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I recently received some feedback on the previous posts in this blog series and was asked the question “what kind of carryover am I seeing?” The truth of the matter is that I struggled to answer this question. I believe this question needs to be qualified a little bit in order for me to answer it. So I am going to break the aforementioned general question into the following two specific questions:
- Am I seeing carryover at concentrations typically encountered (i.e., <100 ppbv)?
- Am I seeing carryover at concentrations out of this world (i.e., >500 ppbv)?
Why the qualification? Well, the answer is different for both questions. Also, the first scenario (i.e., concentrations <100 ppbv) is going to account 99.9% of ambient air samples a laboratory will encounter. In addition, remember that NJ LL TO-15 states that the calibration range “must be from 0.20 to 40 ppbv.” This means that the >500 ppbv scenario represents a blown-out soil vapor sample, which needs to be diluted and/or injected at a smaller volume so that it falls within the calibration range. In any event, in an attempt to answer these questions I analyzed a series of samples followed by blanks as follows:
- Experiment 1: Blank – 200 ppbv – Blank – 200 ppbv – Blank – 200 ppbv – Blank
- Experiment 2: Blank – 1,000 ppbv – Blank – 1,000 ppbv – Blank – 1,000 ppbv – Blank
I observed an average carryover of 0.03 and 0.17% for experiments 1 and 2, respectively. Refer to figures 1 and 2 (below) for a visual representation of what experiment 2 looks like.
Figure 1: Black trace = 1 ppmv, blue trace = typical blank, red trace = carryover sample analyzed immediately after 1 ppmv.
Refer to Figure 2 for a zoom.
Figure 2: Zoom of Figure 1. Black trace = 1 ppmv, blue trace = typical blank, red trace = carryover sample analyzed immediately after 1 ppmv.
Is this good? IDK… The interesting thing is that I cannot genuinely tell you what % carryover is good or bad, as I have never seen anything published by any of the manufacturers. So if I answered the question like this, I am not sure where this would take us. Personally, I think these numbers look great! Think about it… less than 1% carryover on an instrument expected to meet method criteria of ±30%. BUT…
We should note that these same carryovers correspond to 0.03 and 1.14 ppbv in the carryover runs for experiments 1 and 2, respectively. Now what does this mean? It means that when loading “hot” soil vapor samples onto your system for analysis, it would be wise to run them at the end of the sequence and with blanks/conditioning runs in between. Certainly do not try to analyze sub slab samples from Long Island and then samples from the High Sierras.
So to answer the original question: I would say nothing to speak of at typical concentrations and about what I would expect for a “hot” sample analyzed via a headspace, purge and trap, air preconcentrator, or any other analytical instrument containing a solid sorbent bed.
After coming back from a huge lunch at the Bellefonte Wok, a favorite Restek lunch spot, I’m completely stuffed, but I have to eat my words from a previous blog. In this blog, I made the case against using MS detection for headspace analysis of residual solvents in cannabis concentrates due to interference between the air peak from the headspace injection and propane. It turns out that’s absolutely not the case, as my colleague and Restek air chemist Jason Herrington argued when I first posted the blog.
During a trip to Trace Analytics in Spokane, WA, I had the opportunity to run cannabis residual solvents using the full evaporation technique with headspace GC/MS (FET-HS-GC/MS), and lo and behold, propane is resolved from the air peak as shown in Figure 1.
Figure 1: Propane is Resolved from Air Peak on the Rxi-624Sil MS using FET-HS-GC/MS (25ppm Standard)
Propane is still well-resolved even at higher concentrations where band broadening may become an issue (Figure 2):
Figure 2: Propane is Resolved from Air Peak Even at Higher Concentrations (500ppm Standard)
Given the close proximity of propane to air, the two peaks may co-elute under sub-optimal injection conditions, but the two peaks can be separated from one another easily under the chromatographic conditions published in this protocol.
