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

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.

Quantitative Raman chemical imaging of intracellular drug-membrane aggregates and small molecule drug precipitates in cytoplasmic organelles

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.

Non-invasive assessment of cultivar and sex of Cannabis sativa L. by means of hyperspectral measurement

Cannabis sativa L. is a versatile crop attracting increasing attention for food, fiber, and medical uses. As a dioecious species, males and females are visually indistinguishable during early growth. For seed or cannabinoid production, a higher number of female plants is economically advantageous. Currently, sex determination is labor-intensive and costly. Instead, we used rapid and non-destructive hyperspectral measurement, an emerging means of assessing plant physiological status, to reliably differentiate males and females. One industrial hemp (low tetrahydrocannabinol [THC]) cultivar was pre-grown in trays before transfer to the field in control soil. Reflectance spectra were acquired from leaves during flowering and machine learning algorithms applied allowed sex classification, which was best using a radial basis function (RBF) network. Eight industrial hemp (low THC) cultivars were field grown on fertilized and control soil. Reflectance spectra were acquired from leaves at early development when the plants of all cultivars had developed between four and six leaf pairs and in three cases only flower buds were visible (start of flowering). Machine learning algorithms were applied, allowing sex classification, differentiation of cultivars and fertilizer regime, again with best results for RBF networks. Differentiating nutrient status and varietal identity is feasible with high prediction accuracy. Sex classification was error-free at flowering but less accurate (between 60% and 87%) when using spectra from leaves at early growth stages. This was influenced by both cultivar and soil conditions, reflecting developmental differences between cultivars related to nutritional status. Hyperspectral measurement combined with machine learning algorithms is valuable for non-invasive assessment of C. sativa cultivar and sex. This approach can potentially improve regulatory security and productivity of cannabis farming.

An electrochemical approach for the prediction of Δ9-tetrahydrocannabinolic acid and total cannabinoid content in Cannabis sativa L

Two electrochemical sensors are proposed here for the first time for the fast screening of cannabinoids in Cannabis sativa L. plant material (inflorescences). The accurate control of cannabinoid content is important for discriminating between recreational, i.e. illegal, and fibre-type C. sativa samples, which differ mainly according to the amount of Δ9-tetrahydrocannabinol (Δ9-THC) and Δ9-tetrahydrocannabinolic acid (Δ9-THCA). Two screen printed electrodes obtained using different electrode materials were tested for the analysis of extracts from recreational and fibre-type C. sativa and their performance was compared with a consolidated method based on high-performance liquid chromatography (HPLC). The voltammetric responses recorded in the different samples reflected the compositional differences of the recreational and fibre-type extracts in accordance with the results of HPLC analyses. Moreover, the quantification of Δ9-THCA and the total cannabinoid content on the basis of the intensity of the peaks of the voltammograms was possible through a simple and fast electrochemical procedure.

An efficient and simple SERS approach for trace analysis of tetrahydrocannabinol and cannabinol and multi-cannabinoid detection

Many countries have legalized cannabis and its derived products for multiple purposes. Consequently, it has become necessary to develop a rapid, effective, and reliable tool for detecting delta-9-tetrahydrocannabinol (THC) and cannabinol (CBN), which are important biologically active compounds in cannabis. Herein, we have fabricated SERS chips by using glancing angle deposition and tuned dimensions of silver nanorods (AgNRs) for detecting THC and CBN at low concentrations. Experimental and computational results showed that the AgNR substrate with film thickness (or nanorod length) of 150 nm, corresponding to nanorod diameter of 79 nm and gap between nanorods of 23 nm, can effectively sense trace THC and CBN with good reproducibility and sensitivity. Due to limited spectral studies of the cannabinoids in previous reports, this work also explored towards identifying characteristic Raman lines of THC and CBN. This information is critical to further reliable data analysis and interpretation. Moreover, multianalyte detection of THC and CBN in a mixture was successfully demonstrated by applying an open-source independent component analysis (ICA) model. The overall method is fast, sensitive, and reliable for sensing trace THC and CBN. The SERS chip-based method and spectral results here are useful for a variety of cannabis testing applications, such as product screening and forensic investigation.

Confirmatory detection and identification of biotic and abiotic stresses in wheat using Raman spectroscopy

