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Fursman H, Morelato M, Chadwick S, Coppey F, Esseiva P, Roux C, Stojanovska N. Development and evaluation of portable NIR technology for the identification and quantification of Australian illicit drugs. Forensic Sci Int 2024; 362:112179. [PMID: 39096793 DOI: 10.1016/j.forsciint.2024.112179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 06/11/2024] [Accepted: 07/02/2024] [Indexed: 08/05/2024]
Abstract
The efficient and accurate analysis of illicit drugs remains a constant challenge in Australia given the high volume of drugs trafficked into and around the country. Portable drug testing technologies facilitate the decentralisation of the forensic laboratory and enable analytical data to be acted upon more efficiently. Near-infrared (NIR) spectroscopy combined with chemometric modelling (machine learning algorithms) has been highlighted as a portable drug testing technology that is rapid and accurate. However, its effectiveness depends upon a database of chemically relevant specimens that are representative of the market. There are chemical differences between drugs in different countries that need to be incorporated into the database to ensure accurate chemometric model prediction. This study aimed to optimise and assess the implementation of NIR spectroscopy combined with machine learning models to rapidly identify and quantify illicit drugs within an Australian context. The MicroNIR (Viavi Solutions Inc.) was used to scan 608 illicit drug specimens seized by the Australian Federal Police comprising of mainly crystalline methamphetamine hydrochloride (HCl), cocaine HCl, and heroin HCl. A number of other traditional drugs, new psychoactive substances and adulterants were also scanned to assess selectivity. The 3673 NIR scans were compared to the identity and quantification values obtained from a reference laboratory in order to assess the proficiency of the chemometric models. The identification of crystalline methamphetamine HCl, cocaine HCl, and heroin HCl specimens was highly accurate, with accuracy rates of 98.4 %, 97.5 %, and 99.2 %, respectively. The sensitivity of these three drugs was more varied with heroin HCl identification being the least sensitive (methamphetamine = 96.6 %, cocaine = 93.5 % and heroin = 91.3 %). For these three drugs, the NIR technology provided accurate quantification, with 99 % of values falling within the relative uncertainty of ±15 %. The MicroNIR with NIRLAB infrastructure has demonstrated to provide accurate results in real-time with clear operational applications. There is potential to improve informed decision-making, safety, efficiency and effectiveness of frontline and proactive policing within Australia.
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Affiliation(s)
- Harrison Fursman
- Centre for Forensic Science, University of Technology Sydney, 15 Broadway, PO Box 123, Broadway, NSW 2007, Australia.
| | - Marie Morelato
- Centre for Forensic Science, University of Technology Sydney, 15 Broadway, PO Box 123, Broadway, NSW 2007, Australia.
| | - Scott Chadwick
- Centre for Forensic Science, University of Technology Sydney, 15 Broadway, PO Box 123, Broadway, NSW 2007, Australia.
| | - Florentin Coppey
- École des Sciences Criminelles/School of Criminal Justice, University of Lausanne, Building Batochime, Lausanne, Vaud CH-1015, Switzerland.
| | - Pierre Esseiva
- École des Sciences Criminelles/School of Criminal Justice, University of Lausanne, Building Batochime, Lausanne, Vaud CH-1015, Switzerland.
| | - Claude Roux
- Centre for Forensic Science, University of Technology Sydney, 15 Broadway, PO Box 123, Broadway, NSW 2007, Australia.
| | - Natasha Stojanovska
- Forensics Command, Australian Federal Police, 110 Goulburn Street, Sydney, NSW 2000, Australia.
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2
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Foli LP, Hespanhol MC, Cruz KAML, Pasquini C. Miniaturized Near-Infrared spectrophotometers in forensic analytical science - a critical review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124297. [PMID: 38640625 DOI: 10.1016/j.saa.2024.124297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/13/2024] [Accepted: 04/14/2024] [Indexed: 04/21/2024]
Abstract
The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.
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Affiliation(s)
- Letícia P Foli
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Maria C Hespanhol
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Kaíque A M L Cruz
- Grupo de Análise e Educação para a Sustentabilidade, Departamento de Química, Centro de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa, Av. P. H. Rolfs, s/n, Viçosa, MG, 36570-900, Brazil
| | - Celio Pasquini
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Rua Monteiro Lobato, 290, Campinas, SP 13083-862, Brazil.
