Tag Archives: MS

The Practical Chemist

Appropriate Instrumentation for the Chemical Analysis of Cannabis and Derivative Products: Part 1

By Rebecca Stevens
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Election Day 2016 resulted in historic gains for state level cannabis prohibition reform. Voters in California, Maine, Massachusetts and Nevada chose to legalize adult use of Cannabis sp. and its extracts while even traditionally conservative states like Arkansas, Florida, Montana and North Dakota enacted policy allowing for medical use. More than half of the United States now allows for some form of legal cannabis use, highlighting the rapidly growing need for high quality analytical testing.

For the uninitiated, analytical instrumentation can be a confusing mix of abbreviations and hyphenation that provides little obvious information about an instrument’s capability, advantages and disadvantages. In this series of articles, my colleagues and I at Restek will break down and explain in practical terms what instruments are appropriate for a particular analysis and what to consider when choosing an instrumental technique.

Potency Analysis

Potency analysis refers to the quantitation of the major cannabinoids present in Cannabis sp. These compounds are known to provide the physiological effects of cannabis and their levels can vary dramatically based on cultivation practices, product storage conditions and extraction practices.

The primary technique is high performance liquid chromatography (HPLC) coupled to ultraviolet absorbance (UV) detection. Gas chromatography (GC) coupled to a flame ionization detector (FID) or mass spectrometry (MS) can provide potency information but suffers from issues that preclude its use for comprehensive analysis.

Pesticide Residue Analysis

Pesticide residue analysis is, by a wide margin, the most technically challenging testing that we will discuss here. Trace levels of pesticides incurred during cultivation can be transferred to the consumer both on dried plant material and in extracts prepared from the contaminated material. These compounds can be acutely toxic and are generally regulated at part per billion parts-per-billion levels (PPB).

Depending on the desired target pesticides and detection limits, HPLC and/or GC coupled with tandem mass spectrometry (MS/MS) or high resolution accurate mass spectrometry (HRAM) is strongly recommended. Tandem and HRAM mass spectrometry instrumentation is expensive, but in this case it is crucial and will save untold frustration during method development.

Residual Solvents Analysis

When extracts are produced from plant material using organic solvents such as butane, alcohols or supercritical carbon dioxide there is a potential for the solvent and any other contaminants present in it to become trapped in the extract. The goal of residual solvent analysis is to detect and quantify solvents that may remain in the finished extract.

Residual solvent analysis is best accomplished using GC coupled to a headspace sample introduction system (HS-GC) along with FID or MS detection. Solid phase microextraction (SPME) of the sample headspace with direct introduction to the GC is another option.

Terpene Profile Analysis

While terpene profiles are not a safety issue, they provide much of the smell and taste experience of cannabis and are postulated to synergize with the physiologically active components. Breeders of Cannabis sp. are often interested in producing strains with specific terpene profiles through selective breeding techniques.

Both GC and HPLC can be employed successfully for terpenes analysis. Mass spectrometry is suitable for detection as well as GC-FID and HPLC-UV.

Heavy Metals Analysis

Metals such as arsenic, lead, cadmium, chromium and mercury can be present in cannabis plant material due to uptake from the soil, fertilizers or hydroponic media by a growing plant. Rapidly growing plants like Cannabis sp. are particularly efficient at extracting and accumulating metals from their environment.

Several different types of instrumentation can be used for metals analysis, but the dominant technology is inductively coupled plasma mass spectrometry (ICP-MS). Other approaches can also be used including ICP coupled with optical emission spectroscopy (ICP-OES).

