Articles

Digital Edition

View our interactive November/December 2023 digital edition.

Cannabis Science and Technology November/December 2023 Interactive Digital Edition

Here, we begin a new column series on calibration science. The goal of these columns will be to teach you the foundational theory behind how to calibrate analytical instruments that are used for quantitative analysis in the cannabis analysis industry, such as potency and pesticide analyzers. We begin with an in-depth discussion of accuracy, precision, random error sources, and how to reduce them.

Early in the history of the “Cannabis Analysis” column series, I wrote an article titled, “Error, Precision, and Accuracy” (1). Here, we review the salient parts of that article and expand upon that discussion to give you the full story on these topics.

Let’s get the definitions of precision and accuracy out of the way first. Precision is the spread in the repeated measurements of the same quantity. Imagine we are going to weigh the same sample three times on two different balances. Balance 1 gives us values of 2.11, 2.12, and 2.13 grams (g), whereas balance 2 gives us values of 2.0, 2.2, and 2.4 g. We define the range in any set of numbers as the highest minus the lowest value as such in 

where R is the range, Max is the maximum value in a data set, and Min is the minimum value in a data set.

Thus, the range for the measurements from Balance 1 is (2.13 g – 2.11 g) or 0.02 g, whereas the range for the measurements from Balance 2 is (2.4 g – 2.0 g) or 0.4 g. In the case of our two balances, then Balance 1 has a precision of 0.02 g whereas Balance 2 has a precision of 0.4 g. We would then say that Balance 1 is more precise, so of course it is preferred. Imagine the dots on the targets in 

 are measurements of the mass of the same sample on a balance. For the leftmost target the data are imprecise because there is wide scatter in the data; the measurements are all over the place. Assuming the mass of our sample did not change during these measurements, the spread in the values was caused by random error. Precision then is a measure of the amount of random error in a set of measurements.

The ideas of precision and random error were driven home for me in the very first laboratory experiment I performed when I took Physics 101 as a freshman. There was a team of three of us, the professor handed us a meter stick, pointed at the table in front of us, and said, “I want each of you to measure the length of that table 10 times” for a total of 30 measurements. I thought this was the most stupid laboratory experiment ever concocted. The table wasn’t going to change length while we were measuring it, so of course all the measurements were going to produce the same number. Boy was I wrong! Not only did we end up with 30 completely different table lengths, but even my own 10 measurements had a spread to them—despite my best efforts to be reproducible. This spread, or range as we have defined it, in a set of measurements has a bunch of names but can be referred to as error, spread, dispersion, or variance: I prefer the term error.

Accuracy—note in Figure 1 that in addition to the word precision the word accuracy is used. 

 is a measure of how far a measurement is off from its true value. The true value being supplied by a standard or reference sample. For example, for balances 1.0 g weights certified by the National Institutes of Standards and Technology (NIST) (2) are available. By the way, NIST is the Federal government agency in the US that is in charge of issuing standard reference materials. Imagine then that we weigh our NIST certified 1 g standard on balance 1 and obtain a value of 1.1 g, and on Balance 2 we obtain a value of 1.01 g. Balance 2 is said to be more accurate than balance 1 because it was off from the true value by 0.01 g whereas Balance 2 was off from the true value by 0.1 g.

Based on these measurements then Balance 1 is more precise, but Balance 2 is more accurate. How can this be? Because precision and accuracy are not the same thing! In Figure 1 the data in the middle target are precise because the measurements are all tightly clustered together, however these data are inaccurate because they cluster far from the bullseye, the true value. The ideal situation is illustrated by the target to the right in Figure 1. The data are tightly clustered, hence are precise, and are centered around the bullseye, which means they are close to the true value and are therefore accurate.

As illustrated by my table measuring anecdote above, there is always error in any measurement of any quantity. Random error is caused by the fact that human beings are not omnipotent and cannot control all the variables in the universe all the time while making a measurement. Thus, random error is caused by random fluctuations in things we cannot control. Also, the sign of random error is random, that is, a positive or negative fluctuation are equally likely at any given point in time. Sources of random error are myriad and include the following:

 – particularly for optical systems such as spectrometers. It is important that all optical elements are aligned properly so that the correct part of the sample is analyzed, and a maximum amount of light hits the detector.

 – This applies to any scale with hash marks on it that must be read by eye, such as rulers and graduated cylinders. Rule of thumb—the error in measurements taken by eye is ±1/2 the space between hash marks. Thus, if a ruler has lines every 1 mm, the error in a length measured with that ruler is ±0.5 mm.

