By Dr. Allison Justice, Vice President of Cultivation at Outco
This presentation discusses:
Prized French wines are aged for years in oak barrels, as are famous whiskies. Tobacco is air-, fire-, flue- or sun-cured. Cannabis, however, is quickly dried and stored in a plastic bucket. Although many cannabis growers have proprietary ways of making flower flavorful and aromatic, little to no research is available for consistency.
Anecdotal examples show that chemical makeup is not only dictated by the strain/cultivar, but also influenced by grow methods, drying and curing. The lack of data prompted us to research what is happening during these processes. In this session, we will present our research at OutCo of how to affect and control the chemical makeup of flower; new protocols to monitor the dry and cure of cannabis flowers so we are able to modulate the terpene and cannabinoid profiles in our strain offering; and our latest findings in this exciting field of post-harvest cannabis research.
Cannabis-testing laboratories have the challenge of removing a variety of unwanted matrix components from plant material prior to running extracts on their LC-MS/MS or GC-MS. The complexity of the cannabis plant presents additional analytical challenges that do not need to be accounted for in other agricultural products. Up to a third of the overall mass of cannabis seed, half of usable flower and nearly all extracts can be contributed to essential oils such as terpenes, flavonoids and actual cannabinoid content1. The biodiversity of this plant is exhibited in the over 2,000 unique strains that have been identified, each with their own pigmentation, cannabinoid profile and overall suggested medicinal use2. While novel methods have been developed for the removal of chlorophyll, few, if any, sample preparation methods have been devoted to removal of other colored pigments from cannabis.
Cannabis samples from four strains of plant (Purple Drink, Tahoe OG, Grand Daddy and Agent Orange) were hydrated using deionized water. Following the addition of 10 mL acetonitrile, samples were homogenized using a SPEX Geno/Grinder and stainless steel grinding balls. QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) non-buffered extraction salts were then added and samples were shaken. Following centrifugation, an aliquot of the supernatant was transferred to various blends of dispersive SPE (dSPE) salts packed into centrifugation tubes. All dSPE tubes were vortexed prior to being centrifuged. Resulting supernatant was transferred to clear auto sampler vials for visual analysis. Recoveries of 48 pesticides and four mycotoxins were determined for the two dSPE blends that provided the most pigmentation removal.
Seven dSPE blends were evaluated for their ability to remove both chlorophyll and purple pigmentation from cannabis plant material:
Based on the coloration of the resulting extracts, blends A, F and G were determined to be the most effective in removing both chlorophyll (all cannabis strains) and purple pigments (Purple Drink and Grand Daddy). Previous research regarding the ability of large quantities of GCB to retain planar pesticides allowed for the exclusion of blend G from further analyte quantitation3. The recoveries of the 48 selected pesticides and four mycotoxins for blends A and F were determined.
A blend of MgSO4, C18, PSA and Chlorofiltr® allowed for the most sample clean up, without loss of pesticides and mycotoxins, for all cannabis samples tested. Average recovery of the 47 pesticides and five mycotoxins using the selected dSPE blend was 75.6% were as the average recovery when including GCB instead of Chlorofiltr® was 67.6%. Regardless of the sample’s original pigmentation, this blend successfully removed both chlorophyll and purple hues from all strains tested. The other six dSPE blends evaluated were unable to provide the sample clean up needed or had previously demonstrated to be detrimental to the recovery of pesticides routinely analyzed in cannabis.
(1) Recommended methods for the identification and analysis of cannabis and cannabis products, United Nations Office of Drugs and Crime (2009)
(2) W. Ross, Newsweek, (2016)
(3) Koesukwiwat, Urairat, et al. “High Throughput Analysis of 150 Pesticides in Fruits and Vegetables Using QuEChERS and Low-Pressure Gas Chromatography Time-of-Flight Mass Spectrometry.” Journal of Chromatography A, vol. 1217, no. 43, 2010, pp. 6692–6703., doi:10.1016/j.chroma.2010.05.012.
