The Importance of Representative Sampling in Cannabis Analysis

February 8, 2019
Abstract / Synopsis: 

Developing a precise and accurate analytical method is important, but it is not enough. The sample itself plays a role in determining the quality of your results. Your sample needs to be stable, homogeneous, and representative of the batch from which it is taken. We discuss all this in more detail below and what it means for the quality of the results obtained in cannabis analysis.

The term sample is defined as a part of anything presented. . . as evidence of the quality of the whole (1). Samples are something we encounter in our everyday lives. We may nibble on a sample of food presented to us at a grocery store. Your doctor may collect a sample of your blood. Come election time we are bombarded with opinion polls that talk about something called “sample size” and that pesky “margin of error.” Ideally, all these samples represent a greater whole from which they are drawn. Hopefully, the product you buy because of a food sample will be as tasty as the nibble that originally enticed you. Your blood sample needs to be representative of your health or your doctor might make a wrong diagnosis. If a political poll is biased it can be misleading. A sample taken improperly, one that does not represent the quality of the whole, is called an unrepresentative sample. Unrepresentative sampling leads to what is called sampling error.

What’s all this got to do with cannabis analysis? In many other industries we must collect representative samples to perform chemical analyses, the cannabis industry is no different. In fact, in California the cannabis analysis laboratories themselves are tasked with collecting representative samples for compliance testing (2). Also, most cannabis businesses collect samples for testing for their own purposes. The purpose of this article then is to discuss the importance of representative sampling, some practical tips on how to ensure representative sampling, and how to quantitate and minimize sampling error.

Why should we care about any of this? Because an unrepresentative sample can give incorrect or misleading results. For example, it has been shown that cannabis buds harvested from the top, middle, and bottom of a plant can vary in potency by several percent (3), and that the buds at the top tend to have the highest potency. Now, if it’s your job to collect a representative sample from a field of plants, how would you go about doing it? It might be tempting to grab buds from the top of the plants because that is easiest and would give you the highest potency reading. This would also be wrong because it would not give you an accurate picture of the potency of your crop. You can’t possibly optimize the use of water, fertilizer, and light if you don’t have a representative way of sampling your grow. So, how do we know if the sample we’ve taken is truly representative of the whole? How do we go about collecting a sample to ensure this? Please keep reading.

The tern aliquot is defined as comprising a known fraction of a whole (4). An aliquot is something most people haven’t heard of, unless you’re a chemist. An aliquot is similar to a sample, but in analytical chemistry an aliquot is generally the portion of the sample that is analyzed. An aliquot then is a sample of the sample, and it too must be as representative of the whole as the original sample was.

We are forced to take aliquots because of the needs of our instruments. For example, the state of California requires the collection of a 50 lb harvest batch of cannabis plant material prior to analysis (2). We can’t shove 50 lbs of material through a chromatograph or spectrometer. In fact, today’s analytical instruments are so sensitive that only small amounts of sample are required for a successful analysis. For example, most high performance liquid chromatography (HPLC) potency measurement methods only require 1 g or so of sample (5–7). The 1-g portion analyzed in this case is an aliquot. Our same concerns about samples expressed above also apply to aliquots. How do we know if the aliquot is truly representative of the sample? How do we go about collecting an aliquot properly? How do we quantify aliquot homogeneity?

Practical Sampling Advice

In sampling, homogeneity is our friend and inhomogeneity is our enemy. Homogeneous materials are the same throughout the whole. Inhomogeneous materials vary throughout the whole. Not surprisingly then, collecting representative samples can range from being easy to difficult depending upon how homogeneous the whole is. A perfect whole from which to collect a sample would be homogeneous and convenient to sample. Liquids fit this bill. Our enemy in sampling liquids is concentration gradients, an inhomogeneous distribution of molecules. Since molecules can diffuse in liquids, concentration gradients tend to naturally decrease over time, but this happens easier in nonviscous liquids than it does in viscous materials: think about the difference in viscosity between water and cannabis distillate. Fortunately, any concerns about liquid inhomogeneity can be overcome by simply mixing or stirring the sample. I performed a small experiment to confirm this. I was given a vial of unwinterized cannabis extract and asked to measure its potency. I used a mid-infrared (IR) method since it was fast and convenient (8,9). The sample was a thick, brown liquid with yellow streaks in it, obviously not uniform. I initially took three separate aliquots from the sample and analyzed them for total tetrahydrocannabinol (THC). I observed a spread in the values. I then simply stirred the sample to homogenize it, measured three more aliquots for total THC, and found the spread in measurements had been reduced by a factor of 2. This is a strong argument then for always stirring liquids before analyzing them.

  1. Webster’s Collegiate Dictionary, (G. & C. Merriam, Springfield, Massachusetts, 1946).
  3. M. Giese and M. Lewis, private communication.
  4., accessed 12-30-2018.
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  6. B. De Backer, B. Debrus, P. Lebrun, L. Theunis, N. Dubois, L. Decock, A Verstraete, P. Hubert, and C. Charlier, J. Chromatogr. B: Biomed. Sci. Appl. 877, 4115 (2009).
  7. M. Giese, M. Lewis, L. Giese, and K. Smith, J. AOAC Intl. 98, 1503 (2015).
  8. B.C. Smith, Terpenes & Testing Magazine Nov/Dec(6), 48–51 (2017).
  9. B.C. Smith, Terpenes & Testing Magazine Jan/Feb(7), 34–40 (2018).
  12. Cannabis Science and Technology, 1(3), cover (2018).
  13. J. Strull, private communication.
  14. B.C. Smith, Cannabis Science and Technology 1(4), 12–16 (2018).
  15. D. Shoemaker and C. Garland, Experiments in Physical Chemistry (McGraw-Hill, New York, New York, 1967).
  16. B.C. Smith, Quantitative Spectroscopy: Theory and Practice (Elsevier, Boston, Massachusetts, 2002).

About the Columnist

Brian C. Smith, PhD, is Founder, CEO, and Chief Technical Officer of Big Sur Scientific in Capitola, California. Dr. Smith has more than 40 years of experience as an industrial analytical chemist having worked for such companies as Xerox, IBM, Waters Associates, and Princeton Instruments. For 20 years he ran Spectros Associates, an analytical chemistry training and consulting firm where he taught thousands of people around the world how to improve their chemical analyses. Dr. Smith has written three books on infrared spectroscopy, and earned his PhD in physical chemistry from Dartmouth College.

How to Cite This Article

B.C. Smith, Cannabis Science and Technology 2(1), 14-19 (2019).