Variable Red Light Exposure Affects Phytochemical Content in Group III Cannabis Cultivars

, , , , , , , , , , ,
Cannabis Science and Technology, July/August 2022, Volume 5, Issue 6
Pages: 30-42

In the work presented here, the authors evaluated the effects of variable red-light exposure on the phytochemical content of four Group III cannabis cultivars.

Light intensity, direction, photoperiod, and spectral composition play essential roles in the growth and phytochemical expression of all photosynthetic plants. In the work presented here, we evaluated the effects of variable red-light exposure on the phytochemical content of four Group III cannabis (cannabidiol- and cannabigerol-dominant) cultivars. In the presence of full-spectrum light emitting diodes, the modulation of red-light frequencies affected the expression of various major and minor cannabinoids, various terpenes, and six anthocyanins. The plants treated with daily on/off cycles of red-light yielded higher average concentrations of total cannabinoids and anthocyanins, but a lower mass of trimmed flower material, compared to control plants exposed to 1000 W full-spectrum-light. These findings represent preliminary efforts to characterize the phytochemical effects of light modulation in Group III cannabis cultivars. Additionally, this study is the first to characterize terpene expression and volatilization in real time using mobile gas chromatography and reports variable anthocyanin expression in cannabidiol- and cannabigerol-dominant cannabis cultivars.

Light intensity and spectrum play pivotal roles in the growth and development of all plants. Photosynthesis, photorespiration, germination, photoperiodic control of flowering, phototropism, and entrainment of circadian rhythms are physiological mechanisms that are all dependent on light for optimal plant growth (1). The efficacy of horticultural lighting can be distilled down to three primary variables: color of the light (frequency), light coverage of the crop, and using adequate power (photon density) to achieve desired yields. McCree (2) examined 22 crop plants and determined their average photon utilization across the frequency spectrum of sunlight. He used these data to produce a graph, known as the McCree Curve, which illustrates how plants utilize light, from ultraviolet and blue short wavelengths through the longer wavelengths of red and far-red light (2). McCree elucidated the effect of blue light (400-500 nm) on facilitating vegetative growth, and further elucidated the effect of yellow, amber, and red light (620-780 nm) in the flowering stages (3). More specifically, blue light affects nuclear receptors that regulate light-dependent growth responses, such as the inhibition of hypocotyl elongation, leaf expansion, stem growth, and phototropism (4-9). Phytochrome and phototropins, principally red (R) and far-red (FR) (600-800 nm) as well as blue (B) (400-500 nm), act through phytochrome and cryptochrome signaling pathways to regulate photomorphogenic and photoperiodic responses (10,11). Phototropins regulate light-dependent movement, a process known as phototropism.

Other investigators have used the McCree Curve as the basis for investigating the effects of manipulating light to enhance growth (12). Early work focused on food plants using metal halide (MH) and high-pressure sodium (HPS) lighting. MH fixtures produce light predominantly in the blue frequencies, whereas HPS lights produce primarily amber and red frequencies. These technologies have been used as the standards in horticultural lighting and continue to be used today. Early light-emitting diodes (LEDs) replicated MH and HPS with diodes that were biphasic and gave sharp peaks of light in the blue and red frequencies, as opposed to a broad spectrum of colors across the photosynthetic range (13). These biphasic LEDs have been supplanted by full-spectrum LEDs that emulate the McCree Curve and the spectrum of natural sunlight.

The effects of light used to grow food and herb plants have been extensively investigated, especially regarding the manipulation of the light spectrum to enhance growth. This research has documented the effects of light spectrum manipulation on vegetative growth and, more recently, the effects of light manipulation during flower and seed production of vegetables and fruits. Photomorphogenic mutants are excellent tools for studying the interactions between light and plant development. Photomorphogenesis refers to how plants modulate their development in response to light. Several sets of genes are responsible: 1. photoreceptor genes; 2. early signaling intermediates; 3. pleiotropic genes; and 4. downstream effectors (14). For example, monogenic recessive mutations for the elevated expression of pigment elements (hp-1, hp-1w, hp-2, hp-2j, hp-2dg) have been described in tomato (Solanum lycopersicum) (15). These mutations cause exaggerated light responsiveness, shortened hypocotyls, elevated anthocyanin content, delayed development, darker pigmentation of leaves and fruits, and later onset of fruit ripening, as compared to isogenic wild type tomatoes. These hp mutants also overproduce several flavonoids, as increased pigmentation was found to be attributable to increased concentrations of chlorophyll in unripened fruits and carotenoids in mature red fruits (16,17). Furthermore, discovery of the det1 and ddb1 mutants led to the coupling of photomorphogenesis and over production of phytonutrients (18). Lastly, the modulation of light signaling to increase the functional quality of fruits was demonstrated by increased synthesis of flavonoids in tomatoes carrying the hp mutation (13,19). This link led to the hypothesis that manipulation of light signaling machinery may be a means of affecting the functional properties in the tomato fruit and other plants and fruits (15). Light signaling genes are highly conserved in the plant kingdom, and it is highly likely that their effects are reproducible in other plant species either closely or distantly related to tomatoes (20). Therefore, we have used these studies as a template to evaluate the effects of variable red-light exposure on phytochemical content in Group III cannabis, (that is, cannabidiol [CBD]- and cannabigerol [CBG]-dominant) cultivars.

