Scientific testing is perceived as a straightforward process that involves grabbing a pinch of a sample, running it through an instrument, and immediately out comes the exact answer. More often than not, laboratories are challenged with highly regulated and difficult sample schematics, sample preparation, extraction, and testing procedures that try to ensure accuracy and precision of testing. Accuracy in analytical testing starts at the very beginning with sampling and sample preparation prior to testing. If the initial sample collection and preparation are flawed then the final answers will be biased. The basis of accuracy of sampling and testing often rests on two inter-related and fundamental concepts: representative samples and homogeneity. Representative samples are selected to accurately reflect the larger group and should represent the characteristics of the group as a whole. Ideally representative samples are homogeneous or similar in nature, but when that is not possible the best attempts must be made to achieve samples that represent the majority of the characteristics of the larger grouping.
Agricultural samples can be some of the most difficult samples in the world to sample, prepare, and analyze because of their heterogeneous nature and complex matrices. Luckily for most of the agricultural testing world, the industry is equipped with detailed methods for operations, collection, and testing. Sampling for a crop farmer is a defined process of removing samples at designated intervals and testing those samples for the prescribed list of chemical and biological targets. Unfortunately, the cannabis grower has had limited guidance to refer to for managing operations, sampling, and testing. There is also difficulty in the fact that cannabis is a very complex plant.
There have been more than 500 compounds identified in cannabis (many of which are unique to the Cannabaceae family). The distribution of these compounds is highly dependent on individual strains, the gender of the plant, and the location on the plant (1,2). The distribution and concentration of these compounds can also be effected by environmental conditions such as soil, water, and light (1–3). To further complicate the analysis of chemicals in cannabis is the fact that different amounts of compounds can occur in different locations within the plant. In some cases it has been reported that higher tetrahydrocannabinol (THC) concentrations are found in buds located high on the plant as opposed to buds located lower in the plant (2). Different growing conditions, seasons, environmental, and chemical exposure can also alter the chemical composition between growing cycles as well as the chemical distribution within an individual plant.
In addition to a lack of guidance and a complex heterogeneous nature, there is the added concern that the crop itself is a commodity of high economic value, which inherently forces the grower to limit the amount of samples submitted for testing while individual states have begun to mandate sampling minimums (4,5). Sampling of cannabis brings into question how much and what type of sample is enough to conduct representative sample testing, and what are the criteria for sample homogeneity. Many sample preparation and test methods depend on the foundation of representative samples and homogeneity to provide accurate results. If sampling schematics are not designed to ensure representation and homogeneity of the entire crop through to the final analytical sample, then the testing will be biased.
The idea of a sample and sampling is commonplace to most of us. A sample is a small part of something which represents a larger whole or grouping. We take a sample of food and judge the taste of the dish based on one bite. We sample a few notes or bars of a song and decide if we like the music. Our minds often make an assumption of sameness or homogeneity based on a sample and the perception of the larger whole. We estimate the size of our sample based on our bias and perception. But what if the perception causes us to take too small of a sample to get all the different or heterogeneous elements? What happens when a sample is taken from a heterogeneous part and is not representative of the entire portion or population? Imagine taking a sample of some spicy chili and your first and only bite contains a whole hot pepper! Maybe you should have taken a bigger bite with more than just that pepper, or maybe you should have found a different bite to sample.
The concepts of big and little, same and different are introduced very early in all of our lives. We ask a child to pick out the big ball and they zero in on the largest ball in the group. We ask for the child to find which item in a set is different and they do. As we grow older, we start to understand that big and little, same and different are concepts that depend on focus, perspective, and often purpose. Those concepts change with our reasons for defining them and the potential bias that can be included in the choices made. It is important to look at sampling from the grow through to the laboratory to examine where the focus and perspective should be to ensure that cannabis sampling at every stage of the process represents the whole we are trying to characterize.
Into the Field
The key concepts on the larger scale or the grow side are: population, sampling frame, and representative samples. A population is the entire possible group of objects that are being sampled or a subset of those objects. A sampling frame is a possible source material taken from the population where samples will be obtained (Figure 1). So if a single variety of cannabis in a crop of multiple varieties is to be harvested, then the population can be seen as the entire crop of all varieties and the sampling frame is all the harvested plants of a single variety or strain. The sampling frame is composed of primary samples which in turn are composed of sample units. Sample units are the smallest discrete portions that are taken to form the whole or part of a primary sample. For the cannabis industry, the population or sampling frame could be as large as an entire crop, just one variety within an operation, or as small as selected trimmed buds from specific plants, varieties, or just areas of growth, depending on the purpose for the sampling.
There are two basic types of sampling: probability sampling (random) and nonprobability sampling. Probability sampling is when each unit of a population or a whole has the same chance of being selected to make up a sample and the probability of being selected can be calculated. Nonprobability sampling is when samples are collected in a process where some samples are purposely selected and the selection processes do not give all the possible samples an equal chance of selection.
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Patricia Atkins is a Senior Applications Scientist with SPEX CertiPrep and a member of both the AOAC and ASTM committees for cannabis.
How to Cite This Article
P Atkins, Cannabis Science and Technology 2(2), 26-34 (2019).