Rapid and robust on-scene detection of cocaine in street samples using a handheld near-infrared spectrometer and machine learning algorithms.
cocaine
forensic illicit-drug analysis
indicative testing
k-nearest neighbors
near-infrared
Journal
Drug testing and analysis
ISSN: 1942-7611
Titre abrégé: Drug Test Anal
Pays: England
ID NLM: 101483449
Informations de publication
Date de publication:
Oct 2020
Oct 2020
Historique:
received:
30
05
2020
revised:
03
07
2020
accepted:
06
07
2020
pubmed:
9
7
2020
medline:
27
8
2021
entrez:
9
7
2020
Statut:
ppublish
Résumé
On-scene drug detection is an increasingly significant challenge due to the fast-changing drug market as well as the risk of exposure to potent drug substances. Conventional colorimetric cocaine tests involve handling of the unknown material and are prone to false-positive reactions on common pharmaceuticals used as cutting agents. This study demonstrates the novel application of 740-1070 nm small-wavelength-range near-infrared (NIR) spectroscopy to confidently detect cocaine in case samples. Multistage machine learning algorithms are used to exploit the limited spectral features and predict not only the presence of cocaine but also the concentration and sample composition. A model based on more than 10,000 spectra from case samples yielded 97% true-positive and 98% true-negative results. The practical applicability is shown in more than 100 case samples not included in the model design. One of the most exciting aspects of this on-scene approach is that the model can almost instantly adapt to changes in the illicit-drug market by updating metadata with results from subsequent confirmatory laboratory analyses. These results demonstrate that advanced machine learning strategies applied on limited-range NIR spectra from economic handheld sensors can be a valuable procedure for rapid on-site detection of illicit substances by investigating officers. In addition to forensics, this interesting approach could be beneficial for screening and classification applications in the pharmaceutical, food-safety, and environmental domains.
Identifiants
pubmed: 32638519
doi: 10.1002/dta.2895
pmc: PMC7590077
doi:
Substances chimiques
Dopamine Uptake Inhibitors
0
Illicit Drugs
0
Cocaine
I5Y540LHVR
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1404-1418Informations de copyright
© 2020 The Authors. Drug Testing and Analysis published by John Wiley & Sons Ltd.
Références
Drug Test Anal. 2017 Feb;9(2):188-198
pubmed: 26888408
Anal Chem. 2018 Apr 17;90(8):5290-5297
pubmed: 29473411
Drug Test Anal. 2020 Oct;12(10):1404-1418
pubmed: 32638519
Carbohydr Polym. 2019 Nov 15;224:115186
pubmed: 31472836
Anal Chem. 2018 Jun 5;90(11):6811-6819
pubmed: 29741867
Forensic Sci Int. 2019 Sep;302:109911
pubmed: 31563026
Forensic Sci Int. 2018 Sep;290:162-168
pubmed: 30053735
Forensic Sci Int. 2017 Apr;273:113-123
pubmed: 28260646
Trends Psychiatry Psychother. 2019 Jul 15;41(2):186-190
pubmed: 31314858
Int J Drug Policy. 2019 Nov;73:7-15
pubmed: 31330276
Anal Bioanal Chem. 2018 Oct;410(26):6691-6704
pubmed: 30073517
Talanta. 2017 Apr 1;165:632-640
pubmed: 28153309
Talanta. 2019 Aug 1;200:553-561
pubmed: 31036222
Drug Alcohol Depend. 2019 Mar 1;196:1-8
pubmed: 30658219
Spectrochim Acta A Mol Biomol Spectrosc. 2019 Jun 15;217:147-154
pubmed: 30933778
Drug Test Anal. 2018 Jan;10(1):95-108
pubmed: 28915346
Adv Drug Deliv Rev. 2005 Jun 15;57(8):1109-43
pubmed: 15899537
J Biophotonics. 2020 Mar;13(3):e201960123
pubmed: 31702875
Talanta. 2019 Apr 1;195:662-667
pubmed: 30625598
Drug Test Anal. 2011 Sep;3(9):621-34
pubmed: 21898860
Addiction. 2019 Sep;114(9):1524-1525
pubmed: 30883941
Drug Dev Ind Pharm. 2020 Jan;46(1):80-90
pubmed: 31794275