 – These days most of our analytical instruments are powered by electricity, which comes from a plug in the wall and is something we rarely think about. However, electrical power has an amperage, voltage, and frequency and all the electronics on our instruments are designed to function with known and fixed values of these quantities. If anything in what is called the 

 of the electrical power changes this can throw off the measurements made by your instrument. I never thought much about this source of error until I was awakened to it by the experiments of some very smart service engineers at PerkinElmer, a place I used to work. They ran some standards on a gas chromatograph-mass spectrometer (GC–MS) using standard wall power, measured the accuracy of the instrument, and then repeated the experiment with the instrument plugged into what is called a 

which simply smooths out fluctuations in voltage, amperage, and frequency of the electrical power coming into an instrument. The use of the line conditioner significantly improved the accuracy of the instrument. Given that line conditioners only cost a few hundred dollars and are readily available and easy to use, I highly recommend all analytical instruments use one. An investment in a line conditioner may give you the biggest bang for the buck in terms of improving accuracy.

Instrument not in calibration or in need of repair

 – The performance of all analytical instruments will degrade over time. This means their performance needs to be constantly monitored and routine maintenance needs to be performed at required intervals. It’s like driving your car, if you don’t get your oil changed at regular intervals your engine will break down. To prevent unwanted breakdowns, I highly recommend a service contract for your most important analytical instruments. With a service contract, for an annual fee the manufacturer will send someone out to inspect your instrument, perform routine maintenance, replace any worn parts, and tweak the instrument so it meets specifications. This does not guarantee that unexpected instrument breakdowns won’t happen, but it does mean they will happen less often.

 – Many samples are prepared and analyzed using solvents and other chemicals. For example, in liquid chromatography, significant amounts of solvent are used as the mobile phase. These chemicals need to be of consistent composition and free of any impurities that may interfere with the measurement of your analytes. For example, if a solvent batch has an impurity in it not normally found in the solvent that happens to elute in chromatography at the same time as an analyte peak, it will interfere with the area measurement of the analyte peak throwing off its quantitation. Now, I am not saying the chemicals used in sample analyses need to be ultra-pure, what is more important is that they are consistent chemically, so their properties are consistent and their performance in the analysis is consistent.

 – Samples and standards are not necessarily stable over time, as I have found out the hard way. Early in my career I worked for a high pressure liquid chromatography (HPLC) company, and I was tasked with troubleshooting problems with a separation. I ran the standard over and over again, with the problem still present. I troubleshot and replaced the injector, pump, column, and detector; when I ran the standard sample again the problem was still present. My laboratory supervisor finally suggested I make up a fresh standard sample and analyze it. This did the trick; the problem was solved. Turns out, the standard sample was a mixture of carbohydrates in water, and of course there were bacteria to whom my standard sample was food. So, they chomped on the molecules in my standard, destroying it. The moral of the story here is that both standards and samples can degrade and should be analyzed as soon as possible after being generated. If necessary, standards and samples should be stored under conditions to slow degradation, such as in a refrigerator or freezer, or in conditions free of oxygen or light.

 – The matrix of a sample is also what I call its chemical environment

 This is comprised of the condition and composition of the sample including temperature, pressure, humidity, pH, composition, and concentration. All these variables can affect the properties of your sample and hence the measurement of its properties. 

 shows an example of how changing chemical environment changes sample measurement. 

The mid-infrared spectrum in purple is of pure liquid water, the peak to the left of it in brown is the mid-infrared spectrum of sepharose dissolved in water. Note that even though both these peaks are caused by water, they are shifted because the chemical environments in the two samples were different. In this case, the sepharose molecules interacted with the water molecules changing their infrared spectrum. This is why when developing calibrations, it is so important to perform matrix matching, which means the chemical environment or matrix of the standard samples used to generate a calibration must match as closely as possible the matrix of the unknown samples to which the calibration will be applied.

 – Not all samples are homogeneous, with cannabis buds being the perfect example. Work conducted by myself and others (3) has shown that buds from the same plant vary measurably in potency, and even sections of the same bud can vary in composition. In these cases, non-representative samples were taken, and the source of error is that the aliquot analyzed may not be representative of the sample as a whole. This sampling error is the source of the infamous “margin of error” in opinion polls. If you ask one person who they want for president, it is of course not representative of the hundreds of millions of voters across the US. However, if you ask thousands of people who they want elected as president, the sampling error is reduced enough that conclusions can be made from the results of the poll. In analytical chemistry, the solution to the non-representative sampling problem is to homogenize samples. For liquids this is easy, they can be stirred. For solids, such as cannabis buds, this is more difficult. Solids can be ground before analysis, which is how cannabis buds should always be prepared, but even then, the resulting powder might not be homogeneous. This is where we take a clue from pollsters and analyze multiple aliquots and average the results. For example, if you have to analyze a container full of solid or powder, an excellent way to ensure a representative analysis is performed is to take 5 aliquots from the container using the sampling pattern as shown in 

; left top, right top, center, left bottom, and right bottom.

These five samples should be analyzed separately, and the results  averaged to reduce sampling error.