The results presented here do not provide the technical resolution to assess the effects of variable light spectra on individual gene expression, such as individual phytochrome receptors, because of the federal restrictions of working with cannabis in a traditional academic research setting. However, these findings provide insight into some of the phytochemical responses to differential light treatments in four Group III cannabis cultivars. Initial studies of the effects of light manipulation of cannabis have focused almost exclusively on increasing the tetrahydrocannabinol (THC) content in Group I flower material (THC-dominant) (21). More recent investigations have focused on enhancing the production of CBD and terpenes (22). These early efforts have focused primarily on the vegetative phase of plant growth. This is because physical growth metrics of interest, such as internodal distance, are easily measured and replicated. In a complimentary fashion, the work presented here focuses on evaluating the effects of light manipulation on the expression of several types of secondary metabolites produced by Group III cannabis cultivars, such as major and minor cannabinoids, various classes of terpenes, and the anthocyanin flavonoids.

Materials and Methods

Group III Cannabis Cultivars and Horticultural Lighting Conditions

In this study, we exposed four different Group III cannabis cultivars to four different lighting conditions during their flowering cycles in individual cultivation tents. The cultivars were produced by ZED Therapeutics and included White Tahoe Cookies x Purple Punch (B73), Blackstar Sherbet (B246), Gatorade Purple Punch (B120), and the CBG-dominant strain CBG127 (B127). Control plants were grown in Tents 1 and 2 under a 1000 W full-spectrum Harvester LED 12 h on/12 h off, and a 600 W full spectrum Harvester LED 12 h on/12 h off, respectively (Curtis Mathes Grow Lights). Experimental plants were grown in Tents 3 and 4 under 600 W white 12 h on/12 h off + 400 W red and far-red 4 h on/4 h off/4 h on/12 h off (slow red cycling), and 600 W white 12 h on/12 h off + 400 W red and far-red 2 h on/1 h off/2 h on/1 h off/2 h on/1 h off/3 h on/12 h off (rapid red cycling), respectively. The lights were positioned approximately 24-in. off the canopy in all four tents to ensure that any differences in photosynthetically active radiation (PAR) were solely attributable to differences in wattage or spectrum (see Figure 1). All plants were grown from clones in custom organic soil from Lane Forest Products (Eugene, Oregon). All plants were watered and fed uniformly across the four different cultivars and lighting conditions, respectively. The water was obtained by reverse osmosis filtration and the nutrients Build a Bloom (Build A Soil) and AGmino, a water soluble, nitrogen fertilizer (14-0-0) (Down to Earth) prepared to final concentrations of 700–900 ppm with a pH between 6.2–6.8, were applied every third day.

Chlorophyll Quantitation

Chlorophyll Content Index (CCI) values were quantified as a surrogate measurement for chlorophyll content (µmol m-2) (23), using an MC-100 chlorophyll content meter by Apogee Instruments. Measurements were taken in triplicate on multiple leaf sites of each plant daily.

Terpene Analysis

Terpene expression and volatilization was analyzed in situ using the NovaTest P300 portable gas chromatograph (GC) by Nanova Environmental. The P300 uses a microfluidic photoionization detector (uPID), which detects more than 400 volatile organic compounds (VOC) including terpenes at the parts-per-billion (ppb) concentration in air. The air was sampled at a rate of 10 mL/min using polytetrafluoroethylene (PTFE) inlet tubing, which was placed inside the cultivation tents during sampling. Sampling was performed on a daily basis during the same time interval. The instrument was calibrated for 19 cannabis-based terpenes (see Table I and Figure 1).

