Label-free SERS-ML detection of cocaine trace in human blood plasma

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/60461373:22330/24:43929378

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Label-free SERS-ML detection of cocaine trace in human blood plasma

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Label-free SERS-ML detection of cocaine trace in human blood plasma

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

<a href="/cs/project/VJ01010065" target="_blank" >VJ01010065: Expresní a portativní detekce zakázaných sloučenin s použitím inovativních technik: flexibilní a chirální SERS, selektivní povrchová extrakce, neuronové sítě.</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

JOURNAL OF HAZARDOUS MATERIALS

ISSN

0304-3894

e-ISSN

1873-3336

Svazek periodika

472

Číslo periodika v rámci svazku

JUL 5 2024

Stát vydavatele periodika

ZA - Jihoafrická republika

Počet stran výsledku

9

Strana od-do

"134525/1"-9

Kód UT WoS článku

001242262400001

EID výsledku v databázi Scopus

2-s2.0-85192806154