How Emerging Tech Could Improve Pesticide Analysis in Cannabis

How new technology could help scientists screen for more pesticides

The QTOF could revolutionize the way we screen for pesticides

Pesticides have always been one of the most difficult compounds to reliably detect and quantify in cannabis. There are several factors that contribute to this difficulty, but the most notable is that the toxicology data for the combustion of many pesticides is missing or unknown, making it difficult to set sensible limits on pesticide residue. This has forced legislators in many legal cannabis markets to set regulations without a true baseline to work from. 

Massachusetts naturally has the most conservative limit on pesticide residue nationwide – ten parts per billion (0.0000001% weight), which is essentially a zero-tolerance limit. This low limit creates a struggle for many testing labs, as we push the commercially-available analytical instrumentation to its maximum capabilities. Combined with the fact that cannabis itself is an incredibly complex plant comprised of a multitude of organic compounds, this creates what we analytical chemists call “interferences and signal suppression” making data analysis much more difficult.

Other states have settled on higher limits but with a much more expansive list of prohibited pesticides. This has forced labs to invest in multiple instruments and techniques to measure the required pesticides at the defined limits, which in turn drives testing costs up.

Fortunately, there is much progress being made in the technological realm of analytical instrumentation. Take, for example, quadrupole time of flight mass spectrometry (QTOF). The QTOF is similar to the triple quadrupole mass spectrometer, which has been the traditional choice of equipment for most testing labs. A major feature of the QTOF that provides a huge advantage over a triple quadrupole is the ability to take any data set you have collected and mine the data for compounds you were not looking for at the time. In triple quadrupole analysis, the data set only provides information for the compounds you have specifically programmed it to detect. In contrast, a QTOF provides the ability to go back in time and mine data for compounds that were previously of no interest. 

For example, this could’ve been an invaluable asset with the most recent vaping-related lung injury crisis. The CDC has linked the disease to vitamin E acetate, an additive sometimes used to dilute vaping products. Six months ago, nobody was talking about vitamin E acetate but if we had been measuring samples with a QTOF, we would have had the ability to reprocess all of our data from 2019 to see which samples are at risk.

There is a catch, however. The instrument trades sensitivity for specificity. For context, specificity is a measure of the ability to discern between the pesticides we’re looking for and the naturally-occurring compounds in the sample that interfere with the signal. Sensitivity describes the lowest concentration of a given compound that the instrument can reliably detect.

So what does all this mean? Basically, the QTOF is better at telling the difference between the contaminants we care about and other natural compounds in the plant at the expense of reliably detecting residues at very low levels of concentration. 

MCR labs has been investigating the feasibility of utilizing this technology to detect and measure pesticides and other contaminants in cannabis samples. The app note attached to this blog post summarizes our findings. The paramount concern with a QTOF was the reduced sensitivity and if we could reliably detect the required pesticides at ten parts per billion. The only pesticide that did not meet the requirements was Cyfluthrin but we were able to reliably detect it at fifty parts per billion. If the limits were raised, a QTOF could be employed for routine analysis of contaminants in cannabis samples. Whether the limits should be raised is another issue in itself. However Canada, where cannabis was recently legalized nationally, has set limits ranging from twenty parts per billion to 3000 parts per billion.

Furthermore, instead of testing for just nine pesticides, a chemical database could be searched for all known pesticides. This would remove the burden from regulators of coming up with a perfect all-encompassing list of banned pesticides and better serve public health and safety by enabling labs to ensure products are truly pesticide-free.

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