Electrochemical sensors for fast classification of different Cannabis sativa L. samples according to total Δ9-tetrahydrocannabinol content

In this work, we investigated the ability of an electrochemical sensor to recognize Cannabis sativa L. samples with different total content of Δ<sup>9</sup>-tetrahydrocannabinol (Δ<sup>9</sup>-THC), determined by the levels of the psychoactive cannabinoid and of its biosynthetic precursor Δ<sup>9</sup>-tetrahydrocannabinolic acid (Δ<sup>9</sup>-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<sup>9</sup>-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<sup>9</sup>-THC content directly on site.

Authors

Institutions

Publication Details

Journal
Talanta
Published
2024-09-28
DOI
https://doi.org/10.1016/j.talanta.2024.126958
Citations
9
Primary Topic
Cannabis and Cannabinoid Research
Type
article
Field-Weighted Citation Impact
4.03
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Electrochemical sensors for fast classification of different Cannabis sativa L. samples according to total Δ9-tetrahydrocannabinol content

Giorgia Foca, Alessandro Ulrici, Chiara Zanardi, Barbara Zanfrognini et al.
9 citations
Talanta
Cannabis and Cannabinoid Research
4.03
article

Electrochemical sensors for fast classification of different Cannabis sativa L. samples according to total Δ9-tetrahydrocannabinol content

Giorgia Foca, Alessandro Ulrici, Chiara Zanardi, Barbara Zanfrognini, Alessandro Monari, Patrizia Verri, Laura Pigani, Federica Pellati, Virginia Brighenti
article en
9 citations

Abstract

In this work, we investigated the ability of an electrochemical sensor to recognize Cannabis sativa L. samples with different total content of Δ<sup>9</sup>-tetrahydrocannabinol (Δ<sup>9</sup>-THC), determined by the levels of the psychoactive cannabinoid and of its biosynthetic precursor Δ<sup>9</sup>-tetrahydrocannabinolic acid (Δ<sup>9</sup>-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<sup>9</sup>-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<sup>9</sup>-THC content directly on site.

TalantaVol. 282
University of Modena and Reggio Emilia (IT), Institute of Organic Synthesis and Photoreactivity (IT)
Zero hunger
Openalex Percentile: Top 6%
Cannabis and Cannabinoid Research
4.03
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.