Optimization of postharvest processing for hops (Humulus lupulus) and cannabis (Cannabis sativa)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».