 – The most irreproducible thing in your laboratory is you. Despite our best efforts at implementing clear standard operating procedures and extensive training, different people do things differently and people can always make mistakes. These include carelessness, not following directions, not paying attention, poor sample handling, undue exposure of the sample to heat, light, and air, sample spillage, and so on. The list of mistakes any of us can make is pretty extensive, which is why all methods should be tested in the hands of multiple people to make sure reproducible results are obtained even if different people perform a method.

We reviewed the concepts of accuracy and precision. Precision is the spread of values of a measurement made multiple times. Accuracy is how far away we are from the true value. Measured values vary because of random error. We then discussed the many experimental sources of random error with an eye towards correcting these problems.

Smith, B., Error, Accuracy, and Precision, 

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

, PhD, 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: 

Smith, B., Calibration Science, Part I: Precision, Accuracy, and Random Error, 

Cannabis Analysis 

Here, we begin a new column series on calibration science. The goal of these columns will be to teach you the foundational theory behind how to calibrate analytical instruments that are used for quantitative analysis in the cannabis analysis industry, such as potency and pesticide analyzers.


Calibration Science, Part I:  Precision, Accuracy, and Random Error

The lack of federal oversight with regard to heavy metals in cannabis and hemp consumer products in the US has meant that it has been left to the individual states to regulate its use. Medical marijuana is legal in 38 states, while 22 states and the District of Columbia allow its use for adult recreational consumption (1). However, the cannabis plant is known to be a hyper-accumulator of heavy metals in the soil, so it is critical to monitor levels of elemental contaminants to ensure cannabis products are safe to use. Unfortunately, there are many inconsistencies with heavy metal limits in different states where cannabis is legal. Some states define four heavy metals while others specify up to eight. Some are based on limits directly in the cannabis, while others are based on consumption per day. Others take into consideration the body weight of the consumer, while some states do not even have heavy metal limits. Some states only require measuring heavy metals in the cannabis plant or flower, while others give different limits for the delivery method such as oral, inhalation, or topical (2). In other words, it’s a fractured and dysfunctional regulatory framework, which desperately needs changing.

Clearly, there is a need for more consistency across state lines, particularly as the industry inevitably moves in the direction of federal oversight. The cannabis industry can learn a great deal from the pharmaceutical industry, as it went through this process almost 25 years ago when it updated its 100-year-old semiquantitative sulfide colorimetric test for a small group of heavy metals to eventually arrive at a list of 24 elemental impurities in drug products using either inductively coupled plasma-optical emission spectroscopy (ICP-OES) or inductively coupled plasma-mass spectrometry (ICP-MS) spectrochemical techniques. This new initiative also included maximum permitted daily exposure (PDE) limits, using elemental toxicological data for the different drug delivery methods (oral, parenteral, inhalation, or transdermal), based on well-established animal studies (3).

These PDE limits were defined under two different directives: United States Pharmacopeia (USP) Chapter <232> (4)and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Q3D guidelines for elemental impurities (5). All 24 elemental impurities with their toxicological classification are listed in 

The ICH, known by its full name of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use, is an international consortium of pharmaceutical regulatory authorities around the world with a focus on scientific and technical aspects of pharmaceuticals development.

In addition, the analytical methodology to measure these elemental impurities was outlined in USP Chapter <233>, which described the analytical procedures, including the appropriate plasma spectrochemical technique (ICP-MS, ICP-OES, or equivalent), the sample digestion method, and a comprehensive set of validation protocols using well-established spike recovery procedures. These new directives meant that pharmaceutical manufacturers were required to not only understand the many potential sources of heavy metals in raw materials and active ingredients, but also to know how the manufacturing process contributed to the elemental impurities in the final drug products by implementing a risk assessment study. They did this by categorizing all 24 elemental impurities under four classes—1, 2A, 2B, and 3—which determine the toxicology priority of the elements and the likelihood of occurrence in the drug product. The different toxicity classes are described in 

These PDE limits became the basis for cannabis state regulators to set limits for heavy metals in their jurisdictions. However, for many different reasons, the majority of states did not require all 24 elemental impurities, mainly because they felt they were not representative of elemental contaminants found in the cannabis manufacturing process. There were many reasons for this, but the main justification was that cannabis consumer products were considered to be more similar to herbal medicines than pharmaceutical formulations. And as a result, they should be regulated as herbal products or dietary supplements, which historically had only required the big four heavy metals (Pb, Cd, As, and Hg) to meet compliance (6). Some states, such as Maryland, added Cr to the big four, while other states, such as Michigan, added Cr, Cu, and Ni, and New York also included Cu, Ni, Sb, Cr, and Zn. However, since these initial assessments, there has been a significant amount of compelling evidence in the public domain to suggest a wider panel of up to 20 elements might now be worthy of consideration (1).

