Enhancing drying efficiency and terpene retention of cannabis using cold plasma pretreatment
Bibliographic record
Abstract
Hang-drying of cannabis at room conditions is a slow process and leads to the risk of microbial growth. This method can sometimes prevent cannabis from reaching the equilibrium moisture content (EMC) below the safe storage threshold. On the other hand, high-temperature drying techniques are faster but negatively impact the secondary metabolites. Cold plasma (CP) is a novel technique explored in this study to treat cannabis at various operational conditions of plasma jet (power: 300, 350, and 400 W, time: 20, 30, and 40 s) prior to drying at environmental conditions of 25°C and 50 % RH. The findings revealed that untreated cannabis samples reached an equilibrium moisture content (EMC) of approximately 16 % in 1260 min. In contrast, CP-pretreated samples achieved lower EMCs of 10–14 % within 690–840 min. CP pretreatment also resulted in high moisture diffusivity, lower energy consumption, and higher energy efficiency. Increasing CP power and residence time accelerated the decarboxylation of cannabinoids, leading to the formation of more tetrahydrocannabinol (THC) and less tetrahydrocannabinolic acid (THCA), without significantly affecting the total THC (27.45 % untreated vs. 25.82 % - 28.36 % g/ g of dry matter in CP pretreated samples). Compared with untreated dried inflorescences, the 400 W and 30 s CP treated inflorescences resulted in the retention of 96 % of terpenes, whereas all 300 W CP treated samples retained > 90 % of terpenes. Overall, the study highlights that CP pretreatment is a promising technology for the cannabis industry in shortening the drying time and preserving the product quality, especially terpenes. • Cold plasma (CP) pretreatment on drying and quality of cannabis was investigated. • CP pretreatment reduced drying time, improved diffusivity and energy efficiency. • CP pretreatment did not significantly affect the total tetrahydrocannabinol. • Cannabis treated with 400 W CP for 