Cannabinoid Analysis

As described previously (24), samples were collected at predetermined times and were analyzed by high performance liquid chromatography (HPLC) methods developed by ZED Therapeutics. Briefly, dried samples were treated with methanol and syringe filtered, prior to separation on a C18 (75 mm x 3.0 mm x 2.2 µm) column and an acetonitrile gradient using Shimadzu’s Cannabis/Hemp Analyzer. Certified reference standards from Cerilliant were used to identify and quantify cannabidivarinic acid (CBDVA), cannabidivarin (CBDV), cannabidiolic acid (CBDA), cannabigerolic acid (CBGA), CBG, CBD, tetrahydrocannabivarin (THCV), tetrahydrocannabivarinic acid (THCVA), cannabinol (CBN), ∆9-tetrahydrocannbinol (∆9-THC), ∆8-tetrahydrocannbinol (∆8-THC), cannabicyclol (CBL), cannabichromene (CBC), tetrahydrocannabinolic acid (THCA), and cannabichromenic acid (CBCA). Calibration was performed using a set of five serial dilutions of each reference standard (2.5, 12.5, 25.0, 125.0, and 250.0 µg/L) with a linearity greater than 0.998. The limits of detection and quantitation were determined experimentally to be 0.5 and 1.0 µg/L, respectively.

Flavonoid Analysis

A detailed methodology of the anthocyanin analysis performed in this study will be reported elsewhere (30). Briefly, dried flower samples were milled using a Retsch CryoMill. The precooling time was set to auto, followed by two cycles of 1-min milling time at a frequency of 25 Hz. Between cycles the system was set for a 1-min intermediate cooling at 5 Hz. Batches of 100 mg of milled hemp inflorescence were loaded into a 1.7 mL microcentrifuge tube. Next, 1 mL of extract solvent was added to the microcentrifuge tube. The extraction solvent consisted of a 70:30:1 mix of methanol, water, and trifluoracetic acid (TFA), respectively. Samples were vortexed until the milled hemp was suspended uniformly, then sonicated for 25 min. Next, the samples were centrifuged for 20 min at 4000xg. The extract was separated from the solid and added dropwise to 5 mL of hexane in a 3-dram glass vial. The extraction process was repeated a second time and the extract was added to the extract–hexane vial. The extract–hexane vial was then sonicated for 30 min and stored at 4 °C overnight. The lower aqueous layer was removed using a pipette. At the hexane–extract solvent interface, there was usually a small quantity of precipitate that was not collected. The aqueous layer was diluted 1:3 with 30:70:1, methanol, water, and TFA, respectively, to a final volume of 1 mL. Solutions were filtrated with a 13 mm nylon 0.2 µm pore size syringe filter. Finally, filtrate solutions were cleaned using solid-phase extraction (SPE) (Waters Oasis Prime HLB 1 cc/30 mg cartridges) and pipetted into liquid chromatography (LC) vials. A Shimadzu LCMS-8040 triple quadrupole instrument equipped with LC-20AD solvent delivery pumps, DGC-20A5 degassing unit, SIL 20AC XR autosampler, CTO 200AC column oven, and CBM-20A system controller was used for the targeted analysis. Instrument control and data acquisition were performed using LabSolutions software v.5.97 (Shimadzu Corp.). Injections of 1 µL were used. Separation was achieved using a Restek Raptor C18 (100 mm x 2.1 mm x 2.7 µm). Mobile phases consisted of water with 0.1% formic acid (solvent A) and acetonitrile with 0.1% formic acid (solvent B). Gradient elution was performed at a flow rate of 0.35 mL/min, at 5% B from 0–2 min, then a ramp to 95% B from 2–16 min with a hold at 95% B from 16–18 min. Re-equilibration to 5% B was performed from 18–21 min. The temperature of the autosampler tray and the oven were set at 10 °C and 50 °C, respectively. Electrospray ionization (ESI) was performed in positive mode. The mass spectrometry (MS) data was collected under the following ESI conditions: nitrogen nebulizing gas and drying gas flows were 3 L/min and 10 L/min, respectively; the desolvation line temperature was 250 °C and the heat block temperature was 400 °C; and the interface voltage was 4.5 kV. Each analyte was monitored by optimized multiple reaction monitoring (MRM) transitions (see Table I).

Statistical Analyses

Unpaired Mann-Whitney analyses were performed using the Prism software suite by GraphPad, assuming non-Gaussian distribution. Similarly, grouped analyses were performed using a two-way ANOVA. The symbols *, **, ***, **** were used to represent p-values less than 0.05, 0.005, 0.001, and 0.0001, respectively. The “ns” is representative of p-values greater than 0.05.