Wheat is one of the oldest and most widely cultivated staple food crops worldwide. Wheat encounters an array of biotic and abiotic stresses during its growth that significantly impact the crop yield and consequently global food security. Molecular and imaging methods that can be used to detect such stresses are laborious and have numerous limitations. This catalyzes the search for alternative techniques that can be used to monitor plant health. Raman spectroscopy (RS) is a modern analytical technique that is capable of probing structure and composition of samples non-invasively and non-destructively. In this study, we investigate the accuracy of RS in confirmatory diagnostics of biotic and abiotic stresses in wheat. Specifically, we modelled nitrogen deficiency (ND) and drought, key abiotic stresses, and Russian wheat aphid (Diuraphis noxia) infestation and viral diseases: wheat streak mosaic virus (WSMV) and Triticum mosaic virus (TriMV), economically significant biotic stresses in common bread wheat. Raman spectra as well as high pressure liquid chromatography (HPLC)-based analyses revealed drastically distinct changes in the intensity of carotenoid vibration (1185 cm^-1) and in the concentration of lutein, chlorophyll, and pheophytin biomolecules of wheat, triggered in response to aforementioned biotic and abiotic stresses. The biochemical changes were reflected in unique vibrational signatures in the corresponding Raman spectra, which, in turn could be used for ~100% accurate identification of biotic and abiotic stresses in wheat. These results demonstrate that a hand-held Raman spectrometer could provide an efficient, scalable, and accurate diagnosis of both biotic as well as abiotic stresses in the field.

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.

FTIR and Raman Spectroscopic Characterization of Cannabinoids

Tetrahydrocannabinol (THC), cannabidiol (CBD) and cannabinol (CBN) are three key phytochemical components of cannabis. All three have demonstrated phytochemical activity and are implicated in pharmacological use of cannabis. In this paper, we present the FTIR and Raman spectroscopic characterization of THC, CBD and CBN compounds obtained from certified reference materials. Spontaneous Raman, mid-Infrared (MIR) absorption spectra as well as the analogous surface-enhanced counterparts (Surface enhanced Raman spectroscopy (SERS) and surface enhanced Infrared absorption (SEIRA)) of the cannabinoids are discussed in detail here. We have also examined the laser induced photothermal changes that occur in THC and CBD under spontaneous Raman acquisition conditions as revealed in their Raman spectra. Vibrational spectroscopy provides a robust, portable and cost effective analytical approach to quality control for various medicinal and consumer cannabinoid products. The pure compound spectra of the three cannabinoids presented in this work will help end-users to establish better quantitative analysis methods based on these techniques.

Raman spectroscopy enables highly accurate differentiation between young male and female hemp plants

Hand-held Raman spectroscopy can be used for highly accurate differentiation between young male and female hemp plants. This differentiation is based on significantly different concentration of lutein in these plants. Last year, a global market of only industrial hemp attained the value of USD 4.7 billion. It is by far the fastest growing market with projected growth of 22.5% between 2021 and 2026. Hemp (Cannabis sativa L.) is a dioecious species that has separate male and female plants. In hemp farming, female plants are strongly preferred because male plants do not produce sufficient amount of cannabinoids. Male plants are also eliminated to minimize a possibility of uncontrolled cross-fertilization of plants. Silver treatments can induce development of male flowers on genetically female plants in order to produce feminized seed. Resulting cannabinoid hemp production fields should contain 100% female plants. However, any unintended pollination from male plants can produce unwanted males in production fields. Therefore, there is a growing demand for a label-free, non-invasive, and confirmatory approach that can be used to differentiate between male and female plants before flowering. In this study, we examined the extent to which Raman spectroscopy, an emerging optical technique, can be used for the accurate differentiation between young male and female hemp plants. Our findings show that Raman spectroscopy enables differentiation between male and female plants with 90% and 94% accuracy on the level of young and mature plants, respectively. Such analysis is entirely non-invasive and non-destructive to plants and can be performed in seconds using a hand-held spectrometer. High-performance liquid chromatography (HPLC) analysis and collected Raman spectra demonstrate that this spectroscopic differentiation is based on significantly different concentrations of carotenoids in male vs female plants. These findings open up a new avenue for quality control of plants grown in both field and a greenhouse.

The characteristics of Raman spectroscopy of isomer CBD- and THC-Au nanoparticles using the density functional theory

The isomers cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC) can both be extracted from cannabis. We use density functional theory to study the Raman activity spectra, frontier molecular orbitals, and molecular electrostatic potentials of CBD, THC, and their respective gold complexes. A "selectivity enhancement" phenomenon for the spectral peaks at frequencies of 1144 cm^-1 and 1553 cm^-1 in the Raman spectrum of the CBD-Au_n complex, and at frequencies of 865 cm^-1, 1335 cm^-1, and 1553 cm^-1 in the Raman spectrum of the THC-Au_n complex, was observed and explained. The frontier molecular orbital energy gaps of CBD and THC are 5.4085 eV and 5.4461 eV, respectively, indicating that CBD is more likely to react than THC. The CBD/THC-Au complexes had the strongest chemical activities and greater charge transfer effects with an Au₃ cluster. The most electronegative sites of CBD and THC were found from molecular electrostatic potential (MEP) mapping. It is assumed that these sites are the adsorption sites of the CBD/THC molecules and gold surface. The MEP of the CBD/THC complexes also demonstrates the charge transfer effect between CBD/THC and Au. Both the "selectivity" phenomenon in the Raman activity spectra of the complex and the above assumption are explained by a surface selection rule. The conformation of the CBD/THC molecules on the gold surface are determined, showing that CBD is adsorbed vertically through the resorcinol structure while THC is adsorbed vertically through the tetrahydropyran and benzene ring.

