Whether you are trying to decipher a certificate of analysis, minimize analysis error, or develop a representative sampling plan, statistics are an important part of cannabis analysis. In this first of a series of columns devoted to statistical concepts related to cannabis analysis, I will introduce the normal distribution, standard deviation, and how they determine how precision and accuracy are calculated.

Statistics is a large and complex mathematical field, and I am neither a mathematician nor a statistician. I am a PhD physical chemist who has taken advanced undergraduate courses in statistics, have used statistics frequently throughout a 40 year career, and have discussed the applications of statistics to spectroscopy in my book 

Quantitative Spectroscopy: Theory and Practice

 (1), which I will be citing frequently in this article. The warning is that since statistics is not my full time job, my writing on the subject may be overly simplified for some or not detailed enough for others. If you want to learn more about statistics, I recommend the books 

Probability and Statistics for Scientists and Engineers

The Gaussian or Normal Distribution and Standard Deviation

When I first took freshman physics, me and my two lab partners entered the laboratory and were presented with a meter stick and told to measure the length of a table 10 times each. I thought this was absurd. I measured the table once, it was clearly 2 meters long, so why did we need to measure the table 29 more times? Surely all the other measurements would be the same as what I obtained. I at least thought that was true until we compiled all the measurements. Not only were the measurements not all the same, but there was a significant scatter to them. Most were at 2 meters ± 1 cm, but some were as high 2.2 and as low as 1.8 meters. And when we plotted the number of measurements at a given length versus length, to my surprise we obtained the distribution seen in Figure 1. This is what some of us know as a “bell shaped curve,” but what mathematicians call a “Gaussian” or “Normal” distribution (4).

“Gaussian” is named after Carl Friedrich Gauss, a 19th century German mathematician and scientist who contributed much to our understanding of statistics and to other fields such as magnetism (5). I was shocked not only that our measurements were a distribution, but that the shape of the curve was well known, characterized by known mathematics, and widely used in physics and other sciences. It turns out that being human beings we each made mistakes in measuring the length of the table, and that the size of the errors we made were randomly distributed resulting in a Gaussian distribution. In general, a set of measurements containing random error will follow a Gaussian distribution, such as a set of potency measurements on the same marijuana sample. Given how goofy and random my lab mates and I were at the age of 19, I am surprised the size of our errors weren’t bigger than what we measured!

The top of a Gaussian distribution, in our case the table length measured most often, is the average for the data plotted. The average of the 30 measurements was 2 meters. I think we all may know how to calculate an average, but the formula is given in equation 1 just to be clear.

where µ is the average; n is the total number of measurements in a dataset; Xi is the ith value in a data set; i is 1…n; and ∑i is the mathematical notation to add sum the members of a dataset from the first to nth members.

In other words, to calculate an average take a set of data with n numbers in it, add them together, and then divide by n.

The Gaussian distribution also contains what are called 

. The inflection points of a graph are where the concavity changes sign as illustrated in Figure 2.

Figure 2 uses an infrared (IR) absorbance band for illustration, but the idea of inflection points also carries over to other spectroscopic peaks, peaks in chromatograms, and so on. Concavity measures the curvature of a function, be it up or down. Imagine pouring water on the peak in Figure 2. The spots where water pools are concave up, the spots from where it drains are concave down, and the transition spots are the inflection points.

In the normal distribution seen in Figure 1 the half-width of the function at the inflection points is called the 

 or σ, and at this point the function is 2σ wide (6). The standard deviation is calculated via equation 2.

where σ is the standard deviation; µ is the average; n is the number of measurements; i is 1…n; and Xi is the ith value.

The numerator in equation 2 contains the difference between any given measurement in a dataset Xi and the average of that dataset µ, which we will call a 

. Each difference is then squared, and they are all added together. The differences are squared so that they are all positive, and the squared difference gives the absolute distance between a data point and the average. Summing unsquared differences would defeat the purpose here because when summed the positive and negative differences would cancel each other. We then divide by n-1 and take the square root obtaining the result, the standard deviation, which is essentially the average absolute difference for the dataset.

Here is an example calculation of a standard deviation using real world analytical chemistry data. Figure 3 is a series of five mid-IR spectra of isopropyl (“rubbing”) alcohol dissolved in water. Thanks to Beer’s Law the peak size and area at 2973 cm-1 in these spectra vary with concentration (1,7). The volume percent isopropyl alcohol (IPA) and peak area data are seen in Table I.

By the way, I have too often heard in the cannabis analysis world that mid-IR spectroscopy is not quantitative. This is of course news to me since I have written a book on the subject (1). Many high pressure liquid chromatography (HPLC) cannabinoid detection methods use ultraviolet-visible (UV-vis) detection (8–10), which makes use of Beer’s Law. There are no theoretical or practical reasons why Beer’s Law won’t work at other wavelengths including the mid-IR, so it is absurd to say that Beer’s Law doesn’t work in a specific wavelength region. To put this issue to rest, a plot of the IPA% and peak area data from Table I is seen in Figure 4.

There is clearly an excellent linear relationship between volume percent and peak area in Figure 4 as expected from Beer’s Law, and the correlation coefficient (1), a measure of model quality we will discuss in a future column, is 0.999 where 1 is a perfect model. These data are proof that mid-IR spectroscopy is quantitative. So please no more blanket statements about infrared spectroscopy not being quantitative as it will make me pull my hair out, and believe me I don’t have that many hairs left!

To calculate the standard deviation of a data set we first have to calculate its average. For the peak area data in Table 1 the average is 5.42. Table II shows the peak areas, 

, in the left hand column, the differences between each data point and the average, 

, in the middle column, and the squared differences, 

, in the right hand column, note the squared differences are all positive. The sum of the squared differences, ∑i = (Xi – µ)2, the numerator in equation 2, is in the bottom row and is 67.97.

To complete the standard deviation calculation we take 67.97, divide it by n-1 = 4, and take the square root. The answer is 4.12. What this calculation tells us is that on average each data point is 4.12 peak area units away from the average.

The Role of Standard Deviation in Measuring Precision and Accuracy

So, what does all this standard deviation stuff have to do with cannabis analysis? Recall from an earlier column the definitions of precision and accuracy (11). Briefly, precision measures how reproducible a set of measurements is, while accuracy is a measure of how far off you are from the true value. Table III shows a set of hypothetical weight percent (Wt.%) total tetrahydrocannabinol (THC) values measured on the same dried, ground marijuana sample.