Results and Discussion

Chlorophyll Depletion Over Time

Chlorophyll content was measured under the four different lighting conditions as an indicator of plant metabolism. For example, depleted or rapidly depleting chlorophyll content in the leaves can be indicative of nutrient deficiencies. All plants were provided the same amount of nutrients, which should be revised in subsequent experiments to an “as needed” basis that is predicated on consistent nutrient levels in the watering flowthrough to account for differences in metabolic demand that are triggered by differences in light intensity. For example, the greatest differences in chlorophyll content during the flowering cycle were observed between the 1000 W and 600 W control tents (tents 1 and 2, p-values <0.0001 in three of the four cultivars). This relative depletion of chlorophyll content is likely attributable to insufficient nutrient feeding (see Figure 2). Conversely, there were no differences in CCI across the time course in tent 3 compared to tent 4 for all four cultivars. Interestingly, Gatorade Purple Punch demonstrated the most consistent rate of chlorophyll depletion over time, as evidenced by the lowest variability between the four different lighting conditions (Figure 2). Conversely, Blackstar Sherbet demonstrated the most variability and expressed the highest amounts of anthocyanins (see section titled “Anthocyanin Expression”), which collectively could be an indicator that this cultivar is the most sensitive or responsive to changes in light intensity and spectrum.

Terpene Expression and Volatilization

Volatilized terpenes in the cultivation tents were determined via mobile gas chromatography (GC), a potentially disruptive technology that can provide considerable insight about terpene expression in real time without having to harvest plant material for analysis. Up to seven terpenes were detected on any single day (Figure 3) and 13 of the 19 detectable terpenes were detected at least once during the experimental period in the control (tent 1–1000 W full-spectrum LED). Linalool, isopulegol, and β-caryophyllene were detected more often than other terpenes. Furthermore, the highest total terpene concentrations were 1308 ppb (eucalyptol 608, y-terpinene 219, and geraniol 473 ppb) and were detected in Tent 1. Comparably, one to five different terpenes were detected simultaneously in Tents 2-4, with α-pinene, limonene, and linalool being detected more often than others. Interestingly, the largest spikes in individual terpene expression were attributed to isopulegol (1010 ppb, Tent 2) and geraniol (1291 ppb, Tent 2), a consistent feature across all four lighting conditions (Figure 4). Additionally, notable spikes in linalool and D-limonene were observed from Tents 3 and 4 (Figure 4). As expected, terpene expression increased at later times; however, the total quantities of terpenes did not differ between each of the four different lighting conditions (Figure 4, p-values >0.05). These results suggest that changes in photosynthetic photon dosage (light intensity in the photosynthetic range) and spectrum can alter the terpene profile, but that these changes do not necessarily affect the total amounts of terpenes produced. The cellular mechanisms responsible for the differences in the terpene profiles remain to be determined. However, the notably more diverse terpene profile observed in Tent 1 may suggest that the cumulative effect of enhanced light exposure could be an antagonistic stressor that induces enhanced terpene expression, even during a period of potential nutrient deficiency.

Cannabinoid Production and Yield

Considerable variability was observed in the expression profiles of the primary acidic cannabinoids (CBDA, CBGA, THCA, and CBCA) between the four different cultivars (Figure 5). Interestingly, WTC x PP was the only cultivar that produced detectable amounts of CBDVA (Tents 3 and 4) (see Table II). Also, CBCA was undetectable in the CBG127 grown in tents 2 and 3. The highest amounts of total cannabinoids, CBDA, and CBGA, were produced in Tents 2 and 4, albeit the overall difference was not significant amongst the four different lighting conditions (see Figure 6, p=0.827). The most total dry plant material was collected from Tent 3, with the least amount being collected from Tent 2 (p=0.114). Similarly, the most amount of trimmed flower material was also collected from Tent 3, with the least being produced in Tent 2 (p=0.029). Interestingly, Tent 1 produced 0.11 g/W compared to 0.13 g/W in Tent 2. These results provide further support for the hypothesis that the plants under the 1000 W full-spectrum control light were underfed relative to the plants under the three other lighting conditions, as one would generally expect higher a g/W yield under the higher wattage light when fed adequately.