Although I dismissed the use of MS originally, there are benefits that I failed to mention. In addition to the ability to confirm peak identity using mass spectra, sensitivity is improved over GC-FID for the later eluting compounds like benzene, toluene, and the xylenes (BTX). This is because these compounds produce higher molecular weight fragments (78, 91, 106 m/z), and MS detectors aren’t great at reliably detecting low mass fragments from solvents like propane, methanol, and butane (29, 31, 43 m/z). Luckily, the solvents that produce the lower molecular-weight fragments have much higher regulatory cutoffs than butane, toluene, and xylenes, so detectability via MS isn’t an issue. Figure 3 shows an extracted ion chromatogram of our low standard (0.5ppm) and the signal-to-noise ratio is very good for BTX. Note that this chromatogram was collected in scan mode, so even more sensitivity can be gained from the development of a selected ion monitoring (SIM) method.
Figure 3: Good Sensitivity is Achieved for BTX using FET-HS-GC/MS (XIC of 78, 91, 106m/z, 0.5ppm Level)
All this being said, there is one big caveat to the use of MS for analysis of residual solvents in cannabis concentrates: the limited linear dynamic range of MS detectors when compared to FIDs. Where a FID can produce a linear curve over several orders of magnitude, an MS detector has a dynamic range limited to about three orders of magnitude. This means that one curve covering the entire regulatory range (e.g. 0.5 – 5500ppm) cannot be run using MS. In fact, under the conditions listed in the protocol for this method, BTX ions begin to saturate the detector at 250ppm (Figure 4), and almost all major ions for all analytes are saturated at 500ppm (Figure 5). This results in quadratic calibration curves for our analytes over even this limited range of quantification (Figure 4 inlay). If we tried to extend the curve beyond 500ppm, even a quadratic fit would be inappropriate.
Figure 4: BTX are Saturated at 250ppm, Resulting in a Quadratic Calibration Curve (Inlay)
Figure 5: Almost All Compounds are Saturated at 500ppm
While quadratic curves aren’t inherently bad, most guidance suggests the use of a linear calibration curve if feasible. Since even a quadratic curve wouldn’t cover the full regulatory range, in the cannabis residual solvents analysis protocol I suggest breaking up calibration into high and low segments. These segments will vary by state due to different regulations, but the approach is the same. For some states with very high cutoffs (I’m talking to you, Oregon), a higher split will most likely have to be used for the higher range curves in order to avoid overloading the column. Please note that this protocol has been partially validated, but still needs some work in terms of internal standard choice and extending the range of quantification above 500ppm.
While I learned a lot in the development of this method, I think the most valuable lesson I learned is to never argue with the air chemist when it comes to volatiles analyses.
An introduction to the benefits of using split injection when performing semivolatiles analysis by 8270D – the instrument checkout mix
We have mentioned several times that the reduced residence time resulting from the fast sample transfer that occurs during a split injection reduces in-inlet degradation and adsorptive loss. Proof of this can be seen in the first run of the 8270 analysis batch – the tune, inlet inertness, and column performance verification check.
Section 7.6 of 8270D indicates that a 50 ng/µL solution of decafluorotriphenylphosphine (DFTPP), 4,4’-dichlorodiphenyltrichloroethane (p,p’-DDT), pentachlorophenol and benzidine should be used. Specifically, the total amount for each compound should be 50 ng or less; the concentration can vary.
- DFTPP is used to verify that the detector is properly tuned (more on this subject here).
- Degradation of DDT is not to exceed 20% – by the criteria listed in EPA Method 8081. Thankfully, Linx has already covered this topic in detail here.
- Benzidine and Pentachlorophenol:
- are to be present at their “normal” responses. This is vaguely worded – perhaps the intent was ± 20% like the CCV evaluation.
- should not exceed a tailing factor of 2 as calculated in Figure 1 of EPA Method 8270D (also blog Figure 1). NOTE: AB>BC is a symptom of fronting (i.e., column overload), indicating a need for less mass on column to properly perform the tailing factor check.
Figure 2 is an example of the system suitability check mix (cat# 31615) analyzed at 50 ng/µL (in dichloromethane) by a 1 µL splitless injection. Pentachlorophenol and benzidine tailing factors are 0.75 and 0.53 respectively, indicating column overload. At 50 ng on column, overload is not a surprise, but it could be problematic as the system gets dirty. The tailing factor is supposed to be a helpful metric to indicate when maintenance should be done before client samples are analyzed. Serious overload significantly biases the tailing factor measurement and can result in a value of less than 2.0, even when the inlet is dirty and maintenance should be performed.