Since these data are repeat measurements on the same sample, we can use this dataset to calculate the precision of the method used to make these measurements. The standard deviation of a set of repeated measurements is a common measure of precision. Recall that we must calculate the average of a dataset before calculating the standard deviation. Using equation 1, the average for the data in Table III is 24.4 Wt.% total THC, as indicated in the table. Using equation 2, we find that the standard deviation of these five measurements is 1.4 Wt. % total THC. Note that the standard deviation is reported in the units of the data set, be it Wt.% total THC, wt.% beta-caryophyllene, or parts per million of lead. So, what does the standard deviation mean in the context of the data in Table III? We would say that the precision of the method used to generate the data in Table III is ±1.4 Wt.% total THC, that is, the precision of the data is ±1.4 Wt.%.

What about standard deviation and accuracy? Remember that to calculate accuracy we need to have reference samples for which we know the correct answer. In previous columns I have bemoaned the lack of what the National Institutes of Standards and Technology (NIST) calls 

 or MRMs (12,13). An MRM is a sample of the matrix being analyzed with a known amount of the analytes of interest. In the cannabis industry examples of MRMs would be dried ground plant samples, extracts, or distillates with known cannabinoid concentrations. We are sorely lacking these in cannabis analysis except for hemp. The University of Kentucky has issued a series of four dried, ground hemp samples that have been analyzed by more than 60 laboratories across the United States (14). The certificates of analysis (COAs) of these samples contain the average of the analyses of these 60 laboratories for a number of cannabinoids including total THC, total cannabidiol (CBD), cannabidiolic acid (CBDA), and ∆-9 THC. The values in these COAs can be used as the right answers in accuracy calculations.

I have analyzed these four hemp matrix reference materials using mid-IR spectroscopy and published the results in this esteemed journal (15). A comparison of the known and measured total THC values for these samples is seen in Table IV.

The left hand column in Table IV contains the sample names, the second column shows the weight percent total THC as the average of the 60+ laboratories that have analyzed these samples by chromatography. The third column shows the total THC values as measured by mid-IR spectroscopy, and the fourth column shows the difference between the two methods for each sample. The standard deviation of a dataset like the one seen in column 4 of Figure 4 is a common measure of accuracy (1). The standard deviation here is 0.03 Wt.% total THC. The “SEP” in Table IV stands for standard error of prediction, which is the standard deviation between the known and predicted values for a set of validation samples. Given the data in Table IV we would then report that the accuracy of mid-IR spectroscopy for determining total THC in dried, ground hemp is ±0.03 Wt.% reported as a standard error of prediction.

When reporting accuracies sometimes the values can be reported as multiple of the standard deviation. For example, here we reported the accuracy as ±1σ; sometimes the accuracy is reported as ±2σ, or other multiples of σ. Thus, it is important to report what multiple of the standard deviation is being used in your accuracy measurement. To be complete then for Table IV, it is correct to say that “the accuracy for the determination of total THC in dried, ground hemp by mid-IR spectroscopy is 0.03 Wt.% as reported as a standard error of prediction at ±1 σ.” Why different multiples of the standard deviation are used to calculate accuracy gets into confidence limits, a topic for a future column.

The normal or Gaussian distribution is a result of the random error in a set of measurements. The top of this distribution is the average, and its width is characterized by the standard deviation. We saw how to calculate an average and calculate a standard deviation. We then used standard deviation calculations to determine the precision and accuracy in sets of total THC measurements.

Probability and Statistics for Scientists and Engineers,

 2nd Edition (Macmillan, New York, NY, 1978).

 2nd Edition, (Academic Press, Boston, MA 2003).

https://en.wikipedia.org/wiki/Normal_distribution

https://en.wikipedia.org/wiki/Carl_Friedrich_Gauss

https://en.wikipedia.org/wiki/Standard_deviation

M. Giese, M. Lewis, L. Giese, and K. Smith, 

A. Hazekamp, A. Peltenburg, R. Verpoorte, and C. Giroud, 

B. De Backer, B. Debrus, P. Lebrun, L. Theunis, N. Dubois, L. Decock, A Verstraete, P. Hubert, and C. Charlier, 

University of Kentucky Hemp Proficiency Testing Program (uky.edu)

, is Founder, CEO, and Chief Technical Officer of Big Sur Scientific. He is the inventor of the BSS series of patented mid-infrared based cannabis analyzers. Dr. Smith has done pioneering research and published numerous peer-reviewed papers on the application of mid-infrared spectroscopy to cannabis analysis, and sits on the editorial board of 

. He has worked as a laboratory director for a cannabis extractor, as an analytical chemist for Waters Associates and PerkinElmer, and as an analytical instrument salesperson. He has more than 30 years of experience in chemical analysis and has written three books on the subject. Dr. Smith earned his PhD on physical chemistry from Dartmouth College.
Direct correspondence to: 

Articles

Cannabis Analysis 

In part I of this series on statistical concepts related to cannabis analysis, we introduce the normal distribution, standard deviation, and how to calculate precision and accuracy.

Statistics for Cannabis Analysis, Part I: Standard Deviation and Its Relationship to Accuracy and Precision

Better seed genetics, power-free tunable lighting, and other new scientific discoveries are making headway in what has become an ag-tech leading industry.

There is so much going on in the world of cannabis cultivation today, much of it propelling a deeper look into how to control the plant throughout its grow cycle, and work with genetics to reliably produce the same plant with the same effects grow after grow. Methods of achieving standardization and uniformity are driving new discoveries.

This “Tech Innovations” column will examine new ideas in cultivation including managing mold and mildew issues through genetics; keeping the grow facility safe from pathogens; new lighting ideas that do not use electricity; and a better understanding of editing the gene of the cannabis seed.

Generally, cannabis plants are grown from clones—from cuttings of female plants—which will produce a uniform product. But there are problems with that. “Some of the really negative things about growing from clones is the transmission of pests and pathogens,” Nathaniel Pennington said.