There is still much debate of what toxic metals might be included in a future federally regulated panel for cannabis and hemp, but there is no question about what analytical methodology should be used to monitor that panel. It is well-accepted that ICP-MS is the optimum technique to use for measurement purposes. The very low limits of detection and high sample throughput with ICP-MS make it ideally suited for cannabis testing laboratories. However, the technique is not without its drawbacks. It requires a skilled analytical chemist to fully understand the method development process including isotopic selection, interference removal, matrix suppression effects, internal standard selection, and sample preparation contamination issues. In other words, it requires someone who is experienced in working in the ultra-trace element environment to ensure the highest data integrity (7).

This is why when USP developed Chapter <232> PDE elemental impurities they also accompanied it with Chapter <233>, a robust and well-proven analytical method to ensure high quality data (8). However, this seemed to be an area that escaped the scrutiny of most state regulators of cannabis and hemp consumer products. They were very strict about the actual limits, based on the method of administration (oral, inhalation, or topical), but they typically didn’t propose methodology to carry out the analysis. They left it to the discretion of the cannabis testing laboratories to develop their own analytical method including quality control and quality assurance procedures. Some of the laboratories with more experienced ICP-MS analysts who were familiar with trace element analysis were implementing these safeguards and generating high quality results, but many of the other laboratories did not put an emphasis on this type of expertise and as a result were often producing questionable results, particularly for elements that are notorious environmental contaminants or are prone to loss during the sample preparation stage (9).

So, let’s take a closer look at USP Chapter <233> to better understand why it’s so important to follow this methodology to ensure data integrity and offer suggestions as to how it could be adapted for use by cannabis testing laboratories.

This chapter deals with the sample preparation, analytical procedure or instrumental technique, and validation protocols for measuring elemental impurities using one of two plasma based spectrochemical techniques: ICP-OES or ICP-MS, or any other alternative technique as long as it meets the data quality objectives of the method defined in the validation protocol section (9).

: It was originally developed for drug products and dietary supplements but has been somewhat modified by taking shortcuts for use with cannabis-related samples. So, let’s take a closer look at each of these areas.

The selection of the appropriate sample preparation procedure will be dependent on the material being analyzed and is the responsibility of the analyst. For example, it suggests that if the sample is in a liquid form, it could be analyzed neat if it is compatible with the measurement technique or could be diluted with water and acidified if the sample is hydrophilic in nature or diluted with an organic solvent if the sample is hydrophobic. However, it recommends that the most suitable technique for solid materials is closed vessel dissolution of which microwave digestion is the most common. It emphasizes that the actual procedure should be at the discretion of the analyst but gives a typical generic method which is described here.

 The benefit of closed vessel digestion is that it minimizes the loss of volatile impurities. The choice of what concentrated mineral acid to use depends on the sample matrix and its impact of any potential interferences on the analytical technique being used. An example procedure that has been shown to have broad applicability is described below:

Weigh accurately 0.5 g of the dried sample in an appropriate flask and add 5 mL of the concentrated acid of choice. Allow the flask to sit loosely covered for 30 min in a fume hood then add an additional 10 mL of the acid, and digest using a closed vessel technique, until digestion is complete, but be sure to follow the manufacturer’s recommended procedures to ensure safe use. Make up to an appropriate volume and analyze using the technique of choice.

A 9:1 mixture of concentrated nitric acid to hydrochloric acid is typical for most cannabis infused samples (10–12).

Two analytical procedures are suggested in this chapter. Where elemental impurities are typically at the parts-per-million level in the diluted sample, ICP–OES is the recommended technique. Whereas, for elemental impurities at the parts-per-billion level or lower in the diluted sample, ICP–MS is the preferred technique. The chapter also describes criteria for an alternative procedure, such as atomic absorption (AA) as long as it meets the validation requirements laid-out in this chapter. Whichever technique is used, the analyst should verify that the procedure is appropriate for the instrument and samples being analyzed by meeting the Procedure Validation requirements described below.

 Even though ICP-OES might be suitable for a small number of consumer products, it is well accepted that ICP-MS will be required for the majority of plant or flower materials, edibles, topical, and inhalation products, as demonstrated by the publication of a recent ASTM standardized method (13).

All analytical procedures—including ICP-OES, ICP-MS, or an alternative procedure—must be validated and shown to be acceptable, in accordance with the validation protocol. The level of validation necessary depends on whether a limit test or a quantitative determination is specified in the individual monograph. The requirements for the validation of an elemental impurity’s procedure for each type of determination are described below. Any alternative procedure that has been validated and meets the acceptance criteria is considered suitable for use.

 Validation protocols are critically important for measuring heavy metals in cannabis-infused products because it’s very difficult to use one matrix as a quality control sample which is representative of all samples being analyzed.

The following section defines the validation parameters for the acceptability of analytical procedure to monitor PDE limits. Meeting these requirements must be demonstrated experimentally using an appropriate system suitability procedure and reference material. The suitability of the method must be determined by conducting studies with the material under test supplemented or spiked with known concentrations of each target element of interest at the appropriate acceptance limit concentration.

 It should also be emphasized that the materials under test must be spiked before any sample preparation steps are performed.

