How Standardized Sample Preparation Can Solve the Inter-Laboratory Variation Problem

March 6, 2020
Abstract / Synopsis: 

The problem of different cannabis laboratories obtaining different results on the same samples, inter-laboratory variation, has been studied extensively. One of the causes of the problem is different laboratories prepare their samples differently. Another issue is how varying amounts of moisture in plant material can impact potency measurements. Ideas on how to improve the situation are given.

Inter-laboratory variation is the problem in cannabis and hemp analysis where different laboratories obtain significantly different results on the same samples. Scientifically the problem has been well documented (1–6). Anecdotally I have found that many people in this industry have experienced the problem. For example, in one study (6) the tetrahydrocannabinol (THC) readings on a homogeneous marijuana distillate sample sent to five different California state licensed, ISO certified laboratories, came back with a range from 77 wt.% to 94 wt.%. These results are alarming because a uniform sample containing a high concentration of the target analyte should be one of the easiest samples to analyze correctly. In the same study a distillate sample known to be free of pesticides was spiked with known amounts of six pesticides, some well above the state of California legal limits (7). Again five laboratories were involved, and none of them detected the presence of all six pesticides, and two of them said the sample contained no pesticides at all!

The inter-laboratory variation problem isn’t found just on the THC side of the business. Significant variations in cannabinoid readings are found when the same hemp sample is sent to multiple laboratories as well. Some supporting data are shown in Table I.

These data were generated by sending seven of the same hemp samples to two state licensed, ISO certified laboratories in different parts of the country. A comparison of the cannabidiolic acid (CBDA) results show huge differences in the weight percent values for the samples, the standard deviation being greater than 4.5 wt.%. There is also a small amount of bias between the two data sets, indicating that most of the error here is random.

These results are troubling since hemp farmers are paid on the total cannabidiol (CBD) content in their crop, most of which is in the form of CBDA. If a test at 10% CBDA is used to set a purchase price, and the crop really contained 15%, then the farmer is being ripped off for hundreds of thousands of dollars. Similarly, if an extractor purchases hemp biomass that is supposedly 15% CBDA, but it’s really 10%, they are overpaying by hundreds of thousands of dollars for CBD that is not there. The inter-laboratory variation problem threatens the simple act of buying and selling product in the cannabis and hemp industries.

Table II contains an even more alarming example of inter-laboratory variation. Since the passage of the 2018 U.S. Farm Bill (8), and the promulgation by the U.S. Department of Agriculture (USDA) in October 2019 of an interim final rule (9), hemp is legally defined as having not more than 0.3 wt.% total THC. Table II shows the results of sending the same six hemp samples to two different laboratories for total THC measurements.

  1. B.C. Smith, Cannabis Science and Technology 2(2), 12–17 (2019).
  2. B.C. Smith, Cannabis Science and Technology 2(3), 10–14 (2019).
  3. M.O. Bonn-Miller, M.J.E. Loflin, B.F. Thomas, J.P. Marcu, T. Hyke, and R. Vandrey, JAMA, J. Am. Med. Assoc. 318, 1708 (2017).
  4. B. Young, The Seattle Times, January 5, 2016.
  5. L. Wagner, M. Bott, M. Villarreal, and M. Horn, NBC Bay Area, November 16, 2017,
  6. B.C. Smith, P. Lessard, and R. Pearson, Cannabis Science and Technology 2(1), 48–53 (2019).
  7. California Bureau of Cannabis Control Regulations, Section 5719.
  8. 115th United States Congress, Senate Bill S.2667, ”Hemp Farming Act of 2018.”
  10. B.C. Smith, Cannabis Science and Technology 1(4), 12–16 (2018).
  11. M. Giese, M. Lewis, L. Giese, and K. Smith, J. AOAC Int. 98(6), 1503–1522 (2015).
  12. B. Smith, M. Giese, and M. Lewis, unpublished results.
  13. B.C. Smith, Cannabis Science and Technology 2(6), 28–33 (2019).

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 Xeros, IBM, Waters Associates, and Princeton Instruments. For 20 years he ran Spectros Associates, an analytical chemistry training and consulting firm where he improved their chemical analyses. Dr. Smith has written three books on infrared spectroscopy, and earned a PhD in physical chemistry from Dartmouth College.

How to Cite this Article

B.C. Smith, Cannabis Science and Technology 3(2), 10–15 (2020).