Pennington is the founder and CEO of Humboldt Seed Company. He is a former legacy cannabis producer, now a scientist and breeder. “That transmission is probably the worst thing about clone propagation. You can get away without using pesticides and cultivate really nice cannabis if you start clean with seed.”

Seed is basically nature’s tissue culture, he explained, and that is the cleanest and most sterile way to share living plant tissue. “The seed itself is this new life that doesn’t carry with it, 99% of the time, a virus or some other pathogen that might come from a mother plant where you get clones,” Pennington said.

Humboldt Seed Company has developed a way of getting feminized cannabis seed, working with a University of California-Davis genomics and breeding consultant team to develop a proprietary method of breeding feminized seeds (1). The company is doing full sequencing on a number of different cannabis varieties. “And right now, we are doing marker assisted breeding with auto flowers,” Pennington said. “It’s helpful to be able to quickly determine whether a plant inherited a certain trait or not before having to grow it full term to flower. So marker assisted breeding, genome annotation are all the important pieces to our puzzle right now.”

Humboldt and other seed companies are getting an assist with more discoveries on the horizon, such as the results coming from ongoing projects at the Agricultural Genomics Foundation (2), working in collaboration with the Cannabis Genomic Research Initiative (CGRI) to map the cannabis genome and the genetic details of the plant.

One of the CGRI projects (3) is an ultra-high density genetic map of cannabis. “The full genome sequencing is becoming more and more accessible to even companies like ours that are not highly capitalized,” said Pennington.

What is reportedly (4) the first time a commercial company has performed genome editing on a cannabis plant was announced by Israeli startup CanBreed, working in a 12,000 square foot facility for seeds testing, and a 45,000 square foot farm for trialing and seed production (5).

The seed gene editing was done using CRISPR-Cas9 gene editing technology (6), designed to make the plant resistant to mildew. CRISPR/Cas9 edits genes by precisely cutting DNA and then letting natural DNA repair processes take over.

The company aims to create and then sell cannabis seeds (called the F1 hybrid [7]) that have a powdery mildew resistance trait, and hopefully use the same technology to edit additional genes to enhance other traits in the plant.

Ido Margalit is an agronomist, the former CEO of medical devices startup Syngenta, and the co-founder of CanBreed. “What you have in the cannabis industry is a lack of standardization and uniformity,” he said. “The cannabis plant is grown from a clone. The genetics have not been stabilized. If you do not stabilize the genetics of a plant, you cannot reproduce it from seed. Because that means that each and every seed from a plant is genetically different. In order to get a stable plant, a stable uniform seed, there is a process that takes 3 to 4 years. And nobody has applied that knowhow, that is very common in the seed industry, to the cannabis seed,” he said.

Tomatoes and other crops have developed resistance to the same sort of mildew that is affecting cannabis, he said. “What we have done is we looked at the genes that these mildew resistance plants have and asked if cannabis has the same gene. The answer is yes. Cannabis is just another agriculture crop. No different from most of the other crops. A plant is a plant.”

CanBreed also has a subsidiary US company that operates a 140,000 square foot hemp seeds production farm.

Their gene editing work in cannabis includes the hemp seed in a process to help hemp farmers keep a legal level of tetrahydrocannabinol (THC) in their hemp plants. THC levels can become “hot” as a result of bad genetics, according to one study (8). “We have identified and edited out the gene that controls the amount of THC,” Margalit said. “And we are going to bring that to the US by the end of 2022. It’s the first stable uniform hemp seed that has zero genetic ability to express THC.”

Margalit also makes a distinction between the gene seed editing he does and genetically modified organisms (GMO). Although GMOs are in a lot of the foods humans eat, most of the GMO crops grown in the United States are used for animal food. But there are other uses. For example, genetic engineering, which is the process used to create GMOs, was first used to make human insulin, a medicine used to treat diabetes (9).

“GMO is the expression of a foreign gene,” Margalit said. “We do not introduce any foreign material into the plant in seed gene editing. We alter a certain gene that is in the plant. We know that if you take out that gene, the gene stops to function as it should. Once you alter the function, you get the resistance (to mildew).”

Can gene editing catch on as a means of getting an identical cannabis plant every time? Pennington has his take on that: “It takes so much to alter the specific cultivar or strain to inherit, time after time after time, this powdery mildew resistance,” he said. “The market is so fickle right now that I’m not sure that something being resistant to powdery mildew is the number one selling point for cannabis.”

David Kessler, the chief science officer at Agrify, a cannabis grow solution provider, said that there are “dozens and dozens of people trying to do this”—stabilize the seed gene—because it’s “a very big deal.”

“When I go to Home Depot and I buy a pack of tomato seeds, 99 out of 100 of those tomatoes are going to grow the same size at the same rate, and produce a pretty homogenized tomato. It’s going to be what we call stabilized. Where we are with cannabis is the seed is all over the place. It’s representative of a very diverse genome. So if I grow 10 seeds, I’m very likely to have 10 different phenotypic and chemotypic expressions, even though technically they came from the same mother.”

More tools and devices are being brought to bear in the indoor cannabis grow operations as other sources of new technology are being investigated. One such device is the Blue Zone, from Agrify. It’s a military style device to reduce odors and kill mold and bacteria.

Blue Zone works by circulating air through a reaction chamber designed to destroy pathogens. Chemical compounds that create odors are oxidized, while mildew spores and botrytis (a common outdoor fungus) are drawn through a self-contained ultraviolet light which kills them instantly (10). “The technology itself was developed in early 2000s for the US military, and it is currently installed on all US aircraft carriers,” Kessler said. “What’s interesting, though, is the original application wasn’t for human biosecurity or safety. It was because the product was able to destroy ethylene, which is a gas created through the maturation and ripening of produce. So using this, they were able to extend the shelf life of fruits and vegetables for soldiers quite a bit.”

Cultivators are looking more closely at the biosecurity of their cultivation, Kessler said. “Cannabis is one of the highest value agricultural commodity crops on a per weight basis,” he said. “So of course, you want to do everything in your power to prevent problems or address them if you have them.”

Each individual Blue Zone is plug and play, he said, meaning that a cultivator operator can literally put it on a table, plug it in, switch it on, and it will control 15,000 cubic feet of airspace for pathogens. “It’s also going to make the air cleaner and healthier for your employees. And I think that that’s not to be undervalued,” Kessler said. “I think that if you can take care of your team, they’ll take good care of your facility, and then everyone’s winning together.”

