MicroNIR/Chemometrics: A new analytical platform for fast and accurate detection of Δ9-Tetrahydrocannabinol (THC) in oral fluids.
Adolescent
Adult
Analgesics, Non-Narcotic
/ analysis
Body Fluids
/ chemistry
Dronabinol
/ analysis
Female
Gas Chromatography-Mass Spectrometry
/ methods
Humans
Male
Microchemistry
/ methods
Middle Aged
Miniaturization
/ methods
Saliva
/ chemistry
Spectroscopy, Near-Infrared
/ methods
Substance Abuse Detection
/ methods
Time Factors
Young Adult
Chemometrics
MicroNIR
Screening
THC
Toxicology
Journal
Drug and alcohol dependence
ISSN: 1879-0046
Titre abrégé: Drug Alcohol Depend
Pays: Ireland
ID NLM: 7513587
Informations de publication
Date de publication:
01 12 2019
01 12 2019
Historique:
received:
17
05
2019
revised:
16
07
2019
accepted:
22
07
2019
pubmed:
15
10
2019
medline:
1
7
2020
entrez:
15
10
2019
Statut:
ppublish
Résumé
Δ Specimens from volunteers were collected in order to consider any sources of variability in the spectral response and spiked with increasing amount of THC in order to realize predictive models to be used in real cases. Partial Least Square-Discriminant Analysis (PLS-DA) and Partial Least Square regression (PLSr) for the simultaneously detection and quantification of THC, were applied to baseline corrected spectra pre-treated by first derivative transform. Results demonstrated that MicroNIR/Chemometric platform is statistically able to identify THC abuse in simulated oral fluid samples containing THC from 10 to 100 ng/ml, with a precision and a sensitivity of about 1.51% and 0.1% respectively. The coupling MicroNIR/Chemometrics permits to simplify THC abuse monitoring for roadside drug testing or workplace surveillance and provides the rapid interpretation of results, as once the model is assessed, it can be used to process real samples in a "click-on" device.
Sections du résumé
BACKGROUND
Δ
METHODS
Specimens from volunteers were collected in order to consider any sources of variability in the spectral response and spiked with increasing amount of THC in order to realize predictive models to be used in real cases. Partial Least Square-Discriminant Analysis (PLS-DA) and Partial Least Square regression (PLSr) for the simultaneously detection and quantification of THC, were applied to baseline corrected spectra pre-treated by first derivative transform.
RESULTS
Results demonstrated that MicroNIR/Chemometric platform is statistically able to identify THC abuse in simulated oral fluid samples containing THC from 10 to 100 ng/ml, with a precision and a sensitivity of about 1.51% and 0.1% respectively.
CONCLUSIONS
The coupling MicroNIR/Chemometrics permits to simplify THC abuse monitoring for roadside drug testing or workplace surveillance and provides the rapid interpretation of results, as once the model is assessed, it can be used to process real samples in a "click-on" device.
Identifiants
pubmed: 31610296
pii: S0376-8716(19)30355-2
doi: 10.1016/j.drugalcdep.2019.107578
pii:
doi:
Substances chimiques
Analgesics, Non-Narcotic
0
Dronabinol
7J8897W37S
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
107578Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.