Postharvest processing of cannabis (Cannabis sativa) and valorization of its stalks
Bibliographic record
Abstract
The legalization of cannabis in Canada has facilitated the growth of its processing industry. Postharvest drying is a critical determinant of the cannabis product quality. Traditional drying methods, involving slow and manual hanging of cannabis inflorescences in controlled environments, are time-intensive and vulnerable to microbial contamination. This study explores advanced drying methods to improve efficiency and quality, compares their environmental impacts through life cycle assessment, and investigates the use of cannabis stalks for producing bio-based materials. The research is divided into five phases, covering drying optimization, quality evaluation, sustainability analysis, and waste valorization. In phase one of this study, an advanced and rapid drying technique was applied to cannabis inflorescences using a combined microwave-infrared (MI) heating system. Drying at three microwave (MW) power levels (0, 70, 140 W) with or without 70 W infrared (IR) was performed and compared with conventional controlled environmental drying (CED) at 30 °C and 60% RH. MI drying reduced drying time from 840 min to 16-200 min based on the MI power level, improved moisture diffusivity, and lowered energy consumption. It enhanced decarboxylation, increasing tetrahydrocannabinol (THC) from 6.31% to 16.65% and reducing tetrahydrocannabinolic acid (THCA). Although terpene retention was significantly lower than CED, MI drying offers a faster, energy-efficient option suitable for medicinal and edible cannabis products. To address quality loss from rapid drying, phase two explored MI heating as a short-duration pretreatment (1-5 min, 70-210 W MW, 75-225 W IR), followed by conventional drying at 25 °C and 50% RH. With the increasing of MI power and time, the drying rate and THC content were increased, while reducing THCA and energy use. Optimal conditions (210 W MW, 225 W IR, 3.36 min) achieved >65% energy savings, lower equilibrium moisture content, and a 43% terpene reduction. Artificial neural network (ANN) modeling outperformed response surface methodology (RSM) in predicting response variables. While MI pretreatment enhanced drying efficiency, terpene preservation remains a challenge. The next phase explored cold plasma (CP) pretreatment of cannabis inflorescences at 300-400 W for 20-40 s. CP-pretreated samples reached lower equilibrium moisture content (10-14%) in 690-840 min, compared to 16% in 1260 min for untreated samples. CP improved moisture diffusivity, reduced energy consumption, and enhanced decarboxylation- increasing THC levels while lowering THCA, with total THC remaining stable (25.82–28.36% vs. 27.45%). Notably, CP at 400 W for 30 s preserved around 96% of total terpene content. These results highlight CP as a promising pretreatment for reducing drying time and preserving key quality attributes, especially terpenes, in cannabis processing. A life cycle assessment (LCA) using IMPACT 2002+ was conducted to compare environmental impacts of conventional drying (CED) with MI-pretreated drying (MI-CED) and CP- pretreated drying (CP-CED) methods. Both MI-CED and CP-CED significantly reduced environmental burdens, with CP-CED cutting impacts by ~50% and MI-CED by ~72% during drying. Global warming potential from greenhouse gas emissions dropped from 11.31 kg CO₂ eq. (CED) to 5.68 kg (CP-CED) and 3.25 kg (MI-CED). These results highlight the sustainability benefits of integrating advanced pretreatment technologies in cannabis drying. In the last phase of this study, cannabis stalks were characterized and valorized into cellulose nanocrystals (CNCs) through alkali treatment, bleaching, and metal-salt oxidation. The stalks contained 57% cellulose and 0.078% THC, which decreased to 0.036% following mild NaOH (2%) pretreatment, demonstrating their suitability for conversion into biobased materials without regulatory constraints. The resulting CNCs exhibited a spindle-shaped morphology (280 nm in length and 9 nm in width) and a crystallinity of 72%, indicating their potential applications in bio-composites, adhesives, absorbents, coatings, and packaging materials. Overall, this thesis demonstrates that emerging drying strategies, particularly short-time CP pretreatment, effectively enhance cannabis drying efficiency, reduce environmental impact, and preserve product quality-particularly terpenes-making them promising alternatives to traditional methods for sustainable cannabis processing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".