With the state led legalization of both adult recreational and medical cannabis, there is a need for comprehensive and reliable analytical testing to ensure consumer safety and drug potency. Cannabis-testing laboratories receive high volumes of test requests from cannabis cultivators for testing quantitative and qualitative aspects of the plant. The testing market is growing as more states bring in stricter enforcement policies on testing. As the number of testing labs grow, it is anticipated that the laboratories that are now servicing other markets, including high throughput contract labs, will cross into cannabis testing as regulations free up. As the volume of tests each lab performs increases, the need for laboratories to make effective use of time and resource management, such as ensuring accurate and quick results, reports, regulatory compliance, quality assurance and many other aspects of data management becomes vital in staying competitive.
Cannabis Testing Workflows
To be commercially competitive, testing labs offer a comprehensive range of testing services. These services are available for both the medical and recreational cannabis markets, including:
Detection and quantification of both acid and neutral forms of cannabinoids
Screening for pesticide levels
Monitoring water activity to indicate the possibility of microbiological contamination
Moisture content measurements
Residual solvents and heavy metal testing
Fungi, molds, mycotoxin testing and many more
Although the testing workflows differ for each test, here is a basic overview of the operations carried out in a cannabis-testing lab:
Cannabis samples are received.
The samples are processed using techniques such as grinding and homogenization. This may be followed by extraction, filtration and evaporation.
A few samples will be isolated and concentrated by dissolving in solvents, while others may be derivatized using HPLC or GC reagents
The processed samples are then subjected to chromatographic separation using techniques such as HPLC, UHPLC, GC and GC-MS.
The separated components are then analyzed and identified for qualitative and quantitative analysis based on specialized standards and certified reference materials.
The quantified analytical data will be exported from the instruments and compiled with the corresponding sample data.
The test results are organized and reviewed by the lab personnel.
The finalized test results are reported in a compliant format and released to the client.
In order to ensure that cannabis testing laboratories function reliably, they are obliged to follow and execute certain organizational and regulatory protocols throughout the testing process. These involve critical factors that determine the accuracy of testing services of a laboratory.
Factors Critical to a Cannabis Testing Laboratory
Accreditations & Regulatory Compliance: Cannabis testing laboratories are subject to regulatory compliance requirements, accreditation standards, laboratory practices and policies at the state level. A standard that most cannabis testing labs comply to is ISO 17025, which sets the requirements of quality standards in testing laboratories. Accreditation to this standard represents the determination of competence by an independent third party referred to as the “Accreditation Body”. Accreditation ensures that laboratories are adhering to their methods. These testing facilities have mandatory participation in proficiency tests regularly in order to maintain accreditation.
Quality Assurance, Standards & Proficiency Testing: Quality assurance is in part achieved by implementing standard test methods that have been thoroughly validated. When standard methods are not available, the laboratory must validate their own methods. In addition to using valid and appropriate methods, accredited laboratories are also required to participate in appropriate and commercially available Proficiency Test Program or Inter-Laboratory Comparison Study. Both PT and ILC Programs provide laboratories with some measure of their analytic performance and compare that performance with other participating laboratories.
Real-time Collaboration: Testing facilities generate metadata such as data derived from cannabis samples and infused products. The testing status and test results are best served for compliance and accessibility when integrated and stored on a centralized platform. This helps in timely data sharing and facilitates informed decision making, effective cooperation and relationships between cannabis testing facilities and growers. This platform is imperative for laboratories that have grown to high volume throughput where opportunities for errors exist. By matching test results to samples, this platform ensures consistent sample tracking and traceability. Finally, the platform is designed to provide immediate, real-time reporting to individual state or other regulatory bodies.
Personnel Management: Skilled scientific staff in cannabis-testing laboratories are required to oversee testing activities. Staff should have experience in analytical chromatography instruments such as HPLC and GC-MS. Since samples are often used for multi-analytes such as terpenes, cannabinoids, pesticides etc., the process often involves transferring samples and tests from one person to another within the testing facility. A chain of custody (CoC) is required to ensure traceability and ‘ownership’ for each person involved in the workflow.