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.

Biochemical Origin of Raman-Based Diagnostics of Huanglongbing in Grapefruit Trees

Biotic and abiotic stresses cause substantial changes in plant biochemistry. These changes are typically revealed by high-performance liquid chromatography (HPLC) and mass spectroscopy-coupled HPLC (HPLC-MS). This information can be used to determine underlying molecular mechanisms of biotic and abiotic stresses in plants. A growing body of evidence suggests that changes in plant biochemistry can be probed by Raman spectroscopy, an emerging analytical technique that is based on inelastic light scattering. Non-invasive and non-destructive detection and identification of these changes allow for the use of Raman spectroscopy for confirmatory diagnostics of plant biotic and abiotic stresses. In this study, we couple HPLC and HPLC-MS findings on biochemical changes caused by Candidatus Liberibacter spp. (Ca. L. asiaticus) in citrus trees to the spectroscopic signatures of plant leaves derived by Raman spectroscopy. Our results show that Ca. L. asiaticus cause an increase in hydroxycinnamates, the precursors of lignins, and flavones, as well as a decrease in the concentration of lutein that are detected by Raman spectroscopy. These findings suggest that Ca. L. asiaticus induce a strong plant defense response that aims to exterminate bacteria present in the plant phloem. This work also suggests that Raman spectroscopy can be used to resolve stress-induced changes in plant biochemistry on the molecular level.

Raman-Based Diagnostics of Stalk Rot Disease of Maize Caused by Colletotrichum graminicola

Stalk rot caused by Colletotrichum graminicola is a disease of worldwide importance. Stalk rot is difficult to detect at the early stages of infection because the fungus colonizes the tissues inside the maize stem. Current diagnostic methods are time-consuming, laborious, and destructive to the stem tissue. We utilized Raman spectroscopy to follow the development of stalk rot in three different maize genotypes grown either in the field or the greenhouse. We then used the acquired spectra to calibrate statistical models to differentiate amongst the different disease timepoints and the genotypes themselves. This non-invasive spectroscopic method enabled high-accuracy identification of stalk rot based on both stalk and leaf spectra. We additionally found that leaf spectra were favorable for identifying maize by genotype. Finally, we identified Raman bands that showed correlation with the sizes of stalk rot-associated lesions in the stems. We demonstrated that Raman spectroscopy is a viable tool for detection of stalk rot disease, as well as potent for the differentiation of maize genotypes.

Phytocannabinomics: Untargeted metabolomics as a tool for cannabis chemovar differentiation

Cannabis sativa is traditionally classified according to five chemotypes based on the concentration of the main phytocannabinoids tetrahydrocannabinol (THC), cannabidiol (CBD), and cannabigerol (CBG). However, cannabis chemovars and varieties very often present similar concentrations of such phytocannabinoids but different chemical profiles, which is unavoidably translated into different pharmacological effects when used for therapeutic purposes. For this reason, a more refined approach is needed for chemovar distinction, which is described in this study and named phytocannabinomics. The classification was achieved by a comprehensive characterization of the phytocannabinoid composition, by liquid chromatography coupled to high-resolution mass spectrometry untargeted metabolomics for the detection of over a hundred phytocannabinoids, and data analysis by chemometrics for chemovars differentiation. The method was developed on fifty cannabis varieties, grown under the same conditions, and was validated to discriminate between the standard chemotypes by partial least squares discriminant analysis. Then, the method was extended to consider the entire chemical variety of the cannabis accessions, by an unsupervised approach based on the principal component analysis. The latter approach clearly indicated several new subgroups within the traditional classifications, which arise from a unique composition of the minor phytocannabinoids. The existence of these subgroups, which were never described before, is of critical importance for evaluating the pharmacological effects of cannabis chemovars.

Raman spectroscopy enables phenotyping and assessment of nutrition values of plants: a review

Our civilization has to enhance food production to feed world's expected population of 9.7 billion by 2050. These food demands can be met by implementation of innovative technologies in agriculture. This transformative agricultural concept, also known as digital farming, aims to maximize the crop yield without an increase in the field footprint while simultaneously minimizing environmental impact of farming. There is a growing body of evidence that Raman spectroscopy, a non-invasive, non-destructive, and laser-based analytical approach, can be used to: (i) detect plant diseases, (ii) abiotic stresses, and (iii) enable label-free phenotyping and digital selection of plants in breeding programs. In this review, we critically discuss the most recent reports on the use of Raman spectroscopy for confirmatory identification of plant species and their varieties, as well as Raman-based analysis of the nutrition value of seeds. We show that high selectivity and specificity of Raman makes this technique ideal for optical surveillance of fields, which can be used to improve agriculture around the world. We also discuss potential advances in synergetic use of RS and already established imaging and molecular techniques. This combinatorial approach can be used to reduce associated time and cost, as well as enhance the accuracy of diagnostics of biotic and abiotic stresses.

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.

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

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.