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3
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Zimmerleiter R, Greibl W, Meininger G, Duswald K, Hannesschläger G, Gattinger P, Rohm M, Fuczik C, Holzer R, Brandstetter M. Sensor for Rapid In-Field Classification of Cannabis Samples Based on Near-Infrared Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2024; 24:3188. [PMID: 38794042 PMCID: PMC11124929 DOI: 10.3390/s24103188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/14/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
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.
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Affiliation(s)
- Robert Zimmerleiter
- Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria; (K.D.); (G.H.); (P.G.); (R.H.)
| | - Wolfgang Greibl
- Criminal Intelligence Service, Forensic Science, Josef Holaubek Platz, 1090 Wien, Austria;
| | - Gerold Meininger
- Spath Micro Electronic Design GmbH, Reininghausstraße 13, 8020 Graz, Austria;
| | - Kristina Duswald
- Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria; (K.D.); (G.H.); (P.G.); (R.H.)
| | - Günther Hannesschläger
- Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria; (K.D.); (G.H.); (P.G.); (R.H.)
| | - Paul Gattinger
- Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria; (K.D.); (G.H.); (P.G.); (R.H.)
| | - Matthias Rohm
- IFHA/Christian Fuczik-Chemisches Labor GmbH, Gerhardusgasse 25/3.OG, 1200 Wien, Austria; (M.R.); (C.F.)
| | - Christian Fuczik
- IFHA/Christian Fuczik-Chemisches Labor GmbH, Gerhardusgasse 25/3.OG, 1200 Wien, Austria; (M.R.); (C.F.)
| | - Robert Holzer
- Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria; (K.D.); (G.H.); (P.G.); (R.H.)
| | - Markus Brandstetter
- Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria; (K.D.); (G.H.); (P.G.); (R.H.)
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4
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Duchateau C, Stévigny C, De Braekeleer K, Deconinck E. Characterization of CBD oils, seized on the Belgian market, using infrared spectroscopy: Matrix identification and CBD determination, a proof of concept. Drug Test Anal 2024; 16:537-551. [PMID: 37793648 DOI: 10.1002/dta.3583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/04/2023] [Accepted: 09/17/2023] [Indexed: 10/06/2023]
Abstract
The availability of cannabidiol (CBD) oil products has increased in recent years. No analytical controls are mandatory for these products leading to uncertainties about composition and quality. In this paper, a methodology was developed to identify the oil matrix and to estimate the CBD content in such samples, using mid-infrared and near-infrared spectroscopy. Different oils were selected based on the information labeled on products and were bought in food stores in order to create a sample set with a variety of matrices. These oils were spiked with CBD to obtain samples with CBD levels from 0% to 20%. The first part of the study was focused on the qualitative analysis of the oil matrix. A classification model, based on Soft Independent Modeling of Class Analogy, was build using the spiked oils to distinguish between the different oil matrices. For both spectroscopic techniques, the sensitivity, the specificity, the accuracy and the precision were equal to 100%. These models were applied to determine the oil matrix of seized samples. The second part of the study was focused on the quantitative estimation of CBD. After determination of CBD in seized samples using gas chromatography-tandem mass spectrometry, partial least square regression (PLS-R) models were built, one for each matrix in the sample set. Both techniques were able to classify unknown oily samples according to their matrix, and although only few samples were available to evaluate the PLS-R models, the approach clearly showed promising results for the estimation of the CBD content in oil samples.
