Tag Archives: MS

Chris English
The Practical Chemist

Accurate Detection of Residual Solvents in Cannabis Concentrates

By Chris English
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Chris English

Edibles and vape pens are rapidly becoming a sizable portion of the cannabis industry as various methods of consumption popularize beyond just smoking dried flower. These products are produced using cannabis concentrates, which come in the form of oils, waxes or shatter (figure 1). Once the cannabinoids and terpenes are removed from the plant material using solvents, the solvent is evaporated leaving behind the product. Extraction solvents are difficult to remove in the low percent range so the final product is tested to ensure leftover solvents are at safe levels. While carbon dioxide and butane are most commonly used, consumer concern over other more toxic residual solvents has led to regulation of acceptable limits. For instance, in Colorado the Department of Public Health and Environment (CDPHE) updated the state’s acceptable limits of residual solvents on January 1st, 2017.

Headspace Analysis

Figure 1: Shatter can be melted and dissolved in a high molecular weight solvent for headspace analysis (HS). Photo Courtesy of Cal-Green Solutions.

Since the most suitable solvents are volatile, these compounds are not amenable to HPLC methods and are best suited to gas chromatography (GC) using a thick stationary phase capable of adequate retention and resolution of butanes from other target compounds. Headspace (HS) is the most common analytical technique for efficiently removing the residual solvents from the complex cannabis extract matrix. Concentrates are weighed out into a headspace vial and are dissolved in a high molecular weight solvent such as dimethylformamide (DMF) or 1,3-dimethyl-3-imidazolidinone (DMI). The sealed headspace vial is heated until a stable equilibrium between the gas phase and the liquid phase occurs inside the vial. One milliliter of gas is transferred from the vial to the gas chromatograph for analysis. Another approach is full evaporation technique (FET), which involves a small amount of sample sealed in a headspace vial creating a single-phase gas system. More work is required to validate this technique as a quantitative method.

Gas Chromatographic Detectors

The flame ionization detector (FID) is selective because it only responds to materials that ionize in an air/hydrogen flame, however, this condition covers a broad range of compounds. When an organic compound enters the flame; the large increase in ions produced is measured as a positive signal. Since the response is proportional to the number of carbon atoms introduced into the flame, an FID is considered a quantitative counter of carbon atoms burned. There are a variety of advantages to using this detector such as, ease of use, stability, and the largest linear dynamic range of the commonly available GC detectors. The FID covers a calibration of nearly 5 orders of magnitude. FIDs are inexpensive to purchase and to operate. Maintenance is generally no more complex than changing jets and ensuring proper gas flows to the detector. Because of the stability of this detector internal standards are not required and sensitivity is adequate for meeting the acceptable reporting limits. However, FID is unable to confirm compounds and identification is only based on retention time. Early eluting analytes have a higher probability of interferences from matrix (Figure 2).

Figure 2: Resolution of early eluting compounds by headspace – flame ionization detection (HS-FID). Chromatogram Courtesy of Trace Analytics.

Mass Spectrometry (MS) provides unique spectral information for accurately identifying components eluting from the capillary column. As a compound exits the column it collides with high-energy electrons destabilizing the valence shell electrons of the analyte and it is broken into structurally significant charged fragments. These fragments are separated by their mass-to-charge ratios in the analyzer to produce a spectral pattern unique to the compound. To confirm the identity of the compound the spectral fingerprint is matched to a library of known spectra. Using the spectral patterns the appropriate masses for quantification can be chosen. Compounds with higher molecular weight fragments are easier to detect and identify for instance benzene (m/z 78), toluene (m/z 91) and the xylenes (m/z 106), whereas low mass fragments such as propane (m/z 29), methanol (m/z 31) and butane (m/z 43) are more difficult and may elute with matrix that matches these ions. Several disadvantages of mass spectrometers are the cost of equipment, cost to operate and complexity. In addition, these detectors are less stable and require an internal standard and have a limited dynamic range, which can lead to compound saturation.

Regardless of your method of detection, optimized HS and GC conditions are essential to properly resolve your target analytes and achieve the required detection limits. While MS may differentiate overlapping peaks the chances of interference of low molecular weight fragments necessitates resolution of target analytes chromatographically. FID requires excellent resolution for accurate identification and quantification.

