The classification of Cannabis hemp cultivars by thermal desorption direct analysis in real time mass spectrometry (TD-DART-MS) with chemometrics

Dong, Wen Li; Liang, Jian; Barnett, Isabella; Kline, Paul C.; Altman, Elliot; Zhang, Mengliang

doi:10.1007/s00216-019-02200-7

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…Both sets of spectra were transformed by taking the cubic roots of the ion abundances and normalizing to unit vector length. The cubic root transformation inflates the effect of small values in the data matrix on chemometric modeling, and was shown to outperform other data transformation methods (e.g., logarithmic and other root transformations) on the classification of cannabis cultivars using MS data with chemometrics [48]. The support vector tree classifier was constructed using each set and the average prediction accuracies were compared.…”

Section: Identification Of Cinnamon Species Based On Selected Componentsmentioning

confidence: 99%

Metabolomic profiling and comparison of major cinnamon species using UHPLC–HRMS

2020

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The metabolomic profiles of four major species of cinnamon (Cinnamomum verum, C. burmannii, C. loureiroi, and C. cassia) were investigated by ultra-high-performance liquid chromatographyhigh-resolution mass spectrometry (UHPLC-HRMS). Thirty-six metabolites were tentatively characterized, belonging to various compound groups such as phenolic glycosides, flavan-3-ols, phenolic acids, terpenes, alkaloids, and aldehydes. Principal component analysis (PCA) and partial least squares -discriminant analysis (PLS-DA) on the HRMS data matrix resulted in a clear separation of the four cinnamon species. Coumarin, cinnamaldehyde, methoxycinnamaldehyde, cinnamoyl-methoxyphenyl acetate, proanthocyanidins, and other components varied among the four species. Such variations were used to develop a step-by-step strategy for differentiating the four cinnamon species based on their levels of pre-selected components. This study suggests a significant variation in the phytochemical compositions of different cinnamon species, which have a direct influence on cinnamon's health benefit potentials.

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Section: Identification Of Cinnamon Species Based On Selected Componentsmentioning

confidence: 99%

Metabolomic profiling and comparison of major cinnamon species using UHPLC–HRMS

2020

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“…Some hemp cultivars may not be visually distinct and readily differentiated from each other. Thus, analytical methods, such as gas/liquid chromatography and mass spectrometry (Capriotti et al, 2021), have been proposed for differentiating hemp cultivars based on the chemical fingerprints of the plants (Jin et al, 2017;Wang et al, 2018;Dong et al, 2019). Although accurate and reliable, these methods are slow, costly, require sample preparation and destructive wet-chemistry procedures, and thus are not suitable for rapid, on-site testing applications.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging With Machine Learning to Differentiate Cultivars, Growth Stages, Flowers, and Leaves of Industrial Hemp (Cannabis sativa L.)

Young

Linder

et al. 2022

Front. Plant Sci.

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As an emerging cash crop, industrial hemp (Cannabis sativa L.) grown for cannabidiol (CBD) has spurred a surge of interest in the United States. Cultivar selection and harvest timing are important to produce CBD hemp profitably and avoid economic loss resulting from the tetrahydrocannabinol (THC) concentration in the crop exceeding regulatory limits. Hence there is a need for differentiating CBD hemp cultivars and growth stages to aid in cultivar and genotype selection and optimization of harvest timing. Current methods that rely on visual assessment of plant phenotypes and chemical procedures are limited because of its subjective and destructive nature. In this study, hyperspectral imaging was proposed as a novel, objective, and non-destructive method for differentiating hemp cultivars, growth stages as well as plant organs (leaves and flowers). Five cultivars of CBD hemp were grown greenhouse conditions and leaves and flowers were sampled at five growth stages 2–10 weeks in 2-week intervals after flower initiation and scanned by a benchtop hyperspectral imaging system in the spectral range of 400–1000 nm. The acquired images were subjected to image processing procedures to extract the spectra of hemp samples. The spectral profiles and scatter plots of principal component analysis of the spectral data revealed a certain degree of separation between hemp cultivars, growth stages, and plant organs. Machine learning based on regularized linear discriminant analysis achieved the accuracy of up to 99.6% in differentiating the five hemp cultivars. Plant organ and growth stage need to be factored into model development for hemp cultivar classification. The classification models achieved 100% accuracy in differentiating the five growth stages and two plant organs. This study demonstrates the effectiveness of hyperspectral imaging for differentiating cultivars, growth stages and plant organs of CBD hemp, which is a potentially useful tool for growers and breeders of CBD hemp.

