Optimization of postharvest processing for hops (Humulus lupulus) and cannabis (Cannabis sativa)
Bibliographic record
Abstract
Differences in cannabis (Cannabis sativa) plant chemistry between accessions are influenced by genetics, plant growth and development, and environmental conditions.Resulting secondary metabolite profiles are further altered post-harvest during storage, drying and extraction, all of which present sizable challenges to licensed producers of food-and pharmaceutical-grade products in Canada and elsewhere.This thesis focused on improving cannabis biomass drying and extraction methods suitable for scale-up in the cannabis industry.Compiling new data for this novel research field, with few studies given the new regulatory framework, will help fill the knowledge gaps.Factors affecting the drying and extraction kinetics for the different systems were evaluated and optimized to improve the quality of dried biomass and extracts.Preliminary studies were first conducted with biomass from another Cannabaceae family member, hops (Humulus lupulus), to determine the effect of post-harvest processing on drying kinetics and oil extraction.Fresh and pre-frozen hops inflorescences at -80 ° C were subjected to freeze-drying, hot air and microwave-assisted hot air drying (MAHD).The effects of drying temperature (35 ° C, 50 ° C, and 65 ° C), with different microwave power (100 W and 200 W) were evaluated.Results showed that pre-freezing caused structural damage to the lupulin glands of hops.Irrespective of the drying condition for hops, pre-freezing reduced drying time by 0.17% to 85.9% by increasing the effective moisture diffusion coefficient.Moisture diffusion coefficient increased with higher drying temperature and microwave power, ranging between 5.9 x 10 -10 m 2 s -1 and 2.4 x 10 -7 m 2 s -1 .Knowledge acquired with hops was then applied to cannabis biomass from three cannabis accessions, Qrazy Train, Qrazy Apple, and Qrazy Angel.The relationship between sample mass reduction and relative humidity during freeze-drying and the effects of pre-freezing and freezedrying temperature on cannabis drying kinetics, trichome structure, and color, in addition to cannabinoid and terpene concentrations were investigated.Cannabis samples were dried at 10 ° C, and 20 ° C, with different pre-freezing conditions (-20 ° C and -40 ° C).Data logged by the three relative humidity sensors (A, B, and C) showed that only sensor C recorded the closest to the actual changes in relative humidity during the entire drying process and can be attributed to placing the sensor near a representative cannabis bud in the center of the drying tray.Modelling studies showed that the rational regression model best explains the relationship between mass reduction and relative humidity during drying.Pre-freezing rates of 0.13 ° C min -1 -0.15 ° C min -1 were recorded for pre-freezing at -20 ° C and significantly (p < 0.05) increased by 71.2% -73.5% when Connecting textIn this review, a summary of cannabis chemistry and biosynthesis of secondary compounds is provided, and post-harvest processing practices occurring along the cannabis product value chain that might affect cannabis phytochemistry, potency, and volatility are presented.An emphasis was placed on improved drying and extraction methods for plant material suitable for the cannabis industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".