Something else to consider: injecting enough material to overload the column not only negates the effectiveness of the tailing factor, but also the DDT degradation check and the pentachlorophenol and benzidine response check. It is likely the active sites in the inlet are also being overloaded, so you are not getting an adequate indication of inlet activity, either.
Figure 3 shows the same system suitability check mix analyzed by a 1 µL 10:1 split injection; the system configuration is the same as that used in Figure 2, except the single taper inlet liner with wool has been replaced with a split precision liner. Pentachlorophenol and benzidine now have reasonable tailing factors. The 0.94 for pentachlorophenol could indicate a slight overload, but this value of less than 1 is more likely due to the scan rate and peak apex assignment (a future blog topic).
Finally, some evidence to support the initial claim –the fast sample transfer that occurs during a split injection reduces in-inlet degradation and adsorptive loss. We see improved tailing factors and the DDT breakdown number is significantly better. The improved breakdown number is not due to reduced sensitivity. As summarized in Table 1, the TIC area for DDT was actually higher under split because the gain factor was increased to maintain sensitivity at the low end of the calibration (another future blog topic).
Summer days are my favorite – hot, sunny, and full of fun. But one thing that’s really not fun is trying to track down contamination and/or carryover in your headspace-GC (HS-GC) system. I get questions about this topic pretty regularly, so I thought I would post a blog on the most common culprit for contamination and carryover – cold spots.
When performing HS-GC, the rule of thumb is that your sample vial should be the coolest part of your system prior to the head of your column. The reason for this is that HS-GC depends on the use of heat to drive volatile compounds into the headspace of your sample vial, where they are then sampled. If you’re using a loop-style system employing a transfer line, then your sample has to be transported a long way before it gets to the head of the column. If your sample encounters a sudden drop in temperature while in transit, then some of the less volatile (or more abundant) compounds in the sample may condense in that cooler area, which is known as a ‘cold spot’, even though it could be significantly warmer than room temperature. The term ‘cold’ is relative to the other temperatures present in your sampling system.
When working with systems employing transfer lines that must be plumbed into an existing inlet, the most common cold spot is the area between where the transfer line ends and the heated inlet begins. This area is circled in Figure 1.
Figure 1: Cold Spot between Heated Transfer Line and Injection Port
As you can see, there is a good amount of tubing that is exposed to room temperature between the end of the heating jacket for the transfer line and the heated inlet. Even though your analytes may be volatile enough to make it through a cold spot like this, over time contamination from less-volatile matrix components can build up in this area, and it will eventually bleed over into your inlet, causing ghost peaks during blank runs as shown in Figure 2.
Figure 2: Ghost Peaks from Contaminated Transfer Line
The contamination shown above is really severe, and usually looks like just a few persistent peaks in your sample and blank runs. Normally, contamination can be eliminated by curing the cold spot, then running a bunch of solvent blanks (e.g. DMSO or DMA) using a method with increased transfer line, needle, and valve oven temperatures.
But how can we cure a cold spot? Two relatively easy fixes will cure most cold spots in your system. The first is to reduce the size of the cold spot. Figure 3 shows how I re-plumbed the transfer line, making the tubing going to the inlet as short as I could get it. You might also notice that since I was doing plumbing anyway, I installed an EZ-Twist Top injection port, which is much more convenient than the old-style weldment shown in Figure 1.
Figure 3: Transfer line Re-Plumbed to Reduce Size of Cold Spot
Although the size of the cold spot has been reduced, there’s still a significant length of tubing that’s exposed to room temperature. This brings us to the second part of our cold-spot remedy – an easy way to keep exposed tubing warm: glass wool and aluminum foil. Simply cover all exposed tubing and the injection port with the wool and use the foil to keep everything further insulated and to keep those irritating wool fibers contained. The setup is shown in Figure 4, and while it’s not pretty, it is effective.