Another new technology being brought to bear in cannabis cultivation is the quantum dot, which is a nanometer-sized semiconductor particle. Quantum dots are widely used for their unique optical properties, as they emit light of specific wavelengths if energy is applied to them.

These wavelengths of light can be accurately tuned by changing various properties of the particle, including shape, material composition, and size (11).

UbiQD, an advanced materials company, created their UbiGro product that uses luminescent films containing their quantum dot nanotechnology to essentially tweak sunlight without using electricity. Using UbiGro, sunlight can be manipulated to enhance photosynthetic efficiency and boost fruit and flower production. Quantum dots in UbiGro shift a portion of ultra violet and blue light towards the red because increased red light improves chlorophyll absorption.

“We describe it as a layer of light that helps plants get more from the sun,” said Hunter McDaniel, founder and CEO of UbiQD (12). “It’s based on the idea that has really been maturing over the years around the quality of light impacting crop health and productivity, which is equally important as the quantity of light. Growers have largely focused on quantity, especially in the cannabis industry.”

Outside of photosynthetically active radiation (PAR) (13), photons are generally thought to be useless for photosynthesis, he explained. “That was the old way of thinking. And then within power, 400 to 700 nanometers, light is actually PAR. But not all photons are kind of equal for photosynthesis,” he said.

Dr. Keith McCree, a researcher at Texas A&M, pioneered work on lighting in 1970 with the McCree curve (14), which has been used to provide a means of establishing the optimal wavelength selections and spectral ratios for different crops. It’s a plot of 22 grain, oilseed and vegetable crops (including cantaloupe, clover, cotton, lettuce, tomato, squash, cucumber, and soybean) where he examined photosynthetic efficiency versus color.

The aim of the measurements was to “provide a factual basis for a standard definition of photosynthetically active radiation.”

Red light is about 30%, or maybe even 50% more efficient than green light, and about 20–30% more efficient than blue light, just on a photon to photon comparison, McDaniel stated. “So if only you could just give plants more red light, they should be more productive,” he added.

In addition to changing the color, UbiGro also gives more diffuse light to the plant. “So by diffusing light, you can get more light to the lower canopy, more full plant light,” said McDaniel.

Plants have evolved to sense the season partially by color because the sun sits lower in the sky as the season goes on. “You get more red in the spectrum. So this is well known in the cannabis community that more red light can help trigger flowering,” McDaniel said. “But in the earlier stage of growth, if you give plants more blue light, it triggers them into thinking ‘Hey, it’s summertime. I need to grow big and tall.’ Then with red light it thinks ‘I should start to worry about winter. Winter’s coming. I need to start reproducing myself.’ The fruit and the buds are all about the plant’s way of surviving the winter,” McDaniel said.

With UbiGro, a grower effectively tricks the plant into thinking that it’s time to do whatever the grower wants it to do. “We tend to focus more on the productivity of the plant, which means flower, fruit, buds, and that can be triggered by more red light.”

“Cannabis is a complicated crop to grow well,” McDaniel said. “The industry is a difficult one to approach because there’s so much regulatory uncertainty and variation between the states, and sort of a boom and bust cycle. It’s a very capital intensive plant, labor intensive, energy intensive. So the profitability isn’t as great as you might expect. You see that it’s 1000 times more valuable per pound than tomatoes, so it should be 1000 times more lucrative right? Well, now it actually costs a lot more to produce it too.”

McDaniel thinks that cannabis can be a beachhead market—a first market—for new technology. He explained that the cannabis industry is also very open to technology. 

I think it’s just kind of in their DNA, to be trying new things and be a little bit more of like a maverick,” he said. 

“Food and cannabis are meeting in the greenhouse. The cannabis industry is coming out of out of the dark, out of the warehouse, and into the greenhouse. The food industry is moving out of the field into the greenhouse,” McDaniel added. 

And then they’re becoming kind of collaborative in the greenhouse, so the greenhouse industry is exploding right now because you’ve got it coming from both sides. We play into that.”

The ongoing problem with cannabis that seed geneticists have to understand is that it has been cultivated by humans for thousands of years. It’s hard to decipher the origins of the plant and the true genetics of the seed. “Fortunately there are still wild populations of cannabis that are self-replicating or self-seeding every year,” said Pennington.

He went on a pilgrimage to Jamaica to find the varietals grown there, such as strains like Lamb’s Bread (also known as Lamb’s Breath with an original inbred line dating back to King Solomon [15]). “Unfortunately what we found was that they were almost gone from the whole island. We would go to these common places where cannabis is cultivated and ask if they had any of those seeds for those old Jamaican strains. And they would say ‘I don’t know about that. But OG Kush seeds, we got them. We got Sour Diesel seeds,’” said Pennington.

He said that he is looking for someone out there who would like to apply to work on a grant proposal in California with Humboldt Seed Company to start a nonprofit and help them start a cryogenic cannabis seed storage facility. “What we are after is a way to predict—with say, 10,000 seeds—exactly what they’re going to look like, smell like, and how big they’re going to get when they flower,” Pennington said.

Kessler said that, first and foremost, companies working on the leading technology for the industry have to appreciate that cannabis is 100 years behind every other modern agronomic crop. “It has not been legal, therefore universities didn’t work with it. The information that was held by legacy cultivators was really closely guarded and non-scientific. And at the same time, it comes from a group of people where there is data collection evidence. And so therefore, there was a lot of non-institutionalized knowledge,” he said. “It was spread out, it was circumstantial, it was not following the scientific process. Now, that does not devalue the information, it does not make it wrong. It just makes it not sustainable or substantiable.”

