Sensor for Rapid In-Field Classification of Cannabis Samples Based on Near-Infrared Spectroscopy

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 10; p. 3188
• Main Authors: Zimmerleiter, Robert, Greibl, Wolfgang, Meininger, Gerold, Duswald, Kristina, Hannesschläger, Günther, Gattinger, Paul, Rohm, Matthias, Fuczik, Christian, Holzer, Robert, Brandstetter, Markus
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.05.2024
• Subjects: Accuracy; Cannabidiol; Cannabis - chemistry; Cannabis - classification; cannabis analysis; Chromatography; Classification; Discriminant Analysis; Dronabinol - analysis; forensic science; handheld sensor; Humans; Infrared spectroscopy; law enforcement; Least-Squares Analysis; Light-emitting diodes; Marijuana; Measurement techniques; near-infrared spectroscopy; partial least squares discriminant analysis; Polyethylene; Sensors; Solvents; Spectroscopy, Near-Infrared - methods; Spectrum analysis; Tetrahydrocannabinol; THC; Canada; Austria; Japan; Germany; United States > US; Switzerland; chemometrics; near-infrared spectroscopy; partial least squares discriminant analysis; forensic science; cannabis analysis; handheld sensor; machine learning; law enforcement
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24103188

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.