Label-free SERS-ML detection of cocaine trace in human blood plasma
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22310%2F24%3A43929378" target="_blank" >RIV/60461373:22310/24:43929378 - isvavai.cz</a>
Alternative codes found
RIV/60461373:22330/24:43929378
Result on the web
<a href="https://doi.org/10.1016/j.jhazmat.2024.134525" target="_blank" >https://doi.org/10.1016/j.jhazmat.2024.134525</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jhazmat.2024.134525" target="_blank" >10.1016/j.jhazmat.2024.134525</a>
Alternative languages
Result language
angličtina
Original language name
Label-free SERS-ML detection of cocaine trace in human blood plasma
Original language description
The widespread consumption of cocaine poses a significant threat to modern society. The most effective way to combat this problem is to control the distribution of cocaine, based on its accurate and sensitive detection. Here, we proposed the detection of cocaine in human blood plasma using a combination of surface enhanced Raman spectroscopy and machine learning (SERS-ML). To demonstrate the efficacy of our proposed approach, cocaine was added into blood plasma at various concentrations and drop-deposited onto a specially prepared disposable SERS substrate. SERS substrates were created by deposition of metal nanoclusters on electrospun polymer nanofibers. Subsequently, SERS spectra were measured and as could be expected, the manual distinguishing of cocaine from the spectra proved unfeasible, as its signal was masked by the background signal from blood plasma molecules. To overcome this issue, a database of SERS spectra of cocaine in blood plasma was collected and used for ML training and validation. After training, the reliability of proposed approach was tested on independently prepared samples, with unknown for SERS-ML cocaine presence or absence. As a result, the possibility of rapid determination of cocaine in blood plasma with a probability above 99.5% for cocaine concentrations up to 10-14 M was confirmed. Therefore, it is evident that the proposed approach has the ability to detect trace amounts of cocaine in bioliquids in an express and simple manner.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20501 - Materials engineering
Result continuities
Project
<a href="/en/project/VJ01010065" target="_blank" >VJ01010065: Express and portative detection of prohibited substances using innovative techniques: flexible and chiral SERS, selective surface extraction, artificial neural networks.</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
JOURNAL OF HAZARDOUS MATERIALS
ISSN
0304-3894
e-ISSN
1873-3336
Volume of the periodical
472
Issue of the periodical within the volume
JUL 5 2024
Country of publishing house
ZA - SOUTH AFRICA
Number of pages
9
Pages from-to
"134525/1"-9
UT code for WoS article
001242262400001
EID of the result in the Scopus database
2-s2.0-85192806154