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Affiliation(s)
- Céline Duchateau
- Pharmacognosy, Bioanalysis and Drug Discovery Unit, RD3, Faculty of Pharmacy, ULB, Brussels, Belgium
- Medicines and Health Products, Scientific Direction Physical and Chemical Health Risks, Sciensano, Brussels, Belgium
| | - Caroline Stévigny
- Pharmacognosy, Bioanalysis and Drug Discovery Unit, RD3, Faculty of Pharmacy, ULB, Brussels, Belgium
| | - Kris De Braekeleer
- Pharmacognosy, Bioanalysis and Drug Discovery Unit, RD3, Faculty of Pharmacy, ULB, Brussels, Belgium
| | - Eric Deconinck
- Pharmacognosy, Bioanalysis and Drug Discovery Unit, RD3, Faculty of Pharmacy, ULB, Brussels, Belgium
- Medicines and Health Products, Scientific Direction Physical and Chemical Health Risks, Sciensano, Brussels, Belgium
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5
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Rafiq H, Hartung J, Schober T, Vogt MM, Carrera DÁ, Ruckle M, Graeff-Hönninger S. Non-Destructive Near-Infrared Technology for Efficient Cannabinoid Analysis in Cannabis Inflorescences. PLANTS (BASEL, SWITZERLAND) 2024; 13:833. [PMID: 38592891 PMCID: PMC10975745 DOI: 10.3390/plants13060833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/07/2024] [Accepted: 03/12/2024] [Indexed: 04/11/2024]
Abstract
In the evolving field of cannabis research, scholars are exploring innovative methods to quantify cannabinoids rapidly and non-destructively. This study evaluates the effectiveness of a hand-held near-infrared (NIR) device for quantifying total cannabidiol (total CBD), total delta-9-tetrahydrocannabinol (total THC), and total cannabigerol (total CBG) in whole cannabis inflorescences. Employing pre-processing techniques, including standard normal variate (SNV) and Savitzky-Golay (SG) smoothing, we aim to optimize the portable NIR technology for rapid and non-destructive cannabinoid analysis. A partial least-squares regression (PLSR) model was utilized to predict cannabinoid concentration based on NIR spectra. The results indicated that SNV pre-processing exhibited superior performance in predicting total CBD concentration, yielding the lowest root mean square error of prediction (RMSEP) of 2.228 and the highest coefficient of determination for prediction (R2P) of 0.792. The ratio of performance to deviation (RPD) for total CBD was highest (2.195) with SNV. In contrast, raw data exhibited the least accurate predictions for total THC, with an R2P of 0.812, an RPD of 2.306, and an RMSEP of 1.651. Notably, total CBG prediction showed unique characteristics, with raw data yielding the highest R2P of 0.806. SNV pre-processing emerges as a robust method for precise total CBD quantification, offering valuable insights into the optimization of a hand-held NIR device for the rapid and non-destructive analysis of cannabinoid in whole inflorescence samples. These findings contribute to ongoing efforts in developing portable and efficient technologies for cannabinoid analysis, addressing the increasing demand for quick and accurate assessment methods in cannabis cultivation, pharmaceuticals, and regulatory compliance.
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Affiliation(s)
- Hamza Rafiq
- Department of Agronomy, Institute of Crop Science, University of Hohenheim, 70599 Stuttgart, Germany
| | - Jens Hartung
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70599 Stuttgart, Germany
| | - Torsten Schober
- Department of Agronomy, Institute of Crop Science, University of Hohenheim, 70599 Stuttgart, Germany
| | | | | | | | - Simone Graeff-Hönninger
- Department of Agronomy, Institute of Crop Science, University of Hohenheim, 70599 Stuttgart, Germany
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6
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LaLone V, Smith D, Diaz-Espinosa J, Rosania GR. Quantitative Raman chemical imaging of intracellular drug-membrane aggregates and small molecule drug precipitates in cytoplasmic organelles. Adv Drug Deliv Rev 2023; 202:115107. [PMID: 37769851 PMCID: PMC10841539 DOI: 10.1016/j.addr.2023.115107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 09/20/2023] [Accepted: 09/25/2023] [Indexed: 10/02/2023]
Abstract
Raman confocal microscopes have been used to visualize the distribution of small molecule drugs within different subcellular compartments. This visualization allows the discovery, characterization, and detailed analysis of the molecular transport phenomena underpinning the Volume of Distribution - a key parameter governing the systemic pharmacokinetics of small molecule drugs. In the specific case of lipophilic small molecules with large Volumes of Distribution, chemical imaging studies using Raman confocal microscopes have revealed how weakly basic, poorly soluble drug molecules can accumulate inside cells by forming stable, supramolecular complexes in association with cytoplasmic membranes or by precipitating out within organelles. To study the self-assembly and function of the resulting intracellular drug inclusions, Raman chemical imaging methods have been developed to measure and map the mass, concentration, and ionization state of drug molecules at a microscopic, subcellular level. Beyond the field of drug delivery, Raman chemical imaging techniques relevant to the study of microscopic drug precipitates and drug-lipid complexes which form inside cells are also being developed by researchers with seemingly unrelated scientific interests. Highlighting advances in data acquisition, calibration methods, and computational data management and analysis tools, this review will cover a decade of technological developments that enable the conversion of spectral signals obtained from Raman confocal microscopes into new discoveries and information about previously unknown, concentrative drug transport pathways driven by soluble-to-insoluble phase transitions occurring within the cytoplasmic organelles of eukaryotic cells.
