Rapid and robust on-scene detection of cocaine in street samples using a handheld near-infrared spectrometer and machine learning algorithms

Autor: Joshka Verduin, Frank Bakker, Ger Koomen, Fionn Wallace, Marcel Heerschop, Annemieke Hulsbergen, Annette van Esch, Yannick Weesepoel, Arian C. van Asten, Peter H. J. Keizers, Ruben F. Kranenburg, Martin Alewijn
Přispěvatelé: HIMS Other Research (FNWI), Supramolecular Separations (HIMS, FNWI)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
forensic illicit-drug analysis
Computer science
Pharmaceutical Science
cocaine
Machine learning
computer.software_genre
01 natural sciences
near-infrared
Analytical Chemistry
Machine Learning
Drug detection
03 medical and health sciences
indicative testing
0302 clinical medicine
Dopamine Uptake Inhibitors
Sample composition
BU Authenticity & Bioassays
Humans
Environmental Chemistry
030216 legal & forensic medicine
Research Articles
Spectroscopy
VLAG
Spectroscopy
Near-Infrared
Illicit Drugs
business.industry
010401 analytical chemistry
k-nearest neighbors
forensic illicit‐drug analysis
0104 chemical sciences
Drug market
near‐infrared
Metadata
BU Authenticiteit & Bioassays
Near infrared spectrometer
Nir spectra
Artificial intelligence
business
computer
Mobile device
Algorithm
Algorithms
k‐nearest neighbors
Research Article
Zdroj: Drug Testing and Analysis
Drug Testing and Analysis, 12(10), 1404-1418. John Wiley and Sons Ltd
Drug Testing and Analysis, 12(10), 1404-1418
Drug Testing and Analysis 12 (2020) 10
ISSN: 1942-7603
Popis: 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.
The novel application of 740‐1070 nm small wavelength range NIR spectroscopy to confidently detect cocaine in case samples is demonstrated. Multi‐stage machine learning algorithms are applied to exploit the limited spectral features and predict not only the presence of cocaine but also predict a concentration and sample composition. A model based on >10,000 spectra from case samples yielded 97% true positive and 98% true negative results. The practical applicability is shown on over 100 case samples not included in model design.
Databáze: OpenAIRE
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