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Title: Bridging Disciplines: Applications of Forensic Science and Industrial Hemp
Forensic laboratories are required to have analytical tools to confidently differentiate illegal substances such as marijuana from legal products (i.e., industrial hemp). The Achilles heel of industrial hemp is its association with marijuana. Industrial hemp from the Cannabis sativa L. plant is reported to be one of the strongest natural multipurpose fibers on earth. The Cannabis plant is a vigorous annual crop broadly separated into two classes: industrial hemp and marijuana. Up until the eighteenth century, hemp was one of the major fibers in the United States. The decline of its cultivation and applications is largely due to burgeoning manufacture of synthetic fibers. Traditional composite materials such as concrete, fiberglass insulation, and lumber are environmentally unfavorable. Industrial hemp exhibits environmental sustainability, low maintenance, and high local and national economic impacts. The 2018 Farm Bill made way for the legalization of hemp by categorizing it as an ordinary agricultural commodity. Unlike marijuana, hemp contains less than 0.3% of the cannabinoid, Δ9-tetrahydrocannabinol, the psychoactive compound which gives users psychotropic effects and confers illegality in some locations. On the other hand, industrial hemp contains cannabidiol found in the resinous flower of Cannabis and is purported to have multiple advantageous uses. There is a paucity of investigations of the identity, microbial diversity, and biochemical characterizations of industrial hemp. This review provides background on important topics regarding hemp and the quantification of total tetrahydrocannabinol in hemp products. It will also serve as an overview of emergent microbiological studies regarding hemp inflorescences. Further, we examine challenges in using forensic analytical methodologies tasked to distinguish legal fiber-type material from illegal drug-types.  more » « less
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Frontiers in microbiology
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Abstract Background

    Hemp and marijuana are the two major varieties ofCannabis sativa. While both contain Δ9-tetrahydrocannabinol (THC), the primary psychoactive component ofC. sativa, they differ in the amount of THC that they contain. Presently, U.S. federal laws stipulate thatC. sativacontaining 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 allC. sativamaterials.


    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 recreationalCannabismarket). 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.


    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.


    The results show that the developed approach would significantly aid in the analysis and differentiation ofC. sativaplant 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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  3. Abstract

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