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“…A modified version of DART-MS analysis introduced thermal desorption (TD) into the methodology (TD-DART-MS). One study utilized TD-DART-MS data to differentiate four hemp cultivars using PCA and partial least squares discriminant analysis (PLS-DA) (Dong et al 2019 ). Another found that the application of statistical analysis to DART-MS data derived from methanolic extracts of hemp and marijuana samples revealed the potential for utilizing this method for optimally differentiating hemp and marijuana varieties (Pieslak 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Combined ambient ionization mass spectrometric and chemometric approach for the differentiation of hemp and marijuana varieties of Cannabis sativa

et al. 2023

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Background Hemp and marijuana are the two major varieties of Cannabis sativa. While both contain Δ9-tetrahydrocannabinol (THC), the primary psychoactive component of C. sativa, they differ in the amount of THC that they contain. Presently, U.S. federal laws stipulate that C. sativa containing greater than 0.3% THC is classified as marijuana, while plant material that contains less than or equal to 0.3% THC is hemp. Current methods to determine THC content are chromatography-based, which requires extensive sample preparation to render the materials into extracts suitable for sample injection, for complete separation and differentiation of THC from all other analytes present. This can create problems for forensic laboratories due to the increased workload associated with the need to analyze and quantify THC in all C. sativa materials. Method The work presented herein combines direct analysis in real time—high-resolution mass spectrometry (DART-HRMS) and advanced chemometrics to differentiate hemp and marijuana plant materials. Samples were obtained from several sources (e.g., commercial vendors, DEA-registered suppliers, and the recreational Cannabis market). DART-HRMS enabled the interrogation of plant materials with no sample pretreatment. Advanced multivariate data analysis approaches, including random forest and principal component analysis (PCA), were used to optimally differentiate these two varieties with a high level of accuracy. Results When PCA was applied to the hemp and marijuana data, distinct clustering that enabled their differentiation was observed. Furthermore, within the marijuana class, subclusters between recreational and DEA-supplied marijuana samples were observed. A separate investigation using the silhouette width index to determine the optimal number of clusters for the marijuana and hemp data revealed this number to be two. Internal validation of the model using random forest demonstrated an accuracy of 98%, while external validation samples were classified with 100% accuracy. Discussion The results show that the developed approach would significantly aid in the analysis and differentiation of C. sativa plant materials prior to launching painstaking confirmatory testing using chromatography. However, to maintain and/or enhance the accuracy of the prediction model and keep it from becoming outdated, it will be necessary to continue to expand it to include mass spectral data representative of emerging hemp and marijuana strains/cultivars.

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The classification of Cannabis hemp cultivars by thermal desorption direct analysis in real time mass spectrometry (TD-DART-MS) with chemometrics

Cited by 17 publications

References 28 publications

Metabolomic profiling and comparison of major cinnamon species using UHPLC–HRMS

Metabolomic profiling and comparison of major cinnamon species using UHPLC–HRMS

Hyperspectral Imaging With Machine Learning to Differentiate Cultivars, Growth Stages, Flowers, and Leaves of Industrial Hemp (Cannabis sativa L.)

Combined ambient ionization mass spectrometric and chemometric approach for the differentiation of hemp and marijuana varieties of Cannabis sativa

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