The Practical Chemist

Pesticide Analysis in Cannabis and Related Products: Part 3

By Julie Kowalski
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As mentioned in Part 1, pesticides residue analysis is very challenging especially considering the complexity of cannabis and the variety of flower, concentrates and infused products. In addition, pesticides are tested at low levels typically at parts-per-billion (ppb). For example, the food safety industry often uses 10 ppb as a benchmark limit of quantification. To put that in perspective, current pesticides limits in cannabis range from 10 ppb default (Massachusetts Regulatory Limit) to a more typical range of 100 ppb to 2 ppm in other states. Current testing is also complicated by evolving regulations.

Despite these challenges, adaptation of methods used by the food safety industry have proved successful for testing pesticides in cannabis. These methods typically rely on mass spectrometric detection paired with sample preparation methods to render the sample clean enough to yield quality data.

Pesticide Analysis Methods: Sample preparation and Analytical Technique Strategy

Generally, methods can be divided into two parts; sample preparation and analytical testing where both are critical to the success of pesticide residue testing and are inextricably linked. Reliance on mass spectrometric techniques like tandem mass spectrometry and high resolution accurate mass (HRAM) mass spectrometry is attributed to the substantial sensitivity and selectivity provided. The sensitivity and selectivity achievable by the detector largely dictates the sample preparation that will be required. The more sensitive and selective the detector, the less rigorous and resource intensive sample preparation can be.

Analytical technique: Gas and Liquid Chromatography Tandem Mass Spectrometry 

The workhorse approach for pesticide residue analysis involves using gas chromatography and liquid chromatography tandem mass spectrometry (MS/MS) in the ion transition mode. This ion transition mode, often referred to as multiple reaction monitoring (MRM) or selected reaction monitoring (SRM), adds the selectivity and sensitivity needed for trace level analysis. Essentially, a pesticide precursor ion is fragmented into product ions. The detector monitors the signal for a specified product ion known to have originated from the pesticide precursor ion. This allows the signal to be corrected, associated with the analyte and not with other matrix components in the sample. In addition, because only ions meeting the precursor/product ion requirements are passed to the detector with little noise, there is a benefit to the observed signal to noise ratio allowing better sensitivity than in other modes. Even though ion transitions are specific, there is the possibility a matrix interference that also demonstrates that same ion transition could result in a false positive. Multiple ion transitions for each analyte are monitored to determine an ion ratio. The ion ratio should remain consistent for a specific analyte and is used to add confidence to analyte identification.

The best choice for pesticide analysis between gas chromatography (GC) and liquid chromatography (LC) is often questioned. To perform comprehensive pesticide screening similar to the way the food safety market approaches this challenge requires both techniques. It is not uncommon for screening methods to test for several hundred pesticides that vary in physiochemical properties. It may be possible that with a smaller list of analytes, only one technique will be needed but often in order to reach the low limits for pesticide residues both GC and LC are required.

Modified QuEChERS extraction using 1.5 grams of cannabis flower. Courtesy of Julie Kowalski (Restek Corporation), Jeff Dahl (Shimadzu Scientific Instruments) and Derek Laine (Trace Analytics).
Modified QuEChERS extraction using 1.5 grams of cannabis flower. Courtesy of Julie Kowalski (Restek Corporation), Jeff Dahl (Shimadzu Scientific Instruments) and Derek Laine (Trace Analytics).

Analytical technique: Sample Preparation

Less extensive sample preparation is possible when combined with sensitive and selective detectors like MS/MS. One popular method is the QuEChERS approach. QuEChERS stands for Quick, Easy, Cheap, Effective, Rugged and Safe. It consists of a solvent extraction/salting out step followed by a cleanup using dispersive solid phase extraction. Originally designed for fruit and vegetable pesticide testing, QuEChERS has been modified and used for many other commodity types including cannabis. Although QuEChERS is a viable method, sometimes more cleanup is needed and this can be done with cartridge solid phase extraction. This cleanup functions differently and is more labor intensive, but results in a cleaner extract. A cleaner extract helps to secure quality data and is sometimes needed for difficult analyses.

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
amandarigdon
The Practical Chemist

Calibration Part II – Evaluating Your Curves

By Amanda Rigdon
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amandarigdon

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.