To understand the suitability of the technique being used and whether its detection capability is appropriate for the analytical task, it’s important to know the PDE limit for each target element and, in particular, what the USP calls its J-value. In Chapter <233>, the J-value is defined as the PDE concentration of the element of interest, appropriately diluted to the working range of the instrument, after the sample preparation step is completed.

So, let’s take Pb as an example. The oral PDE limit for Pb defined in Chapter <232> is 5 µg/day. Based on a suggested dosage of 10 g of the drug product/day, that’s equivalent to 0.5 µg/g Pb. If 0.5 g of sample is digested or dissolved and made up to 50 mL, that’s a 100-fold dilution, which is equivalent to 5 µg/L. So, the J-value for Pb in this example is equal to 5 µg/L (ppb).

The method then suggests using a calibration made up of two standards and a matrix blank: Standard 1 = 0.5 J and Standard 2= 2.0 J. So, for Pb, that’s equivalent to 2.5 µg/L for Std 1 and 10 µg/L for Std 2.

The suitability of a technique is then determined by measuring the calibration drift by comparing results for Standard 1 before and after the analysis of all the sample solutions under test. This calibration drift should always be <20% for each target element.

It should also be pointed out that no specific instrumental parameters are suggested in this section, but only to analyze according to the manufacturer’s suggested conditions and to calculate and report results based on the original sample size. However, it does say that appropriate measures must be taken to correct for interferences, such as matrix-induced wavelength overlaps in ICP-OES and argon-based polyatomic interference with ICP-MS. For guidance, it references the use of 

General Chapter <730> on Plasma Spectrochemistry 

United States Pharmacopeia National Formulary 

) (15) describing both ICP-OES and ICP-MS techniques for the determination of elemental impurities in pharmaceutical materials.

The suitability of the technique and analytical procedure is then determined by running a set of validation protocols, which cover a variety of performance and quality tests, including:

It should also be noted that, where appropriate, certified reference materials (CRM) from a national metrology institute (NMI) or reference materials that are traceable to that CRM should be used. An example of an NMI in the United States is the National Institute of Standards and Technology (NIST) who are in the process of developing a hemp flower and a hemp oil CRM for up to 13 toxic metals (16). However, for many other cannabis and drug products it’s very difficult to find a generic matrix that is representative of all typical samples. For that reason, monitoring recoveries of spiked additions are the accepted way of compensating for the wide variabilities in sample matrices.

This section deals with instrumental detectability.

 of target elements at J and a matrix matched blank

The technique or procedure is considered acceptable when:


gives a signal intensity equal to or greater than the Standard Solution

Prepare six separate test sample solutions and spike each one at a target concentration of 1.0 J. Acceptance criterion: RSD for the six individual samples should be <20%.

The procedure must be able to assess the impact of each target element in the presence of other components that may be present in the sample, including other target elements, matrix components, and other interfering species. It refers to 

USP-NF General Chapter <1225> Validation of Compendial Procedures

This test is designed to assess the accuracy of the analytical method or procedure and, in particular, when samples may be above the normal calibration range.

Prepare standard solutions containing target elements at concentrations ranging from 0.5 J to 2.0 J using suitable calibration or reference materials.

Run calibration using calibration standards.

Prepare samples under test by spiking at concentrations from 0.5 J to 2.0 J before any sample preparation is carried out.

The technique or procedure is considered acceptable when the spike recovery of three replicates at each sample concentration should be 70–150%. If available, CRMs should also be used to confirm accuracy of the method.

The effect of random events on the analytical precision of the method shall be established by performing the “Repeatability” test on (1) different days, (2) with different instrumentation, or (3) with different analysts. 

 Only one of these three experiments are required to demonstrate ruggedness.

Acceptance criterion: RSD should be <25% for each element.

The limit of quantification (LOQ) and linear range capability is demonstrated by meeting the 

It’s also worth mentioning that with pharmaceutical manufacturers, federal regulators can inspect them at any time and ask to see the data to confirm their products are compliant with the PDE limits defined in Chapter <232>. And if not, they must show evidence that they are following the validation protocols defined in Chapter <233>. The US Food and Drug Administration (FDA) even has the power to either fine that company or in extreme cases shut them down if there is flagrant abuse of the process. It is highly unlikely that the majority of cannabis testing laboratories are carrying out these exhaustive validation protocols, but even if they are, do state regulators have the time and the inclination to be holding them to the same high standards as pharmaceutical regulators? . . . I suspect not!

There’s no question that the cannabis industry is facing some serious challenges. Lab shopping has driven many testing laboratories out of business, and as result consumers are not sure whether certificates of analysis (COAs) can actually be believed (18). For that reason, they are turning to the lower cost illicit market, where regulations are lapse and testing is often non-existent. Nowhere is this more evident than in the testing of heavy metals. In a recent ASTM symposium on contaminants, a number of researchers reported finding up to 10 toxic metals in unused vape oils and 21 elemental contaminants in cannabis flower samples (19). There is also a mountain compelling evidence in the public domain that confirms this (20).