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Affiliation(s)
- Vernon LaLone
- Cambium Analytica Research Laboratories, Traverse City, MI, United States
| | - Doug Smith
- Cambium Analytica Research Laboratories, Traverse City, MI, United States
| | - Jennifer Diaz-Espinosa
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, United States
| | - Gus R Rosania
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, United States.
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7
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Birenboim M, Kenigsbuch D, Shimshoni JA. Novel fluorescence spectroscopy method coupled with N-PLS-R and PLS-DA models for the quantification of cannabinoids and the classification of cannabis cultivars. PHYTOCHEMICAL ANALYSIS : PCA 2023; 34:280-288. [PMID: 36597766 DOI: 10.1002/pca.3205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 12/22/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
INTRODUCTION Cannabis sativa L. inflorescences are rich in secondary metabolites, particularly cannabinoids. The most common techniques for elucidating cannabinoid composition are expensive technologies, such as high-pressure liquid chromatography (HPLC). OBJECTIVES We aimed to develop and evaluate the performance of a novel fluorescence spectroscopy-based method coupled with N-way partial least squares regression (N-PLS-R) and partial least squares discriminant analysis (PLS-DA) models to replace the expensive chromatographic methods for preharvest cannabinoid quantification. METHODOLOGY Fresh medicinal cannabis inflorescences were collected and ethanol extracts were prepared. Their excitation-emission spectra were measured using fluorescence spectroscopy and their cannabinoid contents were determined by HPLC-PDA. Subsequently, N-PLS-R and PLS-DA models were applied to the excitation-emission matrices (EEMs) for cannabinoid concentration prediction and cultivar classification, respectively. RESULTS The N-PLS-R model was based on a set of EEMs (n = 82) and provided good to excellent quantification of (-)-Δ9-trans-tetrahydrocannabinolic acid, cannabidiolic acid, cannabigerolic acid, cannabichromenic acid, and (-)-Δ9-trans-tetrahydrocannabinol (R2 CV and R2 pred > 0.75; RPD > 2.3 and RPIQ > 3.5; RMSECV/RMSEC ratio < 1.4). The PLS-DA model enabled a clear distinction between the four major classes studied (sensitivity, specificity, and accuracy of the prediction sets were all ≥0.9). CONCLUSIONS The fluorescence spectral region (excitation 220-400 nm, emission 280-550 nm) harbors sufficient information for accurate prediction of cannabinoid contents and accurate classification using a relatively small data set.
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Affiliation(s)
- Matan Birenboim
- Department of Food Science, Institute for Postharvest and Food Sciences, Agricultural Research Organization, Volcani Center, Rishon LeZion, Israel
- Department of Plant Science, The Robert H Smith Faculty of Agriculture, Food and Environment, The Hebrew University, Rehovot, Israel
| | - David Kenigsbuch
- Department of Postharvest Science, Institute for Postharvest and Food Sciences, Agricultural Research Organization, Volcani Center, Rishon LeZion, Israel
| | - Jakob A Shimshoni
- Department of Food Science, Institute for Postharvest and Food Sciences, Agricultural Research Organization, Volcani Center, Rishon LeZion, Israel
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8
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Tran J, Vassiliadis S, Elkins AC, Cogan NOI, Rochfort SJ. Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa. SENSORS (BASEL, SWITZERLAND) 2023; 23:2607. [PMID: 36904818 PMCID: PMC10007171 DOI: 10.3390/s23052607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/22/2023] [Accepted: 02/25/2023] [Indexed: 06/18/2023]
Abstract
Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography-mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II-Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4-100% accuracy prediction), the handheld device also performed well (83.1-100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.