According to McDaniel, one lesson he has learned about the industry is how much variety there is. “It’s a big industry. So you know, you’d expect there to be some variety. But just walking into one greenhouse after another, there are so many different growing techniques and structures and different kinds of materials, different types of roofing. No two greenhouses are the same. But it also means that we really have to be collaborative with the grower, get down there, get our hands dirty, and be with them in the ground, if you will, in the greenhouse. Then spend a lot of time with them, which is great.”

https://humboldtseedcompany.com/feminized-seed-information/

https://agriculturalgenomics.org/research/

https://www.timesofisrael.com/israeli-startup-says-it-has-edited-cannabis-plant-gene-for-fungus-resistance/

http://www.can-breed.com/page3.html#content4-c

http://www.crisprtx.com/gene-editing/crispr-cas9

https://onlinelibrary.wiley.com/doi/10.1111/gcbb.12667

https://www.fda.gov/food/agricultural-biotechnology/gmo-crops-animal-food-and-beyond

https://www.agrify.com/bluezone-cannabis-air-scrubber

https://avantama.com/what-is-a-quantum-dot/

https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/photosynthetically-active-radiation

https://www.vegenaut.com/pl/.wp-content/uploads/sites/2/2017/07/PPFD_essential_article.pdf

https://oldschoolba.com/product/lambsbread/

 has written for many cannabis publications, and organized or moderated sessions at national and international cannabis trade shows. He was voted the 2018 Journalist of the Year by Americans for Safe Access, the world’s largest medical cannabis advocacy organization.

Tech Innovations

Cultivation Breakthroughs  for a More Uniform Plant Are Coming

The legal cannabis market has created a narrative around the color of cannabis extract. Historically, correction to extract color has occurred in processes after extraction. However, extraction technology has evolved and begun to incorporate color remediation methods in-line with extraction. In this article, we explore the evolution and rise of color remediation.

Plant pigments function as a mechanism for photosynthesis. Chlorophyll and other plant pigments are harnessed to absorb as much light as possible. For some production facilities, the goal of cannabis extraction is to extract the cannabinoids, terpenoids, flavonoids, and other desirable compounds. In other production facilities, the goal of extraction is to concentrate and isolate cannabinoids. Plant pigments can co-extract in the process and produce a dark extract with an undesirable flavor. The legal cannabis market has created a narrative around the color of cannabis extract. Historically, correcting the color of cannabis extract has occurred in processes after extraction. However, extraction technology has evolved and begun to incorporate color remediation methods in-line with extraction.

Cannabis extract can have a wide range of colors depending on the extraction method, extraction parameters, age of input material, and moisture content of input. When ingesting cannabis extract, color is not as important as inhaling extract. Whole plant extracts, also known as full extract cannabis oil (FECO), often is the most broad spectrum extract on the market. Many medical patients find ingesting FECO to be one of the most medically beneficial cannabis products for treating ailments. It is challenging to truly understand the reason behind its efficacy, but anecdotal evidence points to the wide array of compounds that are found in FECO. While it is effective when ingested, FECO is a very dark extract due to the extraction process and should not be vaporized and inhaled as heat will burn some of the compounds. This will result in harmful combustion, a bad taste, and lung irritation.

 extraction produces a beautiful extract with a range of yellow, amber, or orange color. CO

 is an extremely versatile, efficient, and selective extraction solvent and produces a full spectrum extract. CO

 can be used as a liquid or supercritical fluid to extract cannabis oil, but supercritical fluid parameters are most efficient when extracting cannabinoids, terpenoids, and other desirable cannabis compounds. At higher pressures, CO

 will extract tetrahydrocannabinol (THC), tetrahydrocannabinolic acid (THCA), and other cannabinoids more efficiently. However, higher pressures can also extract a higher concentration of plant pigments resulting in a darker extract. CO

 extraction is most commonly used for production of inhalable extracts, like vape cartridges or concentrates, and full spectrum edibles. As mentioned, CO

 extracted cannabis oils can have a wide range of colors from yellow to amber-orange. Most commonly, these colors are a result of the input material genetics or grow-method. Sun-grown cannabis plants harvested later and cured are more likely to produce an orange or amber colored extract. As the trichomes are exposed to sunlight in the later stages of growth, they shift from a clear resin to an amber color.

 and ethanol extraction is done using cured cannabis biomass. However, hydrocarbon extraction of freshly harvested and flash frozen plant material has quickly caught the attention of consumers. The terpene and flavonoid profiles continue to change after harvest and during the cure process. Flash freezing the freshly harvested plant and extracting the fresh plant has arrested the terpene and flavonoid profile, introducing a completely different flavor profile to the industry. In addition, the color of the extract is much lighter due to the plants reduced exposure to light.

As cannabis legalization has expanded throughout the country, the regulations around cannabis sales continue to develop and change depending on the market. In some states, consumers are able to smell the product before purchasing it. In other states, packaging must completely conceal the product. Color has become one method for consumers to understand the quality of the cannabis extract that they are looking to purchase. However, the lighter or clearer concentrates often indicate that some beneficial and integral compounds are being left behind in the extraction process. Despite this understanding, extraction companies are introducing technology, like in-line color remediation, in order to lighten the color of the extract.

The most common in-line color remediation techniques are columns of filters where extract is passed through during the extraction process. In the filter, the pigments are caught in the filter media as the rest of the extract solution passes through producing a much lighter colored extract. Common filter media are activated carbon, magnesium silicate, and bentonite clay. After the extraction column, the extract-solvent solution moves into the color remediation column (CRC) where the pigments are filtered. Next, the filtered extract-solvent solution moves to the separation or collection column for its final stages. Multiple CO

, ethanol, and hydrocarbon extraction equipment manufacturers offer modular CRC units to be added in-line with installed extraction equipment. While these modular units are growing in popularity, the question remains—are any toxins introduced to the extract as a result of these CRC columns that could cause harm when inhaled on a regular basis?

 is the founder, CEO, and Chief Extractor of Heylo. With a background in chemistry and clinical research, Lo was inspired to explore cannabis as a medicine and to enter the emerging industry. She joined Eden Labs, a leading CO

 extraction equipment manufacturer to support and expand a Research and Development department. There she managed the development of their latest and greatest CO

 extraction system. In 2017, after working with Eden Labs and another cannabis processor, Lo launched Heylo with a mission to help people get more out of life with cannabis.

Extraction Science

Here, we explore the evolution and rise of color remediation methods in-line with extraction.

The Rise of In-line Color Remediation

Over the last few columns, we have examined the fundamentals of chromatography, liquid chromatography, methods development, and liquid chromatography–mass spectrometry (LC–MS). In this column, we dive into the field of optical spectroscopy and spectrophotometry and their use in the analytical laboratory. Many laboratories routinely use spectrophotometry as stand-alone instrumentation or coupled with a liquid chromatography system such as a diode array detector (DAD) or UV-vis detector. This column looks the overall scope

of spectroscopy and breaks down the types of optical spectroscopy including spectrophotometry as a tool for the analyst to employ in improving their analyses.

