How certain can you be that every bud within a container labeled 9.25% ∆9-tetrahydrocannabinolic acid (THCA) is exactly 9.25% THCA? Wouldn’t you like to know? The uncertainty of the measurement of THCA must be accounted for. Uncertainty, in an analytical context, refers to the range around a reported result within which the true value can be expected at a certain probability. Intensively grown cannabis plants are highly heterogeneous. Cannabis heterogeneity is contributed genetically from decades of poorly documented hybridization experiments and compositionally from environmental factors. These variables, among others, affect the distribution of key active ingredients and are also likely to cause imbalanced distributions of contaminants such as heavy metals and pesticides. Therefore, no matter how accurate and precise a testing laboratory is in measuring cannabinoids or contaminants, the resulting uncertainty introduced by the plant plays a large role. Independent testing laboratories servicing the cannabis industry contend with this inherent cannabis uncertainty on a daily basis while complying with dynamic state-by-state testing mandates and client expectations. Here we illustrate the uncertainty contributed by cannabis heterogeneity and discuss the uncertainty contributed by laboratory measurement. We propose an accurate and transparent alternative method of relaying active ingredient content, and reference regulatory guidance for handling measurement uncertainty while quantifying and reporting contaminants. A specific example of how measurement uncertainty is involved in determining the pass or fail status of a product in regard to maximum residue limits is also provided and discussed. This article is intended to spark and aid conversations about measurement uncertainty that lead to the recognition and application of effective uncertainty budgets in the cannabis arena.
This manuscript describes “measurement uncertainty” (MU) from the perspective of two International Organization for Standardization (ISO)-17025 accredited independent testing laboratories that analyze cannabis products for potency, terpenes, homogeneity, microbials, mycotoxins, heavy metals, residual solvents, and pesticides. Focus is placed on measurement uncertainties when analyzing cannabis inflorescence, since cannabis extracts and infused products are more homogeneous (if properly formulated). Under the international standard, ISO-17025:2017, accreditation organizations are requesting that laboratories make available MU associated with laboratory methods. The official definition of MU is “a parameter associated with the result of a measurement that characterizes the dispersion of values that could reasonably be attributed to the measurand” (1). In the previous issue of Cannabis Science and Technology, Patricia Atkins provided an empirical description of MU (2). This article provides a link between empirical discussions on result uncertainty and their direct application to measurements in cannabis.
MU can simply be defined as the “give or take” of a measurement. This uncertainty is experienced in everyday life whenever a measurement is taken (how hot, how tall, or how heavy something is). Factors that contribute to MU should be identified and reduced when possible. Uncertainty can be determined by measuring the same sample prepared by different analysts, on different days, using consumables from different lots, and so forth. The maximum variability that the measurement method may encounter, within bounds defined in the standard operating procedure, should be explored and included during replicate analysis. It can be difficult to identify and include all factors of uncertainty present in a measurement. Therefore, we recommend first describing and reducing the MU contributed by the laboratory method, then using the optimized method to determine the MU added by the sample. We discuss the laboratory and sample contributions to MU separately, and refer to the combined MU, along with any adopted expansion, as the uncertainty budget.
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Brianna Cassidy, PhD, is with CDX Analytics, in Salem, Massachusetts. Cindy Orser, PhD, is with Digipath Labs, in Las Vegas, Nevada. Direct correspondence to: [email protected]
How to Cite This Article
B. Cassidy and C. Orser, Cannabis Science and Technology 1(3), 16-21 (2018).