Sensor for Rapid In-Field Classification of Cannabis Samples Based on Near-Infrared Spectroscopy.
cannabis analysis
chemometrics
forensic science
handheld sensor
law enforcement
machine learning
near-infrared spectroscopy
partial least squares discriminant analysis
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
17 May 2024
17 May 2024
Historique:
received:
17
04
2024
revised:
14
05
2024
accepted:
16
05
2024
medline:
25
5
2024
pubmed:
25
5
2024
entrez:
25
5
2024
Statut:
epublish
Résumé
A rugged handheld sensor for rapid in-field classification of cannabis samples based on their THC content using ultra-compact near-infrared spectrometer technology is presented. The device is designed for use by the Austrian authorities to discriminate between legal and illegal cannabis samples directly at the place of intervention. Hence, the sensor allows direct measurement through commonly encountered transparent plastic packaging made from polypropylene or polyethylene without any sample preparation. The measurement time is below 20 s. Measured spectral data are evaluated using partial least squares discriminant analysis directly on the device's hardware, eliminating the need for internet connectivity for cloud computing. The classification result is visually indicated directly on the sensor via a colored LED. Validation of the sensor is performed on an independent data set acquired by non-expert users after a short introduction. Despite the challenging setting, the achieved classification accuracy is higher than 80%. Therefore, the handheld sensor has the potential to reduce the number of unnecessarily confiscated legal cannabis samples, which would lead to significant monetary savings for the authorities.
Identifiants
pubmed: 38794042
pii: s24103188
doi: 10.3390/s24103188
pii:
doi:
Substances chimiques
Dronabinol
7J8897W37S
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Austrian Research Promotion Agency
ID : FO999886335