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Electrochemical sensors for fast classification of different Cannabis sativa L. samples according to total Δ 9 -tetrahydrocannabinol content.

Authors :
Monari A
Foca G
Ulrici A
Zanfrognini B
Brighenti V
Verri P
Pellati F
Zanardi C
Pigani L
Source :
Talanta [Talanta] 2025 Jan 01; Vol. 282, pp. 126958. Date of Electronic Publication: 2024 Sep 28.
Publication Year :
2025

Abstract

In this work, we investigated the ability of an electrochemical sensor to recognize Cannabis sativa L. samples with different total content of Δ <superscript>9</superscript> -tetrahydrocannabinol (Δ <superscript>9</superscript> -THC), determined by the levels of the psychoactive cannabinoid and of its biosynthetic precursor Δ <superscript>9</superscript> -tetrahydrocannabinolic acid (Δ <superscript>9</superscript> -THCA), using a multivariate approach. The voltammetric responses recorded with screen-printed electrodes modified with carbon black reflected the compositional differences from the different samples, in terms of cannabinoids of the vegetal material. PLS-DA models allowed for the correct classification of most C. sativa samples into the classes of legal and illegal samples according to total Δ <superscript>9</superscript> -THC content, based on threshold limits defined by the EU/US (0.3 % w/w) and Italian (0.6 % w/w) regulations. Satisfactory results were achieved in both cases, obtaining classification efficiency values in prediction of the external test set equal to 85 % and 100 % for the EU/US and Italian thresholds, respectively. The obtained results suggest the possibility to consider the proposed method as a starting point for the implementation of an automated device for rapid prescreening of total Δ <superscript>9</superscript> -THC content directly on site.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Details

Language :
English
ISSN :
1873-3573
Volume :
282
Database :
MEDLINE
Journal :
Talanta
Publication Type :
Academic Journal
Accession number :
39366244
Full Text :
https://doi.org/10.1016/j.talanta.2024.126958