LIMS for Laboratory Automation
Gathering, organizing and controlling laboratory-testing data can be time-consuming, labor-intensive and challenging for cannabis testing laboratories. Using spreadsheets and paper methods for this purpose is error-prone, makes data retrieval difficult and does not allow laboratories to easily adhere to regulatory guidelines. Manual systems are cumbersome, costly and lack efficiency. One way to meet this challenge is to switch to automated solutions that eliminate many of the mundane tasks that utilize valuable human resources.. Laboratory automation transforms the data management processes and as a result, improves the quality of services and provides faster turnaround time with significant cost savings. Automating the data management protocol will improve the quality of accountability, improve technical efficiency, and improve fiscal resources.
A Laboratory Information Management System (LIMS) is a software tool for testing labs that aids efficient data management. A LIMS organizes, manages and communicates all laboratory test data and related information, such as sample and associated metadata, tests, Standard Operating Procedures (SOPs), test reports, and invoices. It also enables fully automated data exchange between instruments such as HPLCs, GC-FIDs, etc. to one consolidated location, thereby reducing transcription errors.
How LIMS Helps Cannabis Testing Labs
LIMS are much more capable than spreadsheets and paper-based tools for streamlining the analytical and operational lab activities and enhances the productivity and quality by eliminating manual data entry. Cloud-enabled LIMS systems such as CloudLIMS are often low in the total cost of acquisition, do not require IT staff and are scalable to help meet the ever changing business and regulatory compliance needs. Some of the key benefits of LIMS for automating a cannabis-testing laboratory are illustrated below [Table 1]:
Barcode label designing and printing
Enables proper labelling of samples and inventory
Follows GLP guidelines
Instant data capture by scanning barcodes
Facilitates quick client registration and sample access
3600 data traceability
Saves time and resources for locating samples and other records
Inventory and order management
Supports proactive planning/budgeting and real time accuracy
Promotes overall laboratory organization by assigning custodians for samples and tests
Maintains the Chain-of-custody (CoC)
Accommodates pre-loaded test protocols to quickly assign tests for incoming samples
Accounting for sample and inventory quantity
Automatically deducts sample and inventory quantities when consumed in tests
Package & shipment management
Manages incoming samples and samples that have been subcontracted to other laboratories
Electronic data import
Electronically imports test results and metadata from integrated instruments
Eliminates manual typographical errors
Generates accurate, customizable, meaningful and test reports for clients
Allows user to include signatures and additional sections for professional use
21 CFR Part 11 compliant
Authenticates laboratory activities with electronic signatures
ISO 17025 accreditation
Provides traceable documentary evidence required to achieve ISO 17025 accreditation
Audit trail capabilities
Adheres to regulatory standards by recording comprehensive audit logs for laboratory activities along with the date and time stamp
Centralized data management
Stores all the data in a single, secure database facilitating quick data retrieval
Promotes better data management and resource allocation
Enables modification of screens using graphical configuration tools to mirror testing workflows
State compliance systems
Integrates with state-required compliance reporting systems and communicates using API
Adheres to regulatory compliance
Creates Certificates of Analysis (CoA) to prove regulatory compliance for each batch as well as batch-by-batch variance analysis and other reports as needed.
Data security & confidentiality
Masks sensitive data from unauthorized user access
Cloud-based LIMS encrypts data at rest and in-transit while transmission between the client and the server
Cloud-based LIMS provides real-time access to laboratory data from anytime anywhere
Cloud-based LIMS enhances real-time communication within a laboratory, between a laboratory and its clients, and across a global organization with multiple sites
Table 1. Key functionality and benefits of LIMS for cannabis testing laboratories
Upon mapping the present day challenges faced by cannabis testing laboratories, adopting laboratory automation solutions becomes imperative. Cloud-based LIMS becomes a valuable tool for laboratory data management in cannabis testing laboratories. In addition to reducing manual workloads, and efficient resource management, it helps labs focus on productive lab operations while achieving compliance and regulatory goals with ease.
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:
correlation coefficient (r) or coefficient of determination (r2)
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).
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.
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 firstname.lastname@example.org.
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