So clearly the big four heavy metals required by the majority of states is totally inadequate, but how many are actually worthy of consideration? ICP-MS does not care how many elements you throw at it. It can measure the periodic table in a few minutes, with very little penalty on the cost of analysis when measuring four, 20, or even 40 analytes per sample (21). So, it is clear that a federally regulated suite of heavy metals is most probably going to include a wider panel than just the big four. For that reason, comprehensive quality control procedures are going to be critically important to ensure data integrity. Pharmaceutical regulators realized this by writing a very detailed chapter on the ICP-MS analytical procedure, including exhaustive validation protocols. The cannabis testing community in conjunction with state regulators should start implementing the protocols defined in USP Chapter <233>, if they haven’t begun to already because this will be the standard by which they will be assessed when federal oversight of the industry eventually becomes a reality.

Thomas, R., The Importance of Measuring Heavy Metal Contaminants in Cannabis and Hemp, 

, (accessed Oct. 20, 2023 from website), 

https://cdn.technologynetworks.com/ac/Resources/pdf/the-importance-of-measuring-heavy-metal-contaminants-in-cannabis-and-hemp-312957.pdf

Pruyn, S.A., Wang, Q., Wu, C.G., and Taylor, C.L., Quality Standards in State Programs Permitting Cannabis for Medical Uses, 

Thomas, R. and Destefano, A., Understanding Sources of Heavy Metals in Cannabis and Hemp: Benefits of a Risk Assessment Strategy, 

https://www.analyticalcannabis.com/articles/how-to-better-understand-sources-of-heavy-metal-contaminants-in-cannabis-part-1-what-can-we-learn-313862

United States Pharmacopeia General Chapter <232> Elemental Impurities – Limits: First Supplement to USP 40–NF 35, 

https://www.usp.org/chemical-medicines/elemental-impurities-updates

Navigating the Lab

The big four heavy metals required by the majority of states is totally inadequate, but how many are actually worthy of consideration? Here, Rob Thomas takes a look at the other metals and how ICP-MS can be used.

Ensuring High Data Integrity When Measuring Heavy Metals in Cannabis Consumer Products by ICP-MS: The Importance of Comprehensive Validation Procedures

The physical, cellular, and molecular adaptions that plants demonstrate in response to light intensity and spectrum are extremely salient to anyone in cannabis cultivation, specifically those looking to maximize harvest yield and phytochemical content.

What are tropisms and why should we care? 

 are biological phenomena observed in plants and other organisms. These pertain to the growth or movement of an organism in response to external stimuli, such as light, gravity, touch, or chemicals. Tropisms are essential for plants to adapt and thrive in their environment.

 is the reorientation of plant organs (stalks, branches, stems, and leaves) to compensate for the intensity and direction of light. Among the significant factors that govern plant growth and physiological processes, photons play a crucial role as the primary source of energy for synthesizing carbohydrates (glucose) through photosynthesis. The continuous movement of the light source, usually the sun, results in diurnal changes in both intensity and direction, which have a profound impact on photon availability and, therefore, plant health. To maximize light absorption for efficient photosynthesis and overall physiological development, plants have undergone evolutionary adaptations that bring forth a photomorphogenic tendency known as phototropism (1).

While the term phototropism encompasses a general growth response to a light source, 

 is a specific form of phototropism that involves the movement or reorientation of plant organs towards a light source. The word “heliotropism” is derived from the Greek words “

” meaning turn or direction. This response allows an increased exposure to photons and photonic energy for the purposes of photosynthesis and photomorphogenic functions. 

, or negative phototropism, refers to the growth or movement of plant organs away from a light source. This plant response protects light-sensitive tissues, such as roots or certain reproductive structures, from excessive light exposure. The word “skototropism” comes from the Greek word “

” meaning “darkness.” Hence, skototropism means “turning towards darkness.” When we relate this to the cultivation of cannabis (

), the role of photoreceptors becomes particularly interesting. Cannabis cultivation involves careful control of the light environment to set the potential for optimal yield and phytochemical content. Growers may manipulate light intensity, duration (photoperiod), and spectrum to influence various stages of the plant’s life cycle, from vegetative growth to flowering.

, a subclass of flavoprotein photoreceptors, are integral to the phototropic behavior observed in an array of organisms such as algae, fungi, and higher plant species. These photoreceptors are integral to plant photobiology, serving as a mechanism by which plants detect the direction, intensity, and various spectra (wavelengths) of light, which is vital for environmental adaptation and, ultimately, survival. Phototropins exhibit heightened sensitivity to blue light (2). The phototropic responses mediated by these receptors are remarkably rapid, often taking only minutes to cause plant movement in response to lighting stimuli. When these receptors sense blue light, they trigger a cascade of intercellular signals that result in the redistribution of specific phytohormones known as 

. Auxins are pivotal in governing the growth and development of plants. Phototropins, through complex signaling, activate specific proteins that influence auxin transport and redistribution within plant tissues (3).

