TY - JOUR

T1 - Novel fluorescence spectroscopy coupled with PARAFAC modeling for major cannabinoids quantification and identification in cannabis extracts

AU - Birenboim, Matan

AU - Rinnan, Åsmund

AU - Kengisbuch, David

AU - Shimshoni, Jakob A.

N1 - Publisher Copyright: © 2022 Elsevier B.V.

PY - 2023

Y1 - 2023

N2 - Cannabinoids are commonly identified and quantified using chromatographic based methods. In the present study, fluorescence spectroscopic method coupled with Parallel Factor Analysis (PARAFAC) modeling was developed and validated as a simple and fast alternative technique for identification and quantification of major cannabinoids. A PARAFAC model was built on a set of excitation-emission matrices, yielding an optimal quantitative and qualitative performance using five components, which were identified as (-)-Δ9-trans-tetrahydrocannabinolic acid (THCA), cannabidiolic acid (CBDA), cannabigerolic acid (CBGA), cannabichromenic acid (CBCA) and (-)-Δ9-trans-tetrahydrocannabinol/cannabidiol/cannabigerol (THC/CBD/CBG). The identity of the major acidic components, THCA, CBDA and CBGA was verified by the correlation between PARAFAC model scores and their corresponding concentrations measured by HPLC as well as by the similarity between the excitation-emission spectral loadings of each PARAFAC component and the excitation-emission spectra of pure cannabinoids standards. Moreover, the PARAFAC model scores of each component were plotted against the cannabinoids actual concentrations in the extracts to evaluate the performance of the model for predicting the concentration of each compound. All three major acidic cannabinoids revealed good to excellent linear correlations (R2 > 0.7) between the model scores and measured concentrations according to the model calibration, cross-validation and prediction performance criteria. On the other hand, components 4 and 5 identified as CBCA and THC/CBD/CBG, respectively, revealed weaker linear correlation between the PARAFAC scores to the measured concentrations. These findings pave the way for a more comprehensive assessment of cannabis excitation-emission matrices (EEMs) as a cheaper and fast alternative for commonly used chromatographic-based quantification methods.

AB - Cannabinoids are commonly identified and quantified using chromatographic based methods. In the present study, fluorescence spectroscopic method coupled with Parallel Factor Analysis (PARAFAC) modeling was developed and validated as a simple and fast alternative technique for identification and quantification of major cannabinoids. A PARAFAC model was built on a set of excitation-emission matrices, yielding an optimal quantitative and qualitative performance using five components, which were identified as (-)-Δ9-trans-tetrahydrocannabinolic acid (THCA), cannabidiolic acid (CBDA), cannabigerolic acid (CBGA), cannabichromenic acid (CBCA) and (-)-Δ9-trans-tetrahydrocannabinol/cannabidiol/cannabigerol (THC/CBD/CBG). The identity of the major acidic components, THCA, CBDA and CBGA was verified by the correlation between PARAFAC model scores and their corresponding concentrations measured by HPLC as well as by the similarity between the excitation-emission spectral loadings of each PARAFAC component and the excitation-emission spectra of pure cannabinoids standards. Moreover, the PARAFAC model scores of each component were plotted against the cannabinoids actual concentrations in the extracts to evaluate the performance of the model for predicting the concentration of each compound. All three major acidic cannabinoids revealed good to excellent linear correlations (R2 > 0.7) between the model scores and measured concentrations according to the model calibration, cross-validation and prediction performance criteria. On the other hand, components 4 and 5 identified as CBCA and THC/CBD/CBG, respectively, revealed weaker linear correlation between the PARAFAC scores to the measured concentrations. These findings pave the way for a more comprehensive assessment of cannabis excitation-emission matrices (EEMs) as a cheaper and fast alternative for commonly used chromatographic-based quantification methods.

KW - Cannabaceae

KW - Cannabinoids

KW - Cannabis sativa L.

KW - Excitation-emission matrix (EEM)

KW - Parallel factor analysis (PARAFAC)

U2 - 10.1016/j.chemolab.2022.104717

DO - 10.1016/j.chemolab.2022.104717

M3 - Journal article

AN - SCOPUS:85142681016

VL - 232

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

SN - 0169-7439

M1 - 104717

ER -