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Affiliation(s)
- Jonathan Tran
- Agriculture Victoria Research, AgriBio Centre, AgriBio, Melbourne, VIC 3083, Australia
| | - Simone Vassiliadis
- Agriculture Victoria Research, AgriBio Centre, AgriBio, Melbourne, VIC 3083, Australia
| | - Aaron C. Elkins
- Agriculture Victoria Research, AgriBio Centre, AgriBio, Melbourne, VIC 3083, Australia
| | - Noel O. I. Cogan
- Agriculture Victoria Research, AgriBio Centre, AgriBio, Melbourne, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Simone J. Rochfort
- Agriculture Victoria Research, AgriBio Centre, AgriBio, Melbourne, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
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9
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Gloerfelt-Tarp F, Hewavitharana AK, Mieog J, Palmer WM, Fraser F, Ansari O, Kretzschmar T. Using a global diversity panel of Cannabis sativa L. to develop a near InfraRed-based chemometric application for cannabinoid quantification. Sci Rep 2023; 13:2253. [PMID: 36755037 PMCID: PMC9908977 DOI: 10.1038/s41598-023-29148-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 01/31/2023] [Indexed: 02/10/2023] Open
Abstract
C. sativa has gained renewed interest as a cash crop for food, fibre and medicinal markets. Irrespective of the final product, rigorous quantitative testing for cannabinoids, the regulated biologically active constituents of C. sativa, is a legal prerequisite across the supply chains. Currently, the medicinal cannabis and industrial hemp industries depend on costly chromatographic analysis for cannabinoid quantification, limiting production, research and development. Combined with chemometrics, Near-InfraRed spectroscopy (NIRS) has potential as a rapid, accurate and economical alternative method for cannabinoid analysis. Using chromatographic data on 12 therapeutically relevant cannabinoids together with spectral output from a diffuse reflectance NIRS device, predictive chemometric models were built for major and minor cannabinoids using dried, homogenised C. sativa inflorescences from a diverse panel of 84 accessions. Coefficients of determination (r2) of the validation models for 10 of the 12 cannabinoids ranged from 0.8 to 0.95, with models for major cannabinoids showing best performance. NIRS was able to discriminate between neutral and acidic forms of cannabinoids as well as between C3-alkyl and C5-alkyl cannabinoids. The results show that NIRS, when used in conjunction with chemometrics, is a promising method to quantify cannabinoids in raw materials with good predictive results.
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Affiliation(s)
| | | | - Jos Mieog
- Southern Cross University, Lismore, NSW, 2480, Australia
| | - William M Palmer
- Research Division, Rapid Phenotyping (Hone), Newcastle, NSW, 2300, Australia
| | - Felicity Fraser
- Research Division, Rapid Phenotyping (Hone), Newcastle, NSW, 2300, Australia
| | - Omid Ansari
- Ecofibre Ltd, Virginia, QLD, 4014, Australia.,Hemp GenTech, Fig Tree Pocket, QLD, 4069, Australia
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10
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Weber A, Hoplight B, Ogilvie R, Muro C, Khandasammy SR, Pérez-Almodóvar L, Sears S, Lednev IK. Innovative Vibrational Spectroscopy Research for Forensic Application. Anal Chem 2023; 95:167-205. [PMID: 36625116 DOI: 10.1021/acs.analchem.2c05094] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Alexis Weber
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States.,SupreMEtric LLC, 7 University Pl. B210, Rensselaer, New York 12144, United States
| | - Bailey Hoplight
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Rhilynn Ogilvie
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Claire Muro
- New York State Police Forensic Investigation Center, Building #30, Campus Access Rd., Albany, New York 12203, United States
| | - Shelby R Khandasammy
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Luis Pérez-Almodóvar
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Samuel Sears
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Igor K Lednev
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States.,SupreMEtric LLC, 7 University Pl. B210, Rensselaer, New York 12144, United States
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11
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Birenboim M, Kengisbuch D, Chalupowicz D, Maurer D, Barel S, Chen Y, Fallik E, Paz-Kagan T, Shimshoni JA. Use of near-infrared spectroscopy for the classification of medicinal cannabis cultivars and the prediction of their cannabinoid and terpene contents. PHYTOCHEMISTRY 2022; 204:113445. [PMID: 36165867 DOI: 10.1016/j.phytochem.2022.113445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
Cannabis sativa L. is used to treat a wide variety of medical conditions, in light of its beneficial pharmacological properties of its cannabinoids and terpenes. At present, the quantitative chemical analysis of these active compounds is achieved through the use of laborious, expensive, and time-consuming technologies, such as high-pressure liquid-chromatography- photodiode arrays, mass spectrometer detectors (HPLC-PDA or MS), or gas chromatography-mass spectroscopy (GC-MS). Hence, we aimed to develop a simple, accurate, fast, and cheap technique for the quantification of major cannabinoids and terpenes using Fourier transform near infra-red spectroscopy (FT-NIRS). FT-NIRS was coupled with multivariate classification and regression models, namely partial least square-discriminant analysis (PLS-DA) and partial least squares regression (PLS-R) models. The PLS-DA model yielded an absolute major class separation (high-THC, high-CBD, hybrid, and high-CBG) and perfect class prediction. Using only three latent variables (LVs), the cross-validation and prediction model errors indicated a low probability of over-fitting the data. In addition, the PLS-DA model enabled the classification of chemovars with genetic-chemical similarities. The classification of high-THCA chemovars was more sensitive and more specific than the classifications of the remaining chemovars. The prediction of cannabinoid and terpene concentrations by PLS-R yielded 11 robust models with high predictive capabilities (R2CV and R2pred > 0.8, RPD >2.5 and RPIQ >3, RMSECV/RMSEC ratio <1.2) and additional 15 models whose performance was acceptable for initial screening purposes (R2CV > 0.7 and R2pred < 0.8, RPD >2 and RPIQ <3, 1.2 < RMSECV/RMSEC ratio <2). Our results confirm that there is sufficient information in the FT-NIRS to develop cannabinoid and terpene prediction models and major-cultivar classification models.