In heliotropism, auxin is redirected to the shaded part of the plant where its accumulation leads to differential cell elongation, causing the redirection of the plant’s apex or “growing tip” towards the light source. Such movement ensures that the plant presents the largest possible surface area towards the lighting source, increasing photosynthetic and photomorphogenic function. While auxins play a predominant role in phototropism, they are not the only plant hormones involved. Other vital plant hormones, such as cytokinins and gibberellins also contribute to the complex interplay of growth regulation in plants.

Cytokinins are a class of phytohormones critically implicated in numerous aspects of plant ontogeny, especially in the regulation of cellular proliferation and expansion within the foliage. As leaves mature and enter the senescence phase—the latter part of their life cycle—cytokinins contribute to maintaining leaf vitality. They do so by modulating the build-up of sugars, thereby preventing the adverse effects of their over-accumulation, which can lead to cellular damage. At the same time, they upregulate the biosynthesis of chlorophyll, one of the pigments responsible for detecting and processing specific photons. This regulation is paramount in sustaining an extended photosynthetic period—which is vital for the generation of energy in the plant. Cytokinins promote trichome development, which is especially valuable in cannabis cultivation.

Gibberellins can combat abiotic and physiological stresses. Plants rely on these compounds to control many developmental processes, including seed dormancy, root growth and trichome or hair abundance, germination, quiescence, fruit ripening, and flowering (4). In a conducive environment, gibberellin is released within the seed, stimulating enzyme production to break down the endosperm, fostering the emergence of the radicle tip which, in time, evolves into the primary root known as a cotyledon. Subsequent to this, gibberellins set the stage for other plant hormones, such as auxins and cytokinins, which synergistically drive cell expansion and differentiation during the post-germinative growth phases. Gibberellins assume a critical role in the maturation of fruits, as they operate in tandem with auxins to spur cell division and elongation required for fruit development. By understanding and manipulating these plant hormones, cannabis cultivators can fine-tune the growth, vigor, and cannabinoid profile of their crops. Furthermore, cultivators should not overlook pertinent environmental factors that affect plant hormone levels, such as light, temperature, humidity, water, and nutrient availability—since these factors can have a significant impact on the cultivation of a bountiful and high-quality harvest.

Further Down the Rabbit Hole of Photobiology

In addition to the aforementioned physical adaptions to light and spectra, there are a whole host of very interesting phenomena occurring at the cellular and molecular levels that we should be considerate of, as we will discuss below.

In cannabis cultivation, the perception of blue light through phototropins is of considerable significance. As mentioned earlier, phototropins are a class of photoreceptor proteins activated by blue light. Blue light has a profound effect on chlorophyll formation and inhibits cell expansion resulting in reducing the size of individual cells (5). This diminishment in cellular dimensions correlates with decreased internodal spacing (the gap between nodes) ending up with shorter and denser plants. Such a morphology is particularly desirable for indoor cultivation systems. Blue light is a crucial component of horticulture; however, achieving an optimal spectral balance is key, as both excess and insufficiency can be detrimental to plant development.

Upon the absorption of blue light, activated phototropins undergo a fascinating transformation called

. This is a biochemical process wherein phototropins catalyze the transfer of phosphate groups from adenosine triphosphate (ATP) molecules to certain serine or threonine amino acid residues within their own polypeptide structure. This phosphorylation event is not a mere alteration but a pivotal molecular mechanism that propels the activation of kinase domains within the phototropin proteins, which is a crucial step to downstream signaling (6).

This activation is akin to flipping a “molecular switch” whichs triggers a cascade of intracellular signaling pathways. These pathways, laden with a variety of secondary messengers, involve the modulation of various proteins and molecules within the cell. The consequences of these alterations are far-reaching, potentially encompassing the upregulation or suppression of gene transcription and metabolic pathways. The intricacies of this signal transduction mechanism allow the plant to respond and adapt to varying light conditions, thus influencing its growth and developmental patterns in response to environmental stimuli.

Several factors influence cell growth and expansion, including plant hormones. Auxin is essential to the growth and development of plants because it promotes cell elongation and proliferation. Activating this hormone promotes the production of proton pumps, which in turn results in the acidification of the apoplast, the area outside of the plasma membrane. Auxin also enhances the activity of enzymes that add phosphate groups to proteins, targeting the second-to-last threonine residue in plasma membrane H+-ATPases, which are also responsible for acidification (7). In an acidic environment, combination of wall-loosening proteins and local acidification loosens the cell wall. Consequently, the cell wall becomes more flexible and can expand more readily.

