Highly concentrated cannabis products have seen rapid growth, as customers become more accustomed to cannabis consumption and actively seek out high-potency products. Cannabis concentrates come in various forms and product names, from Badder, Budder and Crumble to Distillate, Oil and Shatter. Those products can have THC concentrations are high as 95%, compared with less than 25% common in cannabis flower.
While the actual THC concentration can vary between 70 and 95%, what is common for all cannabis concentrates is that the total of identified compounds seldomly goes above 95% of product weight.
Delic Labs is a research venture that seeks to add fundamental scientific insight to the field of cannabis and mushroom production. In this regard, we set out to identify those compounds in the missing mass balance for cannabis concentrates. This presentation will show our latest advances in characterizing and quantifying impurities in cannabis concentrates. For example, we found reduced cannabinoid species in CBN products, THC isomers in distillates and oxidation products in CBD formulations.
Out of the Shadows: Identifying Impurities in Cannabis Products
1. Out of the shadows:
Hunting for common impurities in
cannabis products
Dr. Eric Janusson, Dr. Markus Roggen
2. Introduction
DELIC Labs is a research venture that seeks to add fundamental scientific
insight to the field of cannabis and mushroom production.
We seek to support the cannabis and mushroom industries by
establishing a centralized hub in Vancouver, BC, for collaborative
research focused on:
• Process Design
• Process Optimization
• Process Analytics
• Formulation Research
3. Collaborative Research
DELIC Labs collaborates with academic, industry and private groups
around the globe. Some highlights of those collaborations are:
• University of British Columbia, Vancouver
• Loyalist College, Belleville
• Via Innovations by Dr. Monica Vialpando
• Veridient Science by Dr. Linda Klumpers
Fundamental Collaboration
4. Research Topics
• Chemometrics and data analytics for process control and optimization
• Kinetic studies to understand mechanisms
• In-process analytics for process control
• Computational studies to understand mechanisms
• Process development, like crystallization
Fundamental Cannabis and Mushroom Chemistry
5. State of the Art
Quantitative analytical methods
for cannabinoids exist
• Industry still growing and
requires development
Challenges:
• Sample matrix complexity
• Mathematical and method
errors
• Unknown byproducts and
contamination
5
8. 8
• CBN is a sought-after cannabinoid
• Produced by oxidation of THC
• Major Methods:
• I2-Oxidation
• Pd-Oxidation
• Forgotten stash
• HHC (reduced THC) is the new kid on the block
CBN Production Byproducts
I2: 10.1021/acs.jnatprod.7b00946
10. 10
• Two major byproducts have identical mass of 316.2 Da
When THC Oxidation goes the Wrong Way
11. 11
• Isolate the twin peaks at 9.5 min
• Hexane/Ethyl Acetate in 20/1 ratio
• Classic silica gel column
What are those Two New Peaks
12. 12
• Important peaks for THC and CBN to remember
Reference NMR for Identification
wide spectrum of cannabinoids naturally occurring in
noids refers to terpeno-phenolic C21 and C22 compounds
hemp plant. They are predominantly formed in the
p plant [6,7]. The occurrence of a carboxyl group allows
ses: cannabinoid acids featuring a carboxyl group (e.g.,
HCA) and cannabidiolic acid (CBDA)) and the neutral
rent cannabinoids and their carboxylic acid analogs and
in the literature [8]. The individual cannabinoids differ
The modifications are mainly limited to changes of the
ydrocannabivarin (THCV), in Figure 1), the substitution
oup, or an additional cyclization [9].
ing so-called full spectrum hemp extracts is not CBD-se-
wide spectrum of cannabinoids naturally occurring in the
nnabinoids refers to terpeno-phenolic C21 and C22 com-
nd in the hemp plant. They are predominantly formed in
le hemp plant [6,7]. The occurrence of a carboxyl group
two subclasses: cannabinoid acids featuring a carboxyl
inolic acid (THCA) and cannabidiolic acid (CBDA)) and
than 120 different cannabinoids and their carboxylic acid
s are described in the literature [8]. The individual canna-
heir structures. The modifications are mainly limited to
ain (e.g., ∆9-tetrahydrocannabivarin (THCV), in Figure 1),
acid or hydroxyl group, or an additional cyclization [9].