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Affiliation(s)
- Matan Birenboim
- Department of Food Safety, Institute for Postharvest and Food Sciences, Agricultural Research Organization (ARO), Volcani Center, P.O. Box 15159, Rishon LeZion, 7505101, Israel; Department of Plant Science, The Robert H Smith Faculty of Agriculture, Food and Environment, Rehovot, 7610001, Israel
| | - David Kengisbuch
- Department of Food Quality, Institute for Postharvest and Food Sciences, Agricultural Research Organization (ARO), Volcani Center, P.O. Box 15159, Rishon LeZion, 7505101, Israel
| | - Daniel Chalupowicz
- Department of Food Quality, Institute for Postharvest and Food Sciences, Agricultural Research Organization (ARO), Volcani Center, P.O. Box 15159, Rishon LeZion, 7505101, Israel
| | - Dalia Maurer
- Department of Food Quality, Institute for Postharvest and Food Sciences, Agricultural Research Organization (ARO), Volcani Center, P.O. Box 15159, Rishon LeZion, 7505101, Israel
| | - Shimon Barel
- Kimron Veterinary Institute, Department of Toxicology, Bet Dagan, 50250, Israel
| | - Yaira Chen
- Department of Food Safety, Institute for Postharvest and Food Sciences, Agricultural Research Organization (ARO), Volcani Center, P.O. Box 15159, Rishon LeZion, 7505101, Israel
| | - Elazar Fallik
- Department of Food Safety, Institute for Postharvest and Food Sciences, Agricultural Research Organization (ARO), Volcani Center, P.O. Box 15159, Rishon LeZion, 7505101, Israel
| | - Tarin Paz-Kagan
- French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000, Israel
| | - Jakob A Shimshoni
- Department of Food Safety, Institute for Postharvest and Food Sciences, Agricultural Research Organization (ARO), Volcani Center, P.O. Box 15159, Rishon LeZion, 7505101, Israel.
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Kranenburg RF, Ramaker HJ, Weesepoel Y, Arisz PW, Keizers PH, van Esch A, Zieltjens – van Uxem C, van den Berg JD, Hulshof JW, Bakels S, Rijs AM, van Asten AC. The influence of water of crystallization in NIR-based MDMA∙HCl detection. Forensic Chem 2022. [DOI: 10.1016/j.forc.2022.100464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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13
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Kranenburg RF, Ramaker HJ, van Asten AC. On-site forensic analysis of colored seized materials: Detection of brown heroin and MDMA-tablets by a portable NIR spectrometer. Drug Test Anal 2022; 14:1762-1772. [PMID: 35968822 DOI: 10.1002/dta.3356] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/05/2022] [Accepted: 08/11/2022] [Indexed: 01/07/2023]
Abstract
The increasing workload for forensic laboratories and the expanding complexity of the drug market necessitates efficient approaches to detect drugs of abuse. Identification directly at the scene of crime enables investigative forces to make rapid decisions. Additionally, on-site identification of the material also leads to considerable efficiency and cost benefits. As such, paperwork, transportation, and time-consuming analysis in a laboratory may be avoided. Near-infrared (NIR) spectroscopy is an analysis technique suitable for rapid drug testing using portable equipment. A possible limitation of spectroscopic analysis concerns the complexity of seized materials. NIR measurements represent composite spectra for mixtures and diagnostic spectral features can be obscured by excipients such as colorants. Herein, a NIR-based (1300-2600 nm) detection of heroin and MDMA in colored casework (i.e., brown powders and ecstasy tablets) using a portable analyzer is presented. The application includes a multistage data analysis model based on the net analyte signal (NAS) approach. This identification model was specifically designed for mixture analysis and requires a limited set of pure reference spectra only. Consequently, model calibration efforts are reduced to a minimum. A total of 549 forensic samples was tested comprising brown heroine samples and a variety of colored tablets with different active ingredients. This investigation led to a >99% true negative and >93% true positive rate for heroin and MDMA. These results show that accurate on-site detection in colored casework is possible using NIR spectroscopy combined with an efficient data analysis model. These findings may eventually help in the transition of routine forensic laboratories from laboratory-based techniques to portable equipment operated on scene.
