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Author Ortiz-Aguayo, D.; Ceto, X.; De Wael, K.; del Valle, M. url  doi
openurl 
  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 (down) 2022 Publication Sensors and actuators : B : chemical Abbreviated Journal  
  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  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Wos 000745113900003 Publication Date 2021-12-31  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0925-4005 ISBN Additional Links UA library record; WoS full record; WoS citing articles  
  Impact Factor Times cited Open Access OpenAccess  
  Notes Approved no  
  Call Number UA @ admin @ c:irua:185446 Serial 8922  
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Author Ortiz-Aguayo, D.; De Wael, K.; del Valle, M. url  doi
openurl 
  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 (down) 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  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Wos 000714415500006 Publication Date 2021-10-13  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1572-6657; 1873-2569 ISBN 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  
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