Rebecca is an Applications Scientist at Restek Corporation and is eager to field any questions or comments on cannabis analysis, she can be reached by e-mail, rebecca.stevens@restek.com or by phone at 814-353-1300 (ext. 2154)

An inductively coupled plasma torch used in MS reaches local temperatures rivaling the surface of the sun. Image by W. Blanchard, Wikimedia
An inductively coupled plasma torch used in Optical Emission Spectroscopy (OES) reaches local temperatures rivaling the surface of the sun. Image by W. Blanchard, Wikimedia
The Practical Chemist

Calibration Part II – Evaluating Your Curves

By Amanda Rigdon
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Despite the title, this article is not about weight loss – it is about generating valid analytical data for quantitative analyses. In the last installment of The Practical Chemist, I introduced instrument calibration and covered a few ways we can calibrate our instruments. Just because we have run several standards across a range of concentrations and plotted a curve using the resulting data, it does not mean our curve accurately represents our instrument’s response across that concentration range. In order to be able to claim that our calibration curve accurately represents our instrument response, we have to take a look at a couple of quality indicators for our curve data:

  1. correlation coefficient (r) or coefficient of determination (r2)
  2. back-calculated accuracy (reported as % error)

The r or r2 values that accompany our calibration curve are measurements of how closely our curve matches the data we have generated. The closer the values are to 1.00, the more accurately our curve represents our detector response. Generally, r values ≥0.995 and r2 values ≥ 0.990 are considered ‘good’. Figure 1 shows a few representative curves, their associated data, and r2 values (concentration and response units are arbitrary).

Figure 1: Representative Curves and r2 values
Figure 1: Representative Curves and r2 values

Let’s take a closer look at these curves:

Curve A: This represents a case where the curve perfectly matches the instrument data, meaning our calculated unknown values will be accurate across the entire calibration range.

Curve B: The r2 value is good and visually the curve matches most of the data points pretty well. However, if we look at our two highest calibration points, we can see that they do not match the trend for the rest of the data; the response values should be closer to 1250 and 2500. The fact that they are much lower than they should be could indicate that we are starting to overload our detector at higher calibration levels; we are putting more mass of analyte into the detector than it can reliably detect. This is a common problem when dealing with concentrated samples, so it can occur especially for potency analyses.

Curve C: We can see that although our r2 value is still okay, we are not detecting analytes as we should at the low end of our curve. In fact, at our lowest calibration level, the instrument is not detecting anything at all (0 response at the lowest point). This is a common problem with residual solvent and pesticide analyses where detection levels for some compounds like benzene are very low.

Curve D: It is a perfect example of our curve not representing our instrument response at all. A curve like this indicates a possible problem with the instrument or sample preparation.

So even if our curve looks good, we could be generating inaccurate results for some samples. This brings us to another measure of curve fitness: back-calculated accuracy (expressed as % error). This is an easy way to determine how accurate your results will be without performing a single additional run.

Back-calculated accuracy simply plugs the area values we obtained from our calibrators back into the calibration curve to see how well our curve will calculate these values in relation to the known value. We can do this by reprocessing our calibrators as unknowns or by hand. As an example, let’s back-calculate the concentration of our 500 level calibrator from Curve B. The formula for that curve is: y = 3.543x + 52.805. If we plug 1800 in for y and solve for x, we end up with a calculated concentration of 493. To calculate the error of our calculated value versus the true value, we can use the equation: % Error = [(calculated value – true value)/true value] * 100. This gives us a % error of -1.4%. Acceptable % error values are usually ±15 – 20% depending on analysis type. Let’s see what the % error values are for the curves shown in Figure 1.

practical chemist table 1
Table 1: % Error for Back-Calculated Values for Curves A – D

Our % error values have told us what our r2 values could not. We knew Curve D was unacceptable, but now we can see that Curves B and C will yield inaccurate results for all but the highest levels of analyte – even though the results were skewed at opposite ends of the curves.

There are many more details regarding generating calibration curves and measuring their quality that I did not have room to mention here. Hopefully, these two articles have given you some tools to use in your lab to quickly and easily improve the quality of your data. If you would like to learn more about this topic or have any questions, please don’t hesitate to contact me at amanda.rigdon@restek.com.