d in this work including the applied numbering system. (a)
); (b) R = H: ∆9-tetrahydrocannabinol (∆9-THC), R = COOH:
nabinol (∆8-THC); (d) cannabinol (CBN); (e) ∆9-tetrahydro-
in this work including the applied numbering system.
CBDA); (b) R = H: D9-tetrahydrocannabinol (D9-THC),
etrahydrocannabinol (D8-THC); (d) cannabinol (CBN);
Toxics 2021, 9, 136 10 of 20
Toxics 2021, 9, x 10 of 20
Figure 2. 1H NMR spectra of cannabinoids CBD, CBDA, CBN, CBG, ∆9-THC, THCA, ∆8-THC, THCV dissolved in CDCl3.
* Cannabinoid stabilized as N,N-dicyclohexylammonium salt.
Figure 2. 1H NMR spectra of cannabinoids CBD, CBDA, CBN, CBG, D9-THC, THCA, D8-THC, THCV dissolved in CDCl3.
* Cannabinoid stabilized as N,N-dicyclohexylammonium salt.
10.3390/toxics9060136
OH
O
1
2
4
5 6
7
9
10
3’
5’ 1’’
2’’
3’’
4’’
5’’
13. 13
• Only two protons in the aromatic region
• No signal for double bond
• Pair of broad doublets in a 2 to 1 ratio near H-1 of THC
1H-NMR of both Compounds!
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
f1 (ppm)
rd-03.1.fid
ubc_1H CDCl3 {C:Brukertopspin2.1} gsammis 40
6.26
2.47
5.93
7.31
3.83
6.12
1.62
3.05
0.31
0.74
0.90
1.00
0.99
0.87
0.89
0.91
0.94
0.96
1.08
1.13
1.15
1.29
1.30
1.31
1.38
1.53
1.56
1.61
1.65
1.83
1.87
2.88
2.92
3.04
3.08
6.08
6.26
19. 19
MSMS Pattern-Database Similarity
MSMS annotation requires similarity metric:
1. Query database with experimental
fragmentation pattern
2. Compare spectral features
3. Measure similarity. Do they match?
Modified cosine score considers m/z deltas
(shift mass)
• Compensation for functionalization and
ESI adducts
Modified Cosine Scores for MSMS Library
20. 20
“Intelligent” Database Comparisons
• Annotation using a
heuristic machine learning
algorithm
• Spectral patterns treated
like words
• Contextual comparisons
(semantic similarity)
• Computationally
scalable
F. Huber, et. al., PLoS Comput. Biol., 2021
‘Intelligent’
contaminant
filtration
21. 21
Molecular Networking
Jeramie Watrous, Pieter C. Dorrestein, et al. PNAS 2012
• Experimental and
reference MSMS
spectra
compared to
each other
• Spectral similarity
can be
determined
between samples
22. 22
Beneath the Surface
What about the
uncommon
molecules?
Terpenes Cannabinoids
Entourage
Effect
Munchies
Flavonoids
Thiols
Dimeric
cannabinoids
Sugars
Metals
Molecular network of structurally similar
compounds in Cannabis Sativa extracts
25. 25
Sample preparation is challenging
• Matrix interference
• Inter-cultivar variation
• Obtaining a representative dataset
• What is an ‘average’ sample?
What can we do?
• Cryogenic flower ‘embrittling’
• Spike workflow stages
• Instrument optimization
Next Steps for
Cannabis Analysis
A network of suspected and known contaminants in Cannabis Sativa
extract
PULVERISETTE 0
Vibratory Cryomill