Analytical organic laboratories are often acutely focused on their chromatography, mass spectrometry, and hyphenated techniques. Chromatography is a powerful separation tool under the larger umbrella of the overall fields of spectrometry and spectroscopy. The highest level, especially for analytical scientists, is 

 which is the measurement of the interaction of matter with energy (in particular electromagnetic radiation). 

Under spectrometry is the important subdivision of spectroscopy, which contains the bulk of what modern scientists consider analytical instrumentation techniques. 

 is the study or measurement of the interaction of matter and electromagnet radiation resulting in spectra (wavelength or frequency of the radiation). Spectroscopy is also described as the study of color from all bands of the electromagnetic (EM) radiation spectrum and is the basis of many of the most common types of laboratory analyses from atomic absorption to X-ray fluorescence (Figure 1). 

Electromagnetic radiation are all the waves in the electromagnetic field carrying electromagnetic radiation throughout space. Electromagnetic radiation is made up of oscillating waves of magnetic and electrical fields measured by frequency and wavelength. Waves show regular repetitive changes in value where points in the wave are either in-phase (oscillate in unison) or out-of-phase (oscillating at different points not in unison). Wavelength is the distance measured between the nearest two points in phase with each other. These two adjacent peaks (or troughs) are said to be separated by a single wavelength (λ) while the distance from peak to trough is the
amplitude (Figure 2).

The number of peaks in a unit of time or space is called its 

. When peaks, such as the wave discussed, are measured in time it is referred to as temporal frequency measured in hertz (Hz) (one event per second) and is the reciprocal of period (the duration of time for one cycle of an event such as the occurrence of peak and trough).

The EM radiation spectrum encompasses a wide band of energy including energy waves such as radio waves, microwaves, infrared, visible light, ultraviolet, X-rays, and gamma rays
(Figure 3). Radio waves at the end of the spectrum have the largest wavelength and the lowest frequency. The size of these radio waves is in the hundreds of meters comparable with the size of buildings. This wavelength size does not travel particularly long distances or through many obstacles. Just think of the range of AM radio stations in a city with tall buildings versus the range of FM or TV signals in the same area. On the other end of the range are gamma rays which are high frequency and so small they cannot be measured since the particles slip between the molecules of measurement devices. For this reason, these very small waves from ultraviolet to gamma rays are a risk to cells, tissues, DNA, and other molecules (Figure 3).

Many laboratory analytical techniques are focused on range of waves associated with light from infrared to ultraviolet light in the range of 100 nm to 1 mm. This energy encompasses the ultraviolet ranges, visible light, and the infrared spectrum. Light outside of visible range for humans includes infrared light in the range upwards of 700 nm. Infrared (IR) light is divided into infrared-A (700–1400 nm), infrared-B (1400–3000 nm), and infrared-C (3000 nm to 1 mm). These wavelengths of light have a longer spatial period for a periodic wave than visible light. IR radiation is used in a wide variety of applications including scientific and industrial equipment, law enforcement, and medical devices. One of the common uses for IR is in the application of contactless thermometers during the COVID-19 pandemic. 

On the other side of visible light is ultra-violet radiation, which is also classified into three groupings: ultraviolet-A (UVA) from 315–400 nm; ultraviolet-B (UVB) from 280–315 nm; and ultraviolet-C (UVC) from 100–280 nm. UVA was frequently used in artificial tanning before it was discovered to cause formation of free radicals and reactive oxygen. UVB is absorbed to a large extent (along with UVC) by the Earth’s atmosphere. UVB and UVC cause the reaction leading to the production of ozone. UVB is a double-edged sword, it is needed for the production of ozone and the production of vitamin D in the body, but it is also the leading cause for sunburn and DNA damage leading to cancer. UVB is very photoreactive and can cause unwanted DNA changes in the photoreactive cells of the epidermis such as melanocytes and basal cells. UVA has the longest wavelength and penetrates beyond the skin’s hypodermis into the deeper dermis and can also change the DNA of skin cells, but not to the same degree as UVB radiation.

Most animals including human beings can only perceive light within the range of 400–700 nanometers, which also gives us the perception of colors when they are absorbed by objects and reflect back as the opposite color. We see the color red when light of many spectrums hits something like an apple and all of the other wavelengths of visible light are absorbed while the reds are reflected back for the human eye to see.

There are numerous interactions between EM and matter including absorption, transmission or refraction, reflection, and emission. 

 occurs when matter transforms EM energy into internal energy (such as thermal energy) through an absorber. 

 is a function of the amount, wavelength, and angle of which energy (or light) pass through matter. 

 is the measurement of the amount of energy reflected matter and 

 is the change in energy state as the incident energy passes through matter. All of these processes become the basis for some form of detection or spectroscopic measurement technique (Figure 4).

Other types of spectroscopy interactions include elastic scattering (similar to reflection) where the incident beam is scattered within the target material rather than just reflected. Inelastic scattering also involves the measurement of scattering but is part in a change in wavelength. Impedance is the slowing of the transmitting of energy. Resonance spectroscopy is characterized by radiant energy being a radiating field between quantum states of the material. Finally, there is nuclear spectroscopy that utilizes the nuclei to determine the properties of matter. 

Spectroscopy techniques are divided into atomic and molecular spectroscopy depending on the target to be measured and the material being tested (Table I). 

 studies the energy and matter interactions between atoms. Most of the atomic spectroscopy techniques are applied to the study of elemental composition. The instruments under atomic spectroscopy include atomic absorption (AA), X-ray fluorescence (XRF), and inductively coupled spectroscopy (ICP). 

 studies the interaction of energy and matter between molecules and is most often found in techniques measuring organic molecules and includes instruments such as Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), UV, and visible light spectroscopy. 

Spectrometry instruments are often further divided and defined by the type of interactions they produce and the type of energy that is measured. For example, X-ray fluorescence (XRF) measures X-rays and the amount of fluorescent energy a material emits during exposure. Some techniques focus on a narrow band of interactions (such as emission only) or a small part the EM spectrum (like X-rays) while other techniques monitor an array of interactions and wavelengths on the spectrum. 