As a result of the acidification-induced cell wall loosening, cells on the shaded side of the organ elongate more rapidly than those on the illuminated side. This differential growth causes the plant organ to bend towards the light source. Due to the softening of the cell walls from acidification, cells on the shaded side of the organ tend to stretch out more quickly than the ones exposed to light. This uneven growth prompts the plant to curve toward the light (8). The acid-triggered alterations in the cell wall allow the internal pressure of the cell—known as 

, which is the force exerted by the cell’s contents against its wall—to promote cell growth more efficiently. Hence, cells on the darker side of the plant structure, which have higher levels of the growth hormone auxin, exhibit an amplified rate of expansion driven by this turgor pressure compared to the cells on the light-facing side. Through this mechanism, plants adeptly adjust their growth to ensure they capture as much sunlight as possible, which is vital, especially in areas where light availability is uneven or limited.

In general, lighting plays a critical role in setting the potential for phytochemical development and plant crop yield. Photons are the fuel by which photosynthesis and photomorphogenic functions are powered. However, these processes are far from a simple delivery of photonic energy to the plant. In order to effectively utilize this energy, the plant is required to rapidly adapt to fully harness the benefits derived from the available light. In a complex chain of events, a plant does just this by reorienting its tissue to provide its photoreceptors the most exposure to the lighting source. This takes place within the process of phototropism, whereby plant surfaces will turn towards or away from the light source, depending upon its needs.

Of course, cultivators engaged in controlled environment agriculture (CEA) must do their part, as well. Growers will commonly space plants to minimize competition for resources, including canopy lighting exposure and root space. Defoliation is a practice that allows better exposure for multiple plants to valuable lighting, although the process has other benefits. Modern horticultural teams have access to highly-adjustable light sources, in terms of intensity, distribution, and spectrum. Adjusting all three of these variables affects every stage of plant growth, from seed or clone to harvest. It has been proven that manipulation of spectra can significantly alter phytochemical content (cannabinoids, terpenes, and flavonoids), and also produce molecules unique or normally foreign to individual cultivars (9). Even numerous experts in photobiology acknowledge the substantial lacunae in our understanding of plant-light interactions. However, given what we know today with how plants respond to lighting intensity and spectrum, there are considerable opportunities to maximize cultivation yield and phytochemical content through optimizing the configuration of light throughout the cultivation environment.

Pedmale, U. V., Celaya, R. B., and Liscum, E., Phototropism: Mechanism andoutcomes. 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3244944/

Christie J. M.,Phototropin Blue-Light Receptors. 

https://doi.org/10.1146/annurev.arplant.58.032806.103951

Sakai, T., Kagawa, T., Kasahara, M., Swartz, T. E., Christie, J. M., Briggs, W. R., Wada, M., and Okada, K.,ArabidopsisNPH1 and NPL1: Blue Light Receptors That Mediate Both Phototropism and Chloroplast Relocation. 

Savitha, T., and Sankaranarayanan, A., Ameliorative Characteristics of Plant Growth Enhancing Microbes to Revamp Plant Growth in an Intricate Environment. 

Plant-Microbe Interaction - Recent Advances in Molecular and Biochemical Approaches,

https://doi.org/10.1016/b978-0-323-91876-3.00006-3

Lightworks, California. End of Flower Cycle does it help to only use blue light. 

https://californialightworks.com/blog/does-it-help-to-use-only-blue-light-at-the-end-of-flower-cycle/

Inoue, S., Kinoshita, T., Matsumoto, M., Nakayama, K. I., Doi, M., and Shimazaki, K.,Blue Light-Induced Autophosphorylation of Phototropin is A Primary Step for Signaling. 

Majda, M. and Robert, S.,The Role of Auxin in Cell Wall Expansion. Int. J. Mol. Sci., 

Bogoslavsky, L. and Neumann, P. M., Rapid Regulation by Acid pH of Cell Wall Adjustment and Leaf Growth in Maize Plants Responding to Reversal of Water Stress. 

Hildenbrand, Z.L., Grosella, A., Manes, R.J., Jacques, A., Spurlock, M., West, C., Love, O., Westerfield, R.E., Pecore, M., Liden, T., Gao, Sh., and Schug, K.A., Variable Red Light Exposure Affects Content in Group III Cannabis Cultivars. 

 is a research Professor at the University of Texas at El Paso, the principal founder of Inform Environmental, a partner of Medusa Analytical, and is a director of the Curtis Mathes Corporation (OTC:CMCZ). Direct correspondence to: 

is an MS Chemistry student at The University of Texas at El Paso.

 is the CEO and CTO of the publicly-traded Curtis Mathes Corporation, where he specializes in the creation and manufacture of frequency-specific lighting for horticulture and phototherapy purposes.

Hildenbrand, Z., Mendoza-Dickey, H., Manes, R., Shining a Light on Cannabis Photobiology, 

The Cultivation Classroom

In this article, we dive deeper into the physical, cellular, and molecular adaptions that plants demonstrate in response to light intensity and spectrum.