Anthocyanin Expression

Anthocyanins are a subclass of the flavonoid family of compounds that are starting to attract attention in the cannabis and hemp industries for their medicinal properties. Anthocyanins have an anthocyanidin core structure bound to various 3- and 5-linked glycosides. Besides the variable pH-sensitive pigmentation they confer on many natural products, anthocyanins have been reported to exhibit antioxidant properties (25). Numerous anthocyanins glycosides were detected (30); however, only six prominent compounds, comprised of four different anthocyanidins, were pseudo-quantifiable (Figure 7). These included cyanidin-rutinoside, a predominantly reddish-purple pigment, which has been shown to be a potent sirtuin 6 (SIRT6) activator (26), as well as being involved in telomere maintenance and multiple molecular pathways related to aging. Peonidin-rutinoside was also detected, which is purplish-red, and has been shown to be an inhibitor of human metastatic breast cancer cells (27). Two different delphinidin-rutinoside isomers were detected. Delphinidin is a pH-sensitive antioxidant that is blue in acidic pH and red in basic pH (28). Lastly, two different pelargonidins were detected (diglucoside and rutinoside), which are orange pigments with antioxidant activity (29).

The highest total anthocyanin concentrations were found in Gatorade PP and Blackstar Sherbet, with cyanidin-rutinoside and peonidin-rutinoside being the most abundant compounds. Conversely, substantially lower concentrations were found in CBG127 and WTC x PP. Remarkably, CBG127 was devoid of detectable delphinidin-rutinoside 1, delphinidin-rutinoside 2, and pelargonidin-diglucoside under all four lighting conditions; pelargonidin-rutinoside was only detectable in the plant grown in Tent 2. Similarly, WTC x PP was devoid of pelargonidin-diglucoside under all lighting conditions and delphinidin-rutinoside 1 was only detected in plants grown in Tents 2 and 4. Collectively, the highest total anthocyanin concentrations were detected in Tents 2 and 4, suggesting that a lower photosynthetic photon dosage or a more abundant exposure of red-light wavelengths—albeit intermittent—are favorable for increasing anthocyanin expression (Figure 8). However, the dramatic differences in anthocyanin expression between the four different Group III cannabis cultivars presented here, particularly between Blackstar Sherbet and CBG127, appears to suggest that anthocyanin expression is primarily driven by genetic factors (p=0.0004) as opposed to environmental factors (p=0.0916).

Conclusions

Collectively, these results suggest that polyhybrid cultivars of Group III cannabis are highly responsive to different lighting regimens and that phytochemical content can be affected by the modulation of light intensity and different red-light frequencies. Additional research is required to investigate this phenomenon at the genetic and epigenetic levels. Additionally, these data suggest that Group III cannabis cultivars allocate finite amounts of cellular resources to the production of flower material (yield) or the various classes of phytochemicals. This allocation may represent a balance between the major phytochemical classes. For example, Blackstar Sherbet produces relatively higher amounts of anthocyanins but notably lower amounts of cannabinoids, compared to the other three cultivars in this study (see Table II). This interplay is likely influenced by genetic factors; however, the cellular allocation of resources toward different subclasses of phytochemical compounds has also now been shown to be altered by lighting intensity and spectrum.

Supplemental Information

Figure s1 and Tables sI-sIII were not included in the print version of this article. These graphics are considered supplemental information to the published article version.