In addition to all these spectroscopy techniques that have been discussed, there are spectroscopy detectors that are coupled with other chromatography or mass spectrometry techniques into hybrid or hyphenated analytical systems. In some cases chromatography systems such as capillary electrophoresis (CE), gas chromatographs (GC), or liquid chromatographs (LC) are coupled to mass spectroscopy (MS), flame ionization detectors (FID), UV-vis spectrophotometers (UV-vis), refractive index detectors (RID), and evaporative light scattering detectors (ELSD) which each in their own way measure the interactions of energy and matter to produce a result. 

Techniques utilizing the interaction of the energy bands included in the wavelengths of “light” are frequently referred to as spectrophotometers. The basic pathway for a spectrophotometer contains an energy or light source which is then sometimes split or isolated by a filter or monochromator before encountering a sample. The energy interacts with the sample and the energy that is transmitted, absorbed, emitted, or reflected is measured by a detector (Figure 5).

Spectrophotometers are a common, easy-to-use spectrometer found in many organic analytical chemistry laboratories. They often work on the simple principal of comparing the absorbance or transmission of light through a liquid sample compared to a blank or a standard material. The difference or change from the blank or standard material is used to calculate concentration or a number of other results. 

The most widely used form of spectrophotometers are UV or UV-vis spectrometers operating in the UV and visible wavelengths of light from 180–800 nm. There are several options and configurations for spectrophotometers including number of pathways (that is, beams) detectable and the types of modes of detection performed.

Spectrophotometers are characterized as either single beam, containing only one pathway from source to detector and therefore only have one sample pathway similar to Figure 5, or a double beam (split) system splits the energy source prior to interacting with the sample and diverts to a second sample cuvette which most often contains a blank, reference standard or sample (Figure 6).

There are two basic types of a double-beam configuration. Alternating in-time configurations have a series of converging mirrors that bring the beam back to one detector. The beam is directed at alternating times between the two cuvettes and the detection occurs alternately as well. Simultaneous in-time configurations split the beam between the two cuvettes but do not direct the resulting beams to a single detector and instead have multiple detectors that can operate simultaneously during scans. 

Some spectrophotometers have preprogrammed settings and allow for only the monitoring of selected or a single wavelength (fixed wavelength). This configuration usually employs some filter or slit which can be programed to allow only single wavelengths to be exposed to the sample. In contrast, scanning and array spectrophotometers allow for an entire range, multiple sets of wavelengths, or scanning subsequent wavelengths along an entire range of wavelengths. A scanning spectrometer uses a tunable monochromator to isolate individual wavelengths of light from the incident beam before interacting with the sample. An array spectrophotometer allows for the simultaneous scanning of a range and has no filter or monochromator; however, there is often a focusing slit to confine the incidence beam. This configuration allows the entire focused incident beam to interact with the sample and the data from individual wavelengths occur at the detector (known as reverse optics) (Figure 7).

The incident energy or light is provided by several different types of lamps including halogen, xenon, deuterium, and light-emitting diodes (LED). Halogen lamps (also known as tungsten or quartz lamps) are similar to incandescent light bulbs and cover the wavelengths of visible light from about 320–1100 nm. In this lamp, the filament heats up and emits light when a current flows through it. The bulb is filled with inert gas to prevent evaporation of the tungsten filament. A halide is also contained in the lamp to create the halogen cycle to return evaporated tungsten to the filament and reduce blackening of the bulb. Instruments with only halogen lamps can only detect wavelengths of the visible light. Generally, halogen lamps last about 2000 hours and are comparatively low cost.

Deuterium lamps (D2) operate in the range of 190–370 nm and are often paired with halogen lamps to cover the UV-vis spectrum. D2 lamps operate at high temperature and require specialized quartz housings that can increases cost. A D2 lamp is a discharge light source with deuterium sealed in a bulb. A hot cathode is used to deliver a steady arc discharge. These lamps require a large power supply that adds to the cost. D2 lamps last about 1000+ hours.

Xenon lamps are discharge lamps and high energy sources just like the D2 lamp, but xenon lamps cover the entire UV-vis range and more from 185–2000 nm and eliminate the need for two types of lamps. Xenon gas is sealed in the bulb with a tungsten electrode. The lamp operates at 80 Hz, which can prolong their life but comes with increased costs higher than halogen or deuterium lamps. LED lamps produce single wavelengths of light with only small variations in bandwidth. The positive side means no monochromator is needed for some spectrophotometers.

Each type of lamp has its own benefits and drawbacks including range, cost, and lamp life. Some technological or programming advances for spectrophotometers can increase lamp life by limiting lamp emissions to set time periods. One feature called press-to-read (PTR) ensures the lamp is only ‘on’ when needed to increase lamp life.

Spectrophotometry Measurement Modes and Calculations

In all the configurations of spectrophotometers there are often multiple modes in which a system can be operated. Many multi-mode instruments can operate in standard fixed wavelength or multiwavelength modes and some in scanning modes as well. The data can then be used for different types of calculation such as for transmittance, absorbance, concentration, and kinetics.

Transmittance (T) is the calculation of the different of intensity (I0) energy from the incident beam to the resulting beam (I) (Figure 8). Transmittance is usually expressed in units of percent.

The next type of measurement mode is the negative log of transmittance called absorbance (Equation 1).

When a sample in solution is placed in a transparent cuvette the light intensity is found to be proportional to the sample concentration and the length of the pathway of the cuvette (Figure 9). This is called Beers Lambert Law.

A standard type of result is obtained by calculating concentration with either a factor or a standard curve. In this type of calculation, the concentration of a sample is determined by multiplying an absorbance (Abs) value by a specific factor. This type of calculation uses Beers Lambert Law where the concentration is proportional to the absorbance (Equation 2).

The extinction coefficient (ε) is the characteristic of a substance that tells how much light is absorbed at a particular wavelength per concentration, molarity, or pathlength. The extinction coefficient is sample specific and describes how much the sample can absorb in L/(cm*mol) or mL/(cm*g). If the factor is now known, then the concentration of a sample can be determined by standard curve where known concentrations standards are measured, and a standard curve is created.