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Affiliation(s)
- Ruben F Kranenburg
- Unit Amsterdam, Forensic Laboratory, Dutch National Police, Amsterdam, The Netherlands.,Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, GD, The Netherlands
| | | | - Arian C van Asten
- Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, GD, The Netherlands.,Co van Ledden Hulsebosch Center (CLHC), Amsterdam Center for Forensic Science and Medicine, Amsterdam, The Netherlands
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14
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Kranenburg RF, Ramaker HJ, van Asten AC. Portable near infrared spectroscopy for the isomeric differentiation of new psychoactive substances. Forensic Sci Int 2022; 341:111467. [PMID: 36154979 DOI: 10.1016/j.forsciint.2022.111467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/01/2022] [Accepted: 09/15/2022] [Indexed: 11/04/2022]
Abstract
Rapid and efficient identification of the precise isomeric form of new psychoactive substances (NPS) by forensic casework laboratories is a relevant challenge in the forensic field. Differences in legal status occur for ring-isomeric species of the same class, thus leading to different penalties and judicial control. Portable systems such as near-infrared (NIR) spectroscopy recently emerged as suitable techniques for the on-scene identification of common drugs of abuse such as cocaine, MDMA and amphetamine. This way, the overall forensic process becomes more efficient as relevant information on substance identity becomes available directly at the scene of crime. Currently, no NIR-based applications exist for the rapid, on-scene detection of NPS isomers. Herein, we present the differentiation of cathinone and phenethylamine-type NPS analogues based on their NIR spectrum recorded in 2 seconds on a portable 1350 - 2600 nm spectrometer. A prior developed data analysis model was found suitable for the identification of the methylmethcathinone (MMC) isomers 2-MMC, 3-MMC and 4-MMC. In 51 mixtures and 22 seized casework samples, the correct isomeric form was detected in all cases except for a few mixtures with an active ingredient content of 10 wt%. These results show the feasibility of on-site NPS detection as presumptive test performed directly at the scene of crime with a small size NIR-spectrometer. Additionally, in the illicit drug analysis laboratory the combination of NIR and GC-MS analysis might be suitable for robust identification of NPS isomers and analogues.
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Affiliation(s)
- Ruben F Kranenburg
- Dutch National Police, Unit Amsterdam, Forensic Laboratory, Kabelweg 25, Amsterdam 1014 BA, the Netherlands; Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Postbus 94157, Amsterdam 1090 GD, the Netherlands.
