained from the red blood cells, voltammetric responses of the biosensor were modeled by multivariate chemometric calibration methods with the help of radial basis function-partial least squares (RBF-PLS), least squares-support vector machines (LS-SVM), recursive weighted partial least squares (rPLS), ant colony optimization-mathematical pre-processing selection by genetic algorithm-sample selection through a distance-based procedure-partial least squares-1 (ACO-GA-SS-PLS1), and radial basis function-artificial neural networks (RBF-ANN) to select the best method for determination of the number of the RBCs. The results confirmed the amperometric methods modeled by RBF-ANN showed the best performance for supporting the biosensor in determination of the number of the RBCs with a performance which had an excellent compatibility with the results of a hemocytometer. The results of this study as the newest application of chemometric-electrochemical methods can make a strong connection among electrochemists, chemometricians and hematologists to expand their collaborations on determination of blood factors. |
| Databáze: | Directory of Open Access Journals |
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