References

  1. J. Jiao, O.S. Lau, and X.W. Deng, Nature Reviews Genetics 8, 217-230 (2007).
  2. K.J. McCree, Agricultural and Forest Metereology 9, 191-216 (1972).
  3. I. Terashima, T. Fujita, W.S. Chow, and R. Oguchi, Plant and Cell Physiology 50(4), 684-697 (2009).
  4. A.S. Ahmed, C.P. Sanchez, and M.E. Candela, European Journal of Plant Pathology 106, 817-824 (2000).
  5. T.C.Mockler, H. Guo, H. Yang, H. Duong, and C. Lin, Development 126(10), 2073-2082 (1999).
  6. P.F. Devlin and S.A. Kay, The Plant Cell 12(12), 2499-2509 (2000).
  7. T. Roenneberg and M. Merrow, Current Biology 10(20), 742-745 (2000).
  8. W.R. Briggs and J.M. Christie, Trends in Plant Science 7(5), 204-210 (2002).
  9. A.R. Cashmore, Cell 114(5), 537-543 (2003).
  10. N.A. Eckardt, The Plant Cell 15(5), 1051-1052 (2003).
  11. S. Mathews, Heredity 96(3), 197-204 (2005).
  12. B.S. Wu, A.S. Rufyikiri, V. Orsat, and M.G. Lefsrud, Plant Science 289, 110272 (2019).
  13. K.M. Folta and K.S. Childers, HortScience 42(7), 1957-1964 (2008).
  14. P.H. Quail, Nature Reviews Molecular Cell Biology 3(2), 85-93 (2002).
  15. I. Levin, C.H. Ric de Vos, Y. Tadmor, A.G. Bovy, M.L. Lazarovich, et al., Israel Journal of Plant Sciences 54(3), 179-190 (2006).
  16. R.J. Bino, C.H. Ric de Vos, M. Lieberman, R.D., Hall, A.G. Bovy, et al., New Phytologist 166(2), 427-438 (2005).
  17. I. Kolotilin, H. Koltai, Y. Tadmor, C. Bar-Or, M. Reuveni, et al., Plant Physiology 145(2), 389-401 (2007).
  18. I. Levin, P. Frankel, N. Gilboa, S. Tanny, and A. Lalazar, Theoretical and Applied Genetics 106, 454-460 (2003).
  19. M. Sapir, M. Oren-Shamir, R. Ovadia, M. Reuveni, D. Evenor, et al., Journal of Heredity 99(3), 292-303 (2008).
  20. R. Azari, Y. Tadmor, A. Meir, M. Reuveni, D. Evenor, et al., Biotechnology Advances 28(1), 108-118 (2010).
  21. G. Magagnini, G. Grassi, and S. Kotiranta, Medicinal Cannabis & Cannabinoids1(1), 19-27 (2018).
  22. D. Hawley, T. Graham, M. Stasiak, and M. Dixon, HortScience 53(11), 1593-1599 (2018).
  23. C. Parry, J.M. Blonquist,and B. Bugbee, Plant, Cell & Environment 37(11), 2508-2520 (2014).
  24. Z.L. Hildenbrand, T. Liden, A. Jacques, M. Spurlock, C. West, R.E. Westerfield, and K.A. Schug, Cannabis Science and Technology 4(9), 32-37 (2021).
  25. A. Faria, J. Oliveira, P. Neves, P. Gameiro, C. Santos-Buelga, et al., Journal of Agricultural Food Chemistry 53(17), 6896-6902 (2005).
  26. M. Rahnasto-Rilla, J. Tyni, M. Huovinen, E.Jarho, T. Kulilowicz, et al., Scientific Reports 8, 4163 (2018).
  27. J.Y. Kwon, K.W, Lee, H.J. Hur, and H.J. Lee, Annals of the New York Academy of Sciences 1095(1), (2007).
  28. F. Afaq, D.N. Syed, A. Malik, N. Hadi, S. Sarfaraz, et al., Journal of Investigative Dermatology 127(1). 222-232 (2007).
  29. W. Li, M. Gu, P. Gong, J. Wang, Y. Hu, et al., LWT 147, 111581 (2021).
  30. Pecore, et al., unpublished.

About the Authors

Zacariah L. Hildenbrand is with the Department of Chemistry and Biochemistry at the University of Texas at El Paso in El Paso, Texas; The Collaborative Laboratories for Environmental Analysis and Remediation at the University of Texas at Arlington in Arlington, Texas; Curtis Mathes Corporation in Frisco, Texas; and Medusa Analytical, LLC in Southlake, Texas.

Andrew Grosella, Matthew Spurlock, Adam M. Jacques, Christian West, and Oriah Love are with ZED Therapeutics, Inc. in Cheshire, Oregon. Robert J. Manes and R. Edward Westerfield are with Curtis Mathes Corporation. Tiffany Liden and Kevin A. Schug are with The Collaborative Laboratories for Environmental Analysis and Remediation at the University of Texas at Arlington; Medusa Analytical, LLC; and the Department of Chemistry and Biochemistry at The University of Texas at Arlington in Arlington, Texas. Michael Pecore is with the Department of Chemistry and Biochemistry at The University of Texas at Arlington. Shelly Gao is with Nanova Environmental, Inc, in Columbia, Missouri. Direct correspondence to: zlhildenbrand@utep.eduand kschug@uta.edu.

How to Cite this Article:

Z. Hildenbrand, A. Grosella, R. Manes, M. Spurlock, A. Jacques, C. West, O. Love, R. Westerfield, M. Pecore, T. Liden, S. Gao, and K. Schug, Cannabis Science and Technology® Vol. 5(6), 30-42 (2022).