| | - Henk-Jan Ramaker
- TIPb, Koningin Wilhelminaplein 30, Amsterdam 1062 KR, the Netherlands
| | - Arian C van Asten
- Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Postbus 94157, Amsterdam 1090 GD, the Netherlands; Co van Ledden Hulsebosch Center (CLHC), Amsterdam Center for Forensic Science and Medicine, Postbus 94157, Amsterdam 1090 GD, the Netherlands
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15
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Kranenburg RF, Ou F, Sevo P, Petruzzella M, de Ridder R, van Klinken A, Hakkel KD, van Elst DM, van Veldhoven R, Pagliano F, van Asten AC, Fiore A. On-site illicit-drug detection with an integrated near-infrared spectral sensor: A proof of concept. Talanta 2022; 245:123441. [DOI: 10.1016/j.talanta.2022.123441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/24/2022] [Accepted: 04/01/2022] [Indexed: 12/15/2022]
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16
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Deidda R, Dispas A, De Bleye C, Hubert P, Ziemons É. Critical review on recent trends in cannabinoid determination on cannabis herbal samples: From chromatographic to vibrational spectroscopic techniques. Anal Chim Acta 2022; 1209:339184. [DOI: 10.1016/j.aca.2021.339184] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 10/14/2021] [Accepted: 10/16/2021] [Indexed: 12/13/2022]
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17
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Stefkov G, Cvetkovikj Karanfilova I, Stoilkovska Gjorgievska V, Trajkovska A, Geskovski N, Karapandzova M, Kulevanova S. Analytical Techniques for Phytocannabinoid Profiling of Cannabis and Cannabis-Based Products-A Comprehensive Review. Molecules 2022; 27:975. [PMID: 35164240 PMCID: PMC8838193 DOI: 10.3390/molecules27030975] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/31/2021] [Accepted: 01/09/2022] [Indexed: 12/20/2022] Open
Abstract
Cannabis is gaining increasing attention due to the high pharmacological potential and updated legislation authorizing multiple uses. The development of time- and cost-efficient analytical methods is of crucial importance for phytocannabinoid profiling. This review aims to capture the versatility of analytical methods for phytocannabinoid profiling of cannabis and cannabis-based products in the past four decades (1980-2021). The thorough overview of more than 220 scientific papers reporting different analytical techniques for phytocannabinoid profiling points out their respective advantages and drawbacks in terms of their complexity, duration, selectivity, sensitivity and robustness for their specific application, along with the most widely used sample preparation strategies. In particular, chromatographic and spectroscopic methods, are presented and discussed. Acquired knowledge of phytocannabinoid profile became extremely relevant and further enhanced chemotaxonomic classification, cultivation set-ups examination, association of medical and adverse health effects with potency and/or interplay of certain phytocannabinoids and other active constituents, quality control (QC), and stability studies, as well as development and harmonization of global quality standards. Further improvement in phytocannabinoid profiling should be focused on untargeted analysis using orthogonal analytical methods, which, joined with cheminformatics approaches for compound identification and MSLs, would lead to the identification of a multitude of new phytocannabinoids.
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Affiliation(s)
- Gjoshe Stefkov
- Institute of Pharmacognosy, Faculty of Pharmacy, Ss. Cyril and Methodius University, Bul. Majka Tereza 47, 1000 Skopje, North Macedonia; (G.S.); (V.S.G.); (A.T.); (M.K.); (S.K.)
| | - Ivana Cvetkovikj Karanfilova
- Institute of Pharmacognosy, Faculty of Pharmacy, Ss. Cyril and Methodius University, Bul. Majka Tereza 47, 1000 Skopje, North Macedonia; (G.S.); (V.S.G.); (A.T.); (M.K.); (S.K.)
| | - Veronika Stoilkovska Gjorgievska
- Institute of Pharmacognosy, Faculty of Pharmacy, Ss. Cyril and Methodius University, Bul. Majka Tereza 47, 1000 Skopje, North Macedonia; (G.S.); (V.S.G.); (A.T.); (M.K.); (S.K.)
| | - Ana Trajkovska
- Institute of Pharmacognosy, Faculty of Pharmacy, Ss. Cyril and Methodius University, Bul. Majka Tereza 47, 1000 Skopje, North Macedonia; (G.S.); (V.S.G.); (A.T.); (M.K.); (S.K.)
| | - Nikola Geskovski
- Institute of Pharmaceutical Technology, Faculty of Pharmacy, Ss. Cyril and Methodius University, Bul. Majka Tereza 47, 1000 Skopje, North Macedonia;
| | - Marija Karapandzova
- Institute of Pharmacognosy, Faculty of Pharmacy, Ss. Cyril and Methodius University, Bul. Majka Tereza 47, 1000 Skopje, North Macedonia; (G.S.); (V.S.G.); (A.T.); (M.K.); (S.K.)
| | - Svetlana Kulevanova
- Institute of Pharmacognosy, Faculty of Pharmacy, Ss. Cyril and Methodius University, Bul. Majka Tereza 47, 1000 Skopje, North Macedonia; (G.S.); (V.S.G.); (A.T.); (M.K.); (S.K.)
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