Postharvest processing of cannabis (Cannabis sativa) and valorization of its stalks
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».