Figure 4: Cold Spot Insulated with Glass Wool Insulation (inlay) and Aluminum Foil
Remember to wear gloves when handling the wool; just like fiberglass insulation, it will make you itchy! Also remember that the wool I’m describing in this blog isn’t the same as the wool that’s used in areas of the instrument that come into contact with sample. There’s no need for deactivation of the insulating wool.
I hope that this blog was useful, and make sure to get out of the lab and have some fun this summer!
I was working on a project for volatile sulfur analysis. It took so long to cool the GC oven to 30 °C, and yet the most volatile analytes were still difficult to separate. At that time, I realized that I needed a cryogenic cooling system for my Shimadzu GC-MS QP2010 plus!
However, I was not as excited as expected when I received this CRG-2010 N2 part. The CRG is designed to be installed to the left side of GC where my MS is located (see the picture below). Trade the CRG for MS? That would not work for me.
With the help of my colleagues, Becca and Corby, we decided to attach the CRG to the top of the GC, which turned out to be even better, because it is closer to the electronic board and it has a shorter route to the nitrogen tank too (see picture 1).
The installation can be as simple as these 5 steps:
- Introduce the nozzle into the oven by piercing through the top insulation and then connect the copper extension tubing into the oven (picture 2 and 3).
- Mount the side plate of the valve to the top board of the instrument (you may need to drill a hole for screw).
- Plug the cable into the side electronic board (picture 4).
- Connect the nitrogen tank to the valve.
- Assemble the outer box.
If you don’t have other detectors (like the FPD that I have installed), the back position is another good option. In this case, you don’t need to drill hole for screw (Picture 2).
Bile acids are a group of steroidal acids with carboxyl and hydroxyl groups on the side. They are the major metabolic products of cholesterol. Bile acids play important roles as biomarkers for early diagnosis and therapeutic monitoring of many diseases, especially liver and intestinal diseases. For instance, high levels of intestinal bile acids, in particular, deoxcholic acid, is an indication of colon carcinogenesis. Below are structures of some of the most common bile acids.
LC-MS and LC-MS/MS are commonly used for bile acid analysis in biological fluids without derivatization. Disadvantages of LC-based methods include relatively high carry-over on LC columns and low peak capacity.
GC is a robust, simple and inexpensive lab tool for volatile or semi-volatile compound analysis. The only drawback of GC versus HPLC for bile acid analysis is polar functional groups (carboxyl and hydroxyl) need to be derivatized into GC-detectable forms. Trimethylsilylating reagents, such as N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) and N-methyl-N(trimethylsilyl)trifluoroacetamide (MSTFA), are commonly used for derivatization of both hydroxyl and carboxyl groups. Choosing the right reagents can be tricky. Kumar et al. compared four types of derivatization mixtures: MSTFA:NH4I:DTE (500:4:2, 40 lL, 60 C, 30 min), MSTFA:TMSI:TMCS (100:2:5, 40 lL, 60 C, 30 min), MSTFA:TMCS (100:1, 40 lL, 60 C, 30 min), and BSTFA:TMCS (100:1, 40 lL, 60 C,30 min). Test results showed that MSTFA:NH4I:DTE (500:4:2, v/w/w) has the highest efficiency for bile acid derivatization. However, the mixture must be freshly prepared and thus is not convenient for fast analysis. Fortunately, my colleague Amanda Rigdon developed a one-step method for cannabinoid acids derivatization using BSTFA+1% TMCS, and the reaction is only 30 min.
Here, the same derivatization method was applied to bile acids, and the preliminary results are very promising. Basically, this reagent simply replaces the hydrogen in the –OH or –COOH groups with a trimethylsilyl group (see the picture below). The procedure is simple and straightforward.
- Place 100 µL of bile acid standards (~100 ppm) into an interlock vial
- Evaporate the solvent under nitrogen
- Add 50 µL of ethyl acetate and 50 µL of BSTFA+1% TMCS
- Incubate for 30 min at 70 °C
- Cool down and dilute with ethyl acetate if necessary
The derivatized bile acid standards were analyzed by GC-MS under full scan mode using an Rxi-5ms 30 m × 0.25 mm × 0.25 µm column. The chromatogram below shows good separation of closely related bile acids (isomers and enantiomers), such as HDCA and UDCA.
The reaction and GC conditions (reagent/bile acid amount ratio, reaction temperature, time, column dimensions, etc.) can be further optimized. Hopefully, this preliminary work could open the door for the fast GC analysis of bile acids. Keep in mind there are some limitations on conjugated bile acids.
GC analysis time too long? EZGC can help! Here is one of the modulation solutions using shorter column. The total analysis time is less than 10 min.
 (a) Jensen, R. G.; Clark, R. M.; deJong, F. A.; Hamosh, M.; Liao, T. H.; Mehta, N. R., The lipolytic triad: human lingual, breast milk, and pancreatic lipases: physiological implications of their characteristics in digestion of dietary fats. Journal of pediatric gastroenterology and nutrition 1982, 1 (2), 243-55; (b) Reddy, B. S., Diet and excretion of bile acids. Cancer research 1981, 41 (9 Pt 2), 3766-8.
 Kumar, B. S.; Chung, B. C.; Lee, Y. J.; Yi, H. J.; Lee, B. H.; Jung, B. H., Gas chromatography-mass spectrometry-based simultaneous quantitative analytical method for urinary oxysterols and bile acids in rats. Anal. Biochem. 2011, 408 (2), 242-52.
From the INEF website: “The International Network of Environmental Forensics (INEF) was founded in 2008 to provide a forum for scientists, environmental consultants, regulators and lawyers to share information regarding the use of environmental forensics. Environmental forensics is the use of scientific techniques to identify and apportion the source(s), age and timing of a contaminant into the environment. INEF is a non-profit interest group within the Royal Society of Chemistry (RSC).”
This year’s INEF Conference is in Örebro, Sweden this week. I’m pleased to have been a part of the conference almost since its inception, having served on the organizing committee in past years and given several invited lectures. Restek has supported the meeting, too, and this year is one of its main sponsors. As you might imagine when trying to define the source of environmental contamination by polychlorinated biphenyls (PCBs), petroleum hydrocarbons, chlorinated dioxins and furans, polycyclic aromatic hydrocarbons (PAHs), and other samples defined by complex congener patterns, gas chromatography (GC) plays a significant role. Restek’s contribution is having GC columns with unique selectivities that can separate critical isomers for accurate source apportionment.
Comprehensive two-dimensional GC (GC×GC), because of its significant increase in peak capacity and its ability to produce highly structured chromatograms, is becoming ever more important in environmental forensics work. This will be the subject of my INEF 2016 lecture, which is titled, “Can GC×GC Ever Be the DNA Profiling of Environmental Forensics?”. The excellent INEF 2016 program will feature talks by leading scientists in the field (Tue, Wed, Thu).
In order to determine contamination levels for phenols in water, the French normalized method, NF EN 12673, recommends an in-situ derivatization with acetic anhydride, followed by extraction with hexanes. The derivatization process (diagrammed in Figure 1) converts the phenolic compounds into phenyl acetates, which perform much better on GC/MS columns such as the Rxi-5Sil, due to the protective acetic acid ester in place of the acid proton. The acetic acid ester changes the polarity of the phenols, which creates some interesting changes in the elution order for some of the target analytes.
Table 1: The 19 chlorophenol compounds determined using NF EN 12673.
A total of 41 phenolic compounds were analyzed individually by adding 50 µL of acetic anhydride to 1 mL of the concentrated compound in methanol. This reaction allows for both the phenol compound and phenyl acetate to exist in one mixture since the excess methanol will out-compete the phenol and react with most of the acetic anhydride. Each compound (together with its derivatized analog) was analyzed individually by GC/MS on the Rxi-5Sil MS column to determine retention times.
Figure 1: Nucleophilic acyl substitution reaction used in NF EN 12673 to derivatize phenol compounds.
For 36 of the 41 compounds, the phenol eluted earlier than its phenyl acetate counterpart, indicating that the phenyl acetate group increased the compound’s affinity for the stationary phase (Figure 2). The remaining 5 compounds had a specific substitution pattern that decreased the relative retention time of the derivatized compound. The pattern places a highly electrophilic compound such as a chlorine or nitro group in the para and/or meta positions. This substitution pattern decreases the compound’s stationary phase affinity, causing the phenyl acetate to elute before the phenol (Figure 3).Figure 2: 2,4,5-trichlorophenyl acetate (left) and 2,4,5-trichlorophenol (right) are an example of a pair of compounds that does not see an elution order shift.
Figure 3: 3,4,5-trichlorophenol (left) and 3,4,5-trichlorophenyl acetate (right) are an example of a pair of compounds with the substitution pattern that sees an elution order shift.
Figure 4: (From left to right) 3,4-dichlorophenyl acetate, 2,4,-dichlorophenyl acetate, and 3,5-dichlorophenyl acetate. The orange line divides the molecules into meta/para substitution and ortho-substition zones. Disubstution that is limited to the m/p zone results in an elution order change.
Table 2: All 41 phenolic compounds determined using the NF EN 12673 derivatization method. The compounds in red indicate the five compounds which undergo the discussed change in elution order. The compounds in green are the compounds which exhibit changes in co-elution compared to non-derivatized compounds.
It was a great meeting in a beautiful location. It was a pleasure to catch up with old friends and make some new ones.
Please let me know if you have any questions or ideas!
That’s right…NO CLEANUP. This is an update of a project we did a couple of years ago. We tested Yerba Mate tea for polycyclic aromatic hydrocarbons (PAHs). It is an interesting story why we were testing tea for PAHs. In our original work we used a SPE sample cleanup so that the sample was clean enough to provide quality quantitative analysis with GC-TOFMS and GC-MS/MS. At that time, we were using a standard hot splitless injection. Since then, we have shown that the use of a split injection (a.k.a Shoot-and-Dilute) increases ruggedness even when a high amount of co-extracted chlorophyll is injected. In this poster, we present ruggedness data and quantitative data for incurred PAHs with and WITHOUT sample cleanup.
GC inlet liner wool is still a hotly debated subject for pesticide residue chemists. My colleague Jack Cochran did a nice experiment to investigate pesticide response (60 injections) in a strawberry QuEChERS extract for a liner with no wool, a liner with quartz wool and a liner with borosilicate wool. Take a look at the poster for details on the sheep’s conclusions.
This presentation compares these two strategies (analyte protectants and split injection) to help prolong GC-MS/MS performance for pesticides analysis in food matrices. We evaluated samples by both split and splitless injection and each of these with and without analyte protectants. This allowed us to draw some distinction between the benefits of each. For example, analyte protectants has a greater impact in splitless injection compared to split injection. Split injection proved more rugged with less matrix on column and reaching the mass spec ion source. For data and conclusions, take a look at the poster.
This poster describes some of the first work we did investigating split injection with GC-MS/MS for a large number of compounds…over 200 pesticides covering multiple classes. We answered two fundamental questions: Is detectability good enough when using split injection to meet the common 10 ppb target?…and Does split injection allow the GC-MS/MS system to stay up longer? Find the answers in the poster!
I have also added my vendor seminar here. It is a good review of all of the benefits associated with Shoot-and-Dilute GC (split injection)…better ruggedness, faster analysis, better peak shape just to name a few. Each is demonstrated with a snippet of data from larger projects. New data is presented that evaluates if split injection mitigates matrix effects enough to allow the use of solvent only calibration or solvent with analyte protectants calibration instead of matrix-matched calibration. The results look promising and we are continuing to explore this in more detail.
After a long blog hiatus, I’d like to address one of the most frequently-asked questions we get from cannabis labs: “how do we quantitatively extract cannabinoids from edible matrices?”. With the huge number of matrices out there, limited peer-reviewed data, and lack of sufficiently concentrated reference standards for spiking, it’s no wonder that after several years, this is still a question in the cannabis analytical industry.
In February, my colleague, Julie Kowalski and I were lucky enough to spend two weeks at Trace Analytics developing methods for cannabis analysis. One of my focus areas during that time was to evaluate extraction efficiency for cannabinoids in edible matrices using a common sample preparation technique – QuEChERS. The QuEChERS approach is one of the simplest and most cost-effective sample preparation techniques available. It is used heavily in food safety labs for extraction of pesticides in food matrices, and has been applied across many other industries and matrices. In a nutshell, QuEChERS is simply a liquid-liquid extraction (LLE). The special thing about QuEChERS is that unlike most LLEs, the organic extraction solvent can be miscible with water because it is salted out during the QuEChERS extraction process. This improves extraction efficiency for water-soluble analytes and generally produces a cleaner extract than solvent extraction alone. Figure 1 shows the procedure we used for extraction of our edible matrices – in this case a caramel cookie:
Figure 1: Extraction Procedure for Cannabis Edibles
At the end of this process, we end up with four layers: an extract in acetonitrile that also contains some matrix components like fat, sugar, and pigment, insoluble matrix components like fiber and protein, water containing the bulk of the sugar and certain pigments, and excess salt from the extraction. Figure 2 points out the different parts of our QuEChERS extract:
Figure 2: QuEChERS Extract of Cannabis-Infused Caramel Cookie
After extraction, a cleanup step is commonly performed to further remove matrix interferences, but based on data that I don’t have room to show here, the common cleanup procedures result in loss of cannabinoid acids and CBN. You can find a detailed pdf version of the full procedure here (I included a plant extraction method in the document as well).
Now we have an extraction procedure, but how do we know if it works? Ideally, we’d like our extraction procedure to extract 100% of our analytes of interest. This is especially important for cannabis-infused edibles because if our extraction method only recovers a portion of cannabinoids, then we’ll have to determine what portion is recovered for each and every matrix that we test in order to ensure the validity of our results. Generally, when developing an extraction method, labs can measure recovery by spiking applicable amounts of a reference standard into a blank matrix and measuring how much they get back. Unfortunately, we don’t have that luxury for cannabis potency analysis. We can definitely get blank edible matrix, but due to the low concentration of the DEA exempt cannabinoid standards we must use, we are unable to spike the matrix at a representative level. For example, in order to spike 1g of edible matrix at 1mg THC/g, we would need to add 1mL of the most concentrated DEA-exempt THC standard available to that gram of brownie. By adding 1mL of our standard in organic solvent to 1g of brownie, we’ve effectively diluted our matrix by 50%. If we try to extract this, any recovery values we get won’t be representative of extraction from a ‘real’ sample. If we try to spike with additional cannabinoids, the situation only gets worse.
So how do we measure recovery for our extraction procedure without performing any spiking? Luckily, this is a common issue in the botanicals industry, and AOAC has guidance addressing this issue. The guidance suggests re-extraction of the same sample with fresh solvent. In order to do this, perform the following steps:
- After extraction, remove all of the solvent extract from the sample (i.e. all solvent for solvent extracts, and entire top layer for QuEChERS extracts).
- Add fresh solvent to the extracted sample as dictated by your method (if very few salts remain at the bottom of the tube for QuEChERS extractions, add ½ package of fresh salts – it will be messy).
- Extract sample according to your method.
If we removed all of the cannabinoids from our matrix during the first extraction step, we should see little to no cannabinoids in our re-extraction sample after performing the second extraction as detailed above. That’s exactly what we did for our work. We performed a re-extraction experiment on a variety of cannabis edible matrices covering sugar only, sugar + fat, and fat only samples. Figure 3 shows the results from our re-extraction experiment.
Figure 3: Re-extraction Experiment Results for Edible Matrices (mg/g)
Everything looked great, except for the butter matrix. It turned out that the true cannabinoid concentration in the butter was ~10mg/g, whereas in the rest of the matrices, the concentration was ~1mg/g. My hypothesis is that if we reduce the matrix amount for the butter to contain ~1mg total cannabinoids (i.e. use only 100mg of butter), the recovery will improve since the matrix:extraction solvent ratio will be much greater.
Stay tuned for my future blogs which will cover cleanups (or lack thereof) for edible matrices, derivatization for GC potency analysis, and multiple topics on residual solvents.
 AOAC International. AOAC Official Methods of Analysis: Appendix K. 2013. p.5