Dataset Information

Classification of Various Marijuana Varieties by Raman Microscopy and Chemometrics.

ABSTRACT: The Raman analysis of marijuana is challenging because of the sample's easy photo-degradation caused by the laser intensity. In this study, optimization of collection parameters and laser focusing on marijuana trichome heads allowed collecting Raman spectra without damaging the samples. The Raman spectra of Δ9-tetrahydrocannabinol (THC), cannabidiol (CBD), and cannabinol (CBN) standard cannabinoids were compared with Raman spectra of five different types of marijuana: four Sativa varieties (Amnesia Haze, Amnesia Hy-Pro, Original Amnesia, and Y Griega) and one Indica variety (Black Domina). The results verified the presence of several common spectral bands that are useful for marijuana characterization. Results were corroborated by the quantum chemical simulated Raman spectra of their acid-form (tetrahydrocannabinol acid (THCA), cannabidiol acid (CBDA)) and decarboxylated cannabinoids (THC, CBD, and CBN). A chemometrics-assisted method based on Raman microscopy and OPLS-DA offered good classification among the different marijuana varieties allowing identification of the most significant spectral bands.

SUBMITTER: Ramos-Guerrero L

PROVIDER: S-EPMC8948958 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Classification of Various Marijuana Varieties by Raman Microscopy and Chemometrics.

Ramos-Guerrero Luis L Montalvo Gemma G Cosmi Marzia M García-Ruiz Carmen C Ortega-Ojeda Fernando E FE

Toxics 20220228 3

The Raman analysis of marijuana is challenging because of the sample's easy photo-degradation caused by the laser intensity. In this study, optimization of collection parameters and laser focusing on marijuana trichome heads allowed collecting Raman spectra without damaging the samples. The Raman spectra of Δ<sup>9</sup>-tetrahydrocannabinol (THC), cannabidiol (CBD), and cannabinol (CBN) standard cannabinoids were compared with Raman spectra of five different types of marijuana: four Sativa vari ...[more]

PMID: 35324740

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Project description:A novel approach for the identification of New Psychoactive Substances (NPS) by means of Raman spectroscopy coupled with Principal Components Analysis (PCA) employing the largest dataset of NPS reference materials to date is reported here. Fifty three NPS were selected as a structurally diverse subset from an original dataset of 478 NPS compounds. The Raman spectral profiles were experimentally acquired for all 53 substances, evaluated using a number of pre-processing techniques, and used to generate a PCA model. The optimum model system used a relatively narrow spectral range (1300-1750 cm-1) and accounted for 37% of the variance in the dataset using the first three principal components, despite the large structural diversity inherent in the NPS subset. Nonetheless, structurally similar NPS (i.e., the synthetic cannabinoids FDU-PB-22 & NM-2201) grouped together in the PCA model based on their Raman spectral profiles, while NPS with different chemical scaffolds (i.e., the benzodiazepine flubromazolam and the cathinone α-PBT) were well delineated, occupying markedly different areas of the three-dimensional scores plot. Classification of NPS based on their Raman spectra (i.e., chemical scaffolds) using the PCA model was further investigated. NPS that were present in the initial dataset of 478 NPS but were not part of the selected 53 training set (validation set) were observed to be closely aligned to structurally similar NPS within the generated model system in all cases. Furthermore, NPS that were not present in the original dataset of 478 NPS (test set) were also shown to group as expected in the model (i.e., methamphetamine and N-ethylamphetamine). This indicates that, for the first time, a model system can be applied to potential 'unknown' psychoactive substances, which are new to the market and absent from existing chemical libraries, to identify key structural features to make a preliminary classification. Consequently, it is anticipated that this study will be of interest to the broad scientific audience working with large structurally diverse chemical datasets and particularly to law enforcement agencies and associated scientific analytical bodies worldwide investigating the development of novel identification methodologies for psychoactive substances.

Dataset Information

Classification of Various Marijuana Varieties by Raman Microscopy and Chemometrics.

Publications

Classification of Various Marijuana Varieties by Raman Microscopy and Chemometrics.

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