Confirmatory non-invasive and non-destructive differentiation between hemp and cannabis using a hand-held Raman spectrometer

Sensor for Rapid In-Field Classification of Cannabis Samples Based on Near-Infrared Spectroscopy

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.

Molecularly imprinted polymers (MIP) combined with Raman spectroscopy for selective detection of Δ⁹-tetrahydrocannabinol (THC)

A proof-of-concept sensor is developed for the sensitive and selective detection of Trans-Δ⁹-tetrahydrocannabinol (THC) based on a molecularly imprinted polymer (MIP) synthesized with a THC template which was analyzed using Raman spectroscopy to perform label-free monitoring of THC based on a single identifying Raman peak. The MIP sensor produced a peak at 1614 cm-1 in the Raman spectrum originating from the THC target molecule, allowing for the selective quantification of bound THC with the lowest detection limit of 250 ppm. A higher sensitivity of the MIP to the THC target molecule was observed compared to the non-imprinted polymer (NIP) control which confirmed the presence of THC-specific recognition sites within the synthesized MIP sensing material. The selectivity of this MIP-based sensor was determined by measuring the Raman spectrum of MIP exposed to Cannabidiol (CBD), ethanol, and acetone.

A study of major cannabinoids via Raman spectroscopy and density functional theory

Cannabinoids, a class of molecules specific to the cannabis plant, are some of the most relevant molecules under study today due to their widespread use and varying legal status. Here, we present Raman spectra of a series of eleven cannabinoids and compare them to simulated spectra from density functional theory computations. The studied cannabinoids include three cannabinoid acids (Δ9-THC acid, CBD acid, and CBG acid) and eight neutral ones (Δ9-THC, CBD, CBG, CBDVA, CBDV, Δ8-THC, CBN and CBC). All cannabinoids have been isolated from cannabis plant gown at the University of Mississippi. The data presented in this work represents the most resolved experimental and highest-level simulated spectra available to date for each cannabinoid. All cannabinoids displayed higher peak separation in the experimental spectra than CBGA, which is most likely attributable to physical composition of the samples. The overall agreement between the experimental and simulated spectra is good, however for certain vibrational modes, especially those in the -OH stretching region, deviations are observed due to hydrogen bonding, suggesting that the OH stretching region is a good probe for decarboxylation reactions in these and related species.

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

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.

Semiquantitative Screening of THC Analogues by Silica Gel TLC with an Ag(I) Retention Zone and Chromogenic Smartphone Detection

With the ever-evolving cannabis industry, low-cost and high-throughput analytical methods for cannabinoids are urgently needed. Normally, (potentially) psychoactive cannabinoids, typically represented by Δ9-tetrahydrocannabinol (Δ9-THC), and nonpsychoactive cannabinoids with therapeutic benefits, typically represented by cannabidiol (CBD), are the target analytes. Structurally, the former (tetrahydrocannabinolic acid (THCA), cannabinol (CBN), and THC) have one olefinic double bond and the latter (cannabidiolic acid (CBDA), cannabigerol (CBG), and CBD) have two, which results in different affinities toward Ag(I) ions. Thus, a silica gel thin-layer chromatography (TLC) plate with the lower third impregnated with Ag(I) ions enabled within minutes a digital chromatographic separation of strongly retained CBD analogues and poorly retained THC analogues. The resolution (R_s) between the closest two spots from the two groups was 4.7, which is almost 8 times higher than the resolution on unmodified TLC. After applying Fast Blue BB as a chromogenic reagent, smartphone-based color analysis enabled semiquantification of the total percentage of THC analogues (with a limit of detection (LOD) of 11 ng for THC, 54 ng for CBN, and 50 ng for THCA when the loaded volume is 1.0 μL). The method was validated by analyzing mixed cannabis extracts and cannabis extracts. The results correlated with those of high-performance liquid chromatography with ultraviolet detection (HPLC-UV) (R² = 0.97), but the TLC approach had the advantages of multi-minute analysis time, high throughput, low solvent consumption, portability, and ease of interpretation. In a desiccator, Ag(I)-TLC plates can be stored for at least 3 months. Therefore, this method would allow rapid distinction between high and low THC varieties of cannabis, with the potential for on-site applicability.

Rapid In Situ Detection of THC and CBD in Cannabis sativa L. by 1064 nm Raman Spectroscopy

The need to find a rapid and worthwhile technique for the in situ detection of the content of delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) in Cannabis sativa L. is an ever-increasing problem in the forensic field. Among all the techniques for the detection of cannabinoids, Raman spectroscopy can be identified as the most cost-effective, fast, noninvasive, and nondestructive. In this study, 42 different samples were analyzed using Raman spectroscopy with 1064 nm excitation wavelength. The use of an IR wavelength laser showed the possibility to clearly identify THC and CBD in fresh samples, without any further processing, knocking out the contribution of the fluorescence generated by visible and near-IR sources. The results allow assigning all the Raman features in THC- and CBD-rich natural samples. The multivariate analysis underlines the high reproducibility of the spectra and the possibility to distinguish immediately the Raman spectra of the two cannabinoid species. Furthermore, the ratio between the Raman bands at 1295/1440 and 1623/1663 cm^–1 is identified as an immediate test parameter to evaluate the THC content in the samples.

Classification of Various Marijuana Varieties by Raman Microscopy and Chemometrics

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.

A unique approach for in-situ monitoring of the THCA decarboxylation reaction in solid state

The decarboxylation of Δ9-tetrahydrocannabinolic acid (THCA) plays pivotal role in the potency of medical cannabis and its extracts. Our present work aims to draw attention to mid-infrared (MIR) spectroscopy to in-situ monitor and decipher the THCA decarboxylation reaction in the solid state. The initial TG/DTG curves of THCA, for a first time, outlined the solid-solid decarboxylation dynamics, defined the endpoint of the process and the temperature of the maximal conversion rate, which aided in the design of the further IR experiment. Temperature controlled IR spectroscopy experiments were performed on both THCA standard and cannabis flower by providing detailed band assignment and conducting spectra-structure correlations, based on the concept of functional groups vibrations. Moreover, a multivariate statistical analysis was employed to address the spectral regions of utmost importance for the THCA → THC interconversion process. The principal component analysis model was reduced to two PCs, where PC1 explained 94.76% and 98.21% of the total spectral variations in the THCA standard and in the plant sample, respectively. The PC1 plot score of the THCA standard, as a function of the temperature, neatly complemented to the TG/DTG curves and enabled determination of rate constants for the decarboxylation reaction undertaken on several selected temperatures. The predictive capability of MIR was further demonstrated with PLS (R2X = 0.99, R2Y = 0.994 and Q2 = 0.992) using thermally treated flower samples that covered broad range of THCA/THC content. Consequently, a progress in elucidation of kinetic models of THCA decarboxylation in terms of fitting the experimental data for both, solid state standard substance and a plant flower, was achieved. The results open the horizon to promote an appropriate process analytical technology (PAT) in the outgrowing medical cannabis industry.

Analytical Techniques for Phytocannabinoid Profiling of Cannabis and Cannabis-Based Products-A Comprehensive Review

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.

Acidic Cannabinoid Decarboxylation

Introduction: Cannabis is a valuable plant, cultivated by humans for millennia. However, it has only been in the past several decades that biologists have begun to clarify the interesting Cannabis biosynthesis details, especially the production of its fascinating natural products termed acidic cannabinoids. Discussion: Acidic cannabinoids can experience a common organic chemistry reaction known as decarboxylation, transforming them into structural analogues referred to as neutral cannabinoids with far different pharmacology. This review addresses acidic and neutral cannabinoid structural pairs, when and where acidic cannabinoid decarboxylation occurs, the kinetics and mechanism of the decarboxylation reaction as well as possible future directions for this topic. Conclusions: Acidic cannabinoid decarboxylation is a unique transformation that has been increasingly investigated over the past several decades. Understanding how acidic cannabinoid decarboxylation occurs naturally as well as how it can be promoted or prevented during harvesting or storage is important for the various stakeholders in Cannabis cultivation.

Raman spectroscopy-based diagnostics of water deficit and salinity stresses in two accessions of peanut

Water deficit and salinity are two major abiotic stresses that have tremendous effect on crop yield worldwide. Timely identification of these stresses can help limit associated yield loss. Confirmatory detection and identification of water deficit stress can also enable proper irrigation management. Traditionally, unmanned aerial vehicle (UAV)-based imaging and satellite-based imaging, together with visual field observation, are used for diagnostics of such stresses. However, these approaches can only detect salinity and water deficit stress at the symptomatic stage. Raman spectroscopy (RS) is a noninvasive and nondestructive technique that can identify and detect plant biotic and abiotic stress. In this study, we investigated accuracy of Raman-based diagnostics of water deficit and salinity stresses on two greenhouse-grown peanut accessions: tolerant and susceptible to water deficit. Plants were grown for 76 days prior to application of the water deficit and salinity stresses. Water deficit treatments received no irrigation for 5 days, and salinity treatments received 1.0 L of 240-mM salt water per day for the duration of 5-day sampling. Every day after the stress was imposed, plant leaves were collected and immediately analyzed by a hand-held Raman spectrometer. RS and chemometrics could identify control and stressed (either water deficit or salinity) susceptible plants with 95% and 80% accuracy just 1 day after treatment. Water deficit and salinity stressed plants could be differentiated from each other with 87% and 86% accuracy, respectively. In the tolerant accessions at the same timepoint, the identification accuracies were 66%, 65%, 67%, and 69% for control, combined stresses, water deficit, and salinity stresses, respectively. The high selectivity and specificity for presymptomatic identification of abiotic stresses in the susceptible line provide evidence for the potential of Raman-based surveillance in commercial-scale agriculture and digital farming.

Raman Spectroscopy Can Distinguish Glyphosate-Susceptible and -Resistant Palmer Amaranth (Amaranthus palmeri)

The non-judicious use of herbicides has led to a widespread evolution of herbicide resistance in various weed species including Palmer amaranth, one of the most aggressive and troublesome weeds in the United States. Early detection of herbicide resistance in weed populations may help growers devise alternative management strategies before resistance spreads throughout the field. In this study, Raman spectroscopy was utilized as a rapid, non-destructive diagnostic tool to distinguish between three different glyphosate-resistant and four -susceptible Palmer amaranth populations. The glyphosate-resistant populations used in this study were 11-, 32-, and 36-fold more resistant compared to the susceptible standard. The 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene copy number for these resistant populations ranged from 86 to 116. We found that Raman spectroscopy could be used to differentiate herbicide-treated and non-treated susceptible populations based on changes in the intensity of vibrational bands at 1156, 1186, and 1525 cm-1 that originate from carotenoids. The partial least squares discriminant analysis (PLS-DA) model indicated that within 1 day of glyphosate treatment (D1), the average accuracy of detecting herbicide-treated and non-treated susceptible populations was 90 and 73.3%, respectively. We also found that glyphosate-resistant and -susceptible populations of Palmer amaranth can be easily detected with an accuracy of 84.7 and 71.9%, respectively, as early as D1. There were relative differences in the concentration of carotenoids in plants with different resistance levels, but these changes were not significant. The results of the study illustrate the utility of Raman spectra for evaluation of herbicide resistance and stress response in plants under field conditions.

Non-Invasive Identification of Nutrient Components in Grain

Digital farming is a modern agricultural concept that aims to maximize the crop yield while simultaneously minimizing the environmental impact of farming. Successful implementation of digital farming requires development of sensors to detect and identify diseases and abiotic stresses in plants, as well as to probe the nutrient content of seeds and identify plant varieties. Experimental evidence of the suitability of Raman spectroscopy (RS) for confirmatory diagnostics of plant diseases was previously provided by our team and other research groups. In this study, we investigate the potential use of RS as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of nutrient components in the grains from 15 different rice genotypes. We demonstrate that spectroscopic analysis of intact rice seeds provides the accurate rice variety identification in ~86% of samples. These results suggest that RS can be used for fully automated, fast and accurate identification of seeds nutrient components.

Mid-infrared spectroscopy as process analytical technology tool for estimation of THC and CBD content in Cannabis flowers and extracts

Tetrahydrocannabinol (THC) and cannabidiol (CBD) are the most notable Cannabis components with pharmacological activity and their content in the plant flowers and extracts are considered as critical quality parameters. The new Medical Cannabis industry needs to adopt the quality standards of the pharmaceutical industry, however, the variability of phytocannabinoids content in the plant material often exerts an issue in the inconsistency of the finished product quality parameters. Sampling problems and sample representativeness is a major limitation in the end-point testing, particularly when the expected variation of the product quality parameters is high. Therefore, there is an obvious need for the introduction of Process Analytical Technology (PAT) for continuous monitoring of the critical quality parameters throughout the production processes. Infrared spectroscopy is a promising analytical technique that is consistent with the PAT requirements and its implementation depends on the advances in instrumentation and chemometrics that will facilitate the qualitative and quantitative aspects of the technique. Our present work aims in highlighting the potential of mid-infrared (MIR) spectroscopy as PAT in the quantification of the main phytocannabinoids (THC and CBD), considered as critical quality/material parameters in the production of Cannabis plant and extract. A detailed assignment of the bands related to the molecules of interest (THC, CBD) was performed, the spectral features of the decarboxylation of native flowers were identified, and the specified bands for the acid forms (THCA, CBDA) were assigned and thoroughly explained. Further, multivariate models were constructed for the prediction of both THC and CBD content in extract and flower samples from various origins, and their prediction ability was tested on a separate sample set. Savitskzy-Golay smoothing and the second derivative of the native MIR spectra (1800-400 cm^-1 region) resulted in best-fit parameters. The PLS models presented satisfactory R2Y and RMSEP of 0.95 and 3.79% for THC, 0.99 and 1.44% for CBD in the Cannabis extract samples, respectively. Similar statistical indicators were noted for the Partial least-squares (PLS) models for THC and CBD prediction of decarboxylated Cannabis flowers (R2Y and RMSEP were 0.99 and 2.32% for THC, 0.99 and 1.33% for CBD respectively). The VIP plots of all models demonstrated that the THC and CBD distinctive band regions bared the highest importance for predicting the content of the molecules of interest in the respected PLS models. The complexity of the sample (plant tissue or plant extract), the variability of the samples regarding their origin and horticultural maturity, as well as the non-uniformity of the plant material and the flower-ATR crystal contact (in the case of Cannabis flowers) were governing the accuracy descriptors. Taking into account the presented results, ATR-MIR should be considered as a promising PAT tool for THC and CBD content estimation, in terms of critical material and quality parameters for Cannabis flowers and extracts.

Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review

Digital farming is a novel agricultural philosophy that aims to maximize a crop yield with the minimal environmental impact. Digital farming requires the development of technologies that can work directly in the field providing information about a plant health. Raman spectroscopy (RS) is an emerging analytical technique that can be used for non-invasive, non-destructive, and confirmatory diagnostics of diseases, as well as the nutrient deficiencies in plants. RS is also capable of probing nutritional content of grains, as well as highly accurate identification plant species and their varieties. This allows for Raman-based phenotyping and digital selection of plants. These pieces of evidence suggest that RS can be used for chemical-free surveillance of plant health directly in the field. High selectivity and specificity of this technique show that RS may transform the agriculture in the US. This review critically discusses the most recent research articles that demonstrate the use of RS in diagnostics of abiotic and abiotic stresses in plants, as well as the identification of plant species and their nutritional analysis.

Portable Raman leaf-clip sensor for rapid detection of plant stress

Precision agriculture requires new technologies for rapid diagnosis of plant stresses, such as nutrient deficiency and drought, before the onset of visible symptoms and subsequent yield loss. Here, we demonstrate a portable Raman probe that clips around a leaf for rapid, in vivo spectral analysis of plant metabolites including carotenoids and nitrates. We use the leaf-clip Raman sensor for early diagnosis of nitrogen deficiency of the model plant Arabidopsis thaliana as well as two important vegetable crops, Pak Choi (Brassica rapa chinensis) and Choy Sum (Brassica rapa var. parachinensis). In vivo measurements using the portable leaf-clip Raman sensor under full-light growth conditions were consistent with those obtained with a benchtop Raman spectrometer measurements on leaf-sections under laboratory conditions. The portable leaf-clip Raman sensor offers farmers and plant scientists a new precision agriculture tool for early diagnosis and real-time monitoring of plant stresses in field conditions.

Raman Spectroscopy Enables Non-invasive and Confirmatory Diagnostics of Salinity Stresses, Nitrogen, Phosphorus, and Potassium Deficiencies in Rice

Proper management of nutrients in agricultural systems is critically important for maximizing crop yields while simultaneously minimizing the health and environmental impacts of pollution from fertilizers. These goals can be achieved by timely confirmatory diagnostics of nutrient deficiencies in plants, which enable precise administration of fertilizers and other supplementation in fields. Traditionally, nutrient diagnostics are performed by wet-laboratory analyses, which are both time- and labor-consuming. Unmanned aerial vehicle (UAV) and satellite imaging have offered a non-invasive alternative. However, these imaging approaches do not have sufficient specificity, and they are only capable of detecting symptomatic stages of nutrient deficiencies. Raman spectroscopy (RS) is a non-invasive and non-destructive technique that can be used for confirmatory detection and identification of both biotic and abiotic stresses on plants. Herein, we show the use of a hand-held Raman spectrometer for highly accurate pre-symptomatic diagnostics of nitrogen, phosphorus, and potassium deficiencies in rice (Oryza sativa). Moreover, we demonstrate that RS can also be used for pre symptomatic diagnostics of medium and high salinity stresses. A Raman-based analysis is fast (1 s required for spectral acquisition), portable (measurements can be taken directly in the field), and label-free (no chemicals are needed). These advantages will allow RS to transform agricultural practices, enabling precision agriculture in the near future.

Raman Spectroscopy vs Quantitative Polymerase Chain Reaction In Early Stage Huanglongbing Diagnostics

Raman spectroscopy (RS) is an emerging analytical technique that can be used to develop and deploy precision agriculture. RS allows for confirmatory diagnostic of biotic and abiotic stresses on plants. Specifically, RS can be used for Huanglongbing (HLB) diagnostics on both orange and grapefruit trees, as well as detection and identification of various fungal and viral diseases. The questions that remain to be answered is how early can RS detect and identify the disease and whether RS is more sensitive than qPCR, the "golden standard" in pathogen diagnostics? Using RS and HLB as case study, we monitored healthy (qPCR-negative) in-field grown citrus trees and compared their spectra to the spectra collected from healthy orange and grapefruit trees grown in a greenhouse with restricted insect access and confirmed as HLB free by qPCR. Our result indicated that RS was capable of early prediction of HLB and that nearly all in-field qPCR-negative plants were infected by the disease. Using advanced multivariate statistical analysis, we also showed that qPCR-negative plants exhibited HLB-specific spectral characteristics that can be distinguished from unrelated nutrition deficit characteristics. These results demonstrate that RS is capable of much more sensitive diagnostics of HLB compared to qPCR.

Confirmatory non-invasive and non-destructive identification of poison ivy using a hand-held Raman spectrometer

Poison ivy (Toxicodendron radicans) is a forest understory plant that grows throughout the United States, Canada and Mexico. The plant contains urushiol oils, a mixture of pentadecylcatechols, that cause severe allergic reactions on skin including reddish inflammation, uncoloured bumps and blistering. Such allergic reactions develop within hours or days, which facilitates unknowing spread of the urushiol inside the house. This enables continuous contact with urushiol extending the length of time of the rash. It should be noted that apart from extensive washing with soap and cold water, there is no direct way to treat urushiol-induced allergic reactions. In these circumstances, the best practice is to avoid contact with the plant. However, differentiating poison ivy from other plants requires sophisticated botanical experience that is not possessed by a vast majority of people. To overcome this limitation, we developed a confirmatory, label-free, non-invasive and non-destructive approach for detection and identification of poison ivy. We show that using a hand-held Raman spectrometer, 100% accurate identification of this species can be performed in only one second. We also demonstrate that in combination with partial least square discriminant analysis (PLS-DA), Raman spectroscopy is capable of distinguishing poison ivy from more than fifteen different plant species, including weeds, grasses and trees. The use of a hand-held spectrometer on a motorized robotic platform or an unmanned aerial vehicle (UAV) can be used for automated surveillance of household and agricultural spaces enabling confirmatory detection and identification of this dangerous plant species.

Poison ivy (Toxicodendron radicans) is a noxious weed that grows throughout North America and induces terrible rashes on contact. Using a portable Raman device, we identified these plants and differentiated them from other species with high accuracy.

Raman-Based Differentiation of Hemp, Cannabidiol-Rich Hemp, and Cannabis

Hemp (Cannabis sativa) has been used to treat pain as far back as 2900 B.C. Its pharmacological effects originate from a large variety of cannabinols. Although more than 100 different cannabinoids have been isolated from Cannabis plants, clear physiological effects of only a few of them have been determined, including delta-9 tetrahydrocannabinol (THC), cannabidiol (CBD), and cannabigerol (CBG). While THC is an illicit drug, CBD and CBG are legal substances that have a variety of unique pharmacological properties such as the reduction of chronic pain, inflammation, anxiety, and depression. Over the past decade, substantial efforts have been made to develop Cannabis varieties that would produce large amounts of CBD and CBG. Ideally, such plant varieties should produce very little (below 0.3%) if any THC to make their cultivation legal. The amount of cannabinoids in the plant material can be determined using high performance liquid chromatography (HPLC). This analysis, however, is nonportable, destructive, and time and labor consuming. Our group recently proposed to use Raman spectroscopy (RS) for confirmatory, noninvasive, and nondestructive differentiation between hemp and cannabis. The question to ask is whether RS can be used to detect CBD and CBG in hemp, as well as enable confirmatory differentiation between hemp, cannabis, and CBD-rich hemp. In this manuscript, we show that RS can be used to differentiate between cannabis, CBD-rich plants, and regular hemp. We also report spectroscopic signatures of CBG, cannabigerolic acid (CBGA), THC, delta-9-tetrahydrocannabinolic acid (THCA), CBD, and cannabidiolic acid (CBDA) that can be used for Raman-based quantitative diagnostics of these cannabinoids in plant material.