| Records |
| Author |
Ortiz-Aguayo, D.; Ceto, X.; De Wael, K.; del Valle, M. |
| Title |
Resolution of opiate illicit drugs signals in the presence of some cutting agents with use of a voltammetric sensor array and machine learning strategies |
Type |
A1 Journal article |
Year  |
2022 |
Publication |
Sensors and actuators : B : chemical |
Abbreviated Journal |
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| Volume |
357 |
Issue |
|
Pages |
131345 |
| Keywords |
A1 Journal article; Antwerp Electrochemical and Analytical Sciences Lab (A-Sense Lab) |
| Abstract |
In the present work, the resolution and quantification of mixtures of different opiate compounds in the presence of common cutting agents using an electronic tongue (ET) is evaluated. More specifically, ternary mixtures of heroin, morphine and codeine were resolved in the presence of caffeine and paracetamol. To this aim, an array of three carbon screen-printed electrodes were modified with different ink-like solutions of graphite, cobalt (II) phthalocyanine and palladium, and their responses towards the different drugs were characterized by means of square wave voltammetry (SWV). Developed sensors showed a good performance with good linearity at the mu M level, LODs between 1.8 and 5.3 mu M for the 3 actual drugs, and relative standard deviation (RSD) ca. 2% for over 50 consecutive measurements. Next, a quantitative model that allowed the identification and quantification of the individual substances from the overlapped voltammograms was built using partial least squares regression (PLS) as the modeling tool. With this approach, quantification of the different drugs was achieved at the mu M level, with a total normalized root mean square error (NRMSE) of 0.084 for the test subset. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Wos |
000745113900003 |
Publication Date |
2021-12-31 |
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0925-4005 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
| Impact Factor |
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Times cited |
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Open Access |
OpenAccess |
| Notes |
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Approved |
no |
| Call Number |
UA @ admin @ c:irua:185446 |
Serial |
8922 |
| Permanent link to this record |
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| |
| Author |
Ortiz-Aguayo, D.; De Wael, K.; del Valle, M. |
| Title |
Voltammetric sensing using an array of modified SPCE coupled with machine learning strategies for the improved identification of opioids in presence of cutting agents |
Type |
A1 Journal article |
| Year |
2021 |
Publication |
Journal Of Electroanalytical Chemistry |
Abbreviated Journal |
J Electroanal Chem |
| Volume |
902 |
Issue |
|
Pages |
115770 |
| Keywords |
A1 Journal article; AXES (Antwerp X-ray Analysis, Electrochemistry and Speciation); Antwerp Electrochemical and Analytical Sciences Lab (A-Sense Lab) |
| Abstract |
This work reports the use of modified screen-printed carbon electrodes (SPCEs) for the identification of three drugs of abuse and two habitual cutting agents, caffeine and paracetamol, combining voltammetric sensing and chemometrics. In order to achieve this goal, codeine, heroin and morphine were subjected to Square Wave Voltammetry (SWV) at pH 7, in order to elucidate their electrochemical fingerprints. The optimized SPCEs electrode array, which have a differentiated response for the three oxidizable compounds, was derived from Carbon, Prussian blue, Cobalt (II) phthalocyanine, Copper (II) oxide, Polypyrrole and Palladium nanoparticles ink-modified carbon electrodes. Finally, Principal Component Analysis (PCA) coupled with Silhouette parameter assessment was used to select the most suitable combination of sensors for identification of drugs of abuse in presence of cutting agents. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Wos |
000714415500006 |
Publication Date |
2021-10-13 |
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1572-6657; 1873-2569 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
| Impact Factor |
3.012 |
Times cited |
|
Open Access |
OpenAccess |
| Notes |
|
Approved |
Most recent IF: 3.012 |
| Call Number |
UA @ admin @ c:irua:184018 |
Serial |
8745 |
| Permanent link to this record |