Some other modes of data include kinetic measurements where the absorbance of a sample is measured over time for changes. The change in absorbance is plotted giving rise to details regarding reaction rates. In the case of these types of measurements, internal standards can also be added to compensate for any losses or variations and correct for bias. 

There are a number of other specifications—such as accuracy, resolution, and bandwidth—that should be considered in any spectrometers. In chromatography systems these terms refer to the size and retention time of peaks, but in spectrometers it refers to the resolution and accuracy of the measurement around each wavelength being measured.

Sometimes, a spectrophotometric system’s function is enhanced by the accessories that aid measurement including high-throughput sampling systems like multicuvette holders and changers, and sippers (pumping systems allowing for either very accurate or continuous sample introduction), or temperature-controlled accessories for temperature sensitive samples. 

Most basic spectroscopy systems are equipped to perform routine analysis and experiments. Higher end instruments can provide more functionality but increase cost and the learning curve for operation. The selection of a spectrophotometry system ultimately is a tradeoff of functionality, target, resolution, and price.

The selection of spectroscopy instrumentation is dictated by the type of samples, target elements or analytes, and analytical detection level needed. In some cases, the ability to collect or recover the sample after analysis may play a role in the selection of a technique. Many mass spectroscopy methods and instruments which change or consume the sample are known as destructive methods. Methods that can sample in situ, or where sample is not changed or consumed by the process are non-destructive and may be used to collect analytical fractions or recover valuable materials. 

Organic samples or targets are best analyzed by molecular spectroscopy techniques whereas inorganic samples or targets are the focus of atomic spectroscopy. Samples with color or chromophores will interact with light energy waves while other analyte functionalities will react with different wavelengths of energy and produce separate interactions that can be measured. The selection of the appropriate analytical technique depends on all these factors to achieve the most accurate results possible for your targets.

B.T.I. Corp, S. Patel, and M. Lares, Hamamatsu. "A Guide to Selecting Lamps," 

https://www.photonics.com/Articles/A_Guide_to_Selecting_Lamps/a44487

A. Hind, Agilent 101: An Introduction to Optical Spectroscopy. 7.

A. Sapkota,. "22 Types of Spectroscopy with Definition, Principle, Steps, Uses" 

https://microbenotes.com/types-of-spectroscopy/

Atomic Spectroscopy: Basic Principles and Instruments

ACTTR Inc., "What Are The Types of Lamps in An UV-Vis Spectrophotometer?" 

https://www.acttr.com/en/en-faq/en-faq-uv-vis/219-en-faq-what-kind-of-uv-vis-lamps.html

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 is a Senior Applications Scientist with Spex, an Antylia Scientific company and has been a member of many cannabis advisory committees and working groups for cannabis including NACRW, AOAC and ASTM.

Navigating the Lab

What is the overall scope of spectroscopy and how can it be used as a tool to improve analyses?

Examining Energy and Light: Understanding Spectroscopy and Spectrophotometry

Learn about the value of monitoring environmental conditions, how software platforms can analyze data and offer insights, and the ways growers can use data to optimize plant health and vitality.

Many environmental control systems such as lighting, heating, ventilation, and air conditioning (HVAC), and water are crucial for growers to achieve maximum yields in cannabis production facilities. A well-built integrated controls system with analytics software reduces the amount of time a cultivator spends managing environmental conditions and identifying issues in a facility. However, sensing equipment for a typical control system can generate tens of thousands of data points every single day. Translate data into useful information with analytics software. Search for patterns in environmental data to detect problems in your mission-critical systems and what conditions are resulting in the greatest crop production and quality. Learn how you can take action using fault detection and diagnostic software to fix issues and also identify recipes to achieve the best outcomes for plants.

As the old adage goes, you can’t manage what you don’t measure. But the truth is you can’t measure what you don’t monitor. If environmental conditions are out of desired parameters, plants may not show signs of stress until time passes and issues create ripple effects across the canopy.

Knowledge is power. A key way to achieve the best outcomes for your plants and ensure the optimal conditions for growth is to install sensing equipment throughout your grow rooms. Sensors track actual conditions in your facility at recurring intervals to give you a continuous log of field data you can analyze and correlate to productivity and yield. Approximately 55% of cannabis growers surveyed in 2020 report measuring light levels, 85% measure grow room temperature, and 72% measure grow room relative humidity (1).

With the variety of systems and large quantities of environmental points of data that can be collected, growers must plan how data will be used for monitoring, fault detection, and cultivation intelligence. If data is only used for emergencies, recipes for lighting, HVAC, and watering systems cannot be dialed in. Fortify facilities with the tools not just to avoid the worst outcomes, but to achieve the best.

Resource Innovation Institute (RII) is a non-profit organization committed to cultivating a better future for all of humanity. Our consortium of members brings perspectives from across the field—uniting architects and engineers, growers and operators, researchers and analysts. Together, we measure, verify, and celebrate the world’s most efficient agricultural ideas. In this article, RII’s Technical Director and members of the organization’s 

 (2) working groups share perspectives on ways growers can use data to achieve best outcomes for yield and quality.

“There’s an amazing opportunity right now because there is so much room for innovation. In systems, processes, genetics, grow mediums, automation, almost everywhere. There is no single best way to grow, and as systems change so do the processes. Whether you are trying to optimize for biomass, terpenes, or extraction, you have to start by baselining how your strains perform in your facility, start by collecting and aggregating historical batch data, then optimize cultivation recipes towards your goal,” shared Technical Advisory Council member Vince Harkiewicz, CEO of Grownetics.

Learn about the value of monitoring environmental conditions, how software platforms can analyze data and offer insights, and the ways growers can use data to optimize plant health and vitality.


October 2021

Statistics for Cannabis Analysis, Part I: Standard Deviation and Its Relationship to Accuracy and Precision

Cultivation Breakthroughs for a More Uniform Plant Are Coming

The Rise of In-line Color Remediation

Examining Energy and Light: Understanding Spectroscopy and Spectrophotometry

Make Data Work for You: Environmental Monitoring for Optimal Outcomes

Exploring the Impact of Cannabis on the Human Brain, Heart, and Gut

Measuring Cannabidiol Bioactivity: Neuronal Cell Survival and Neurite Outgrowth

Accelerating Cannabis Testing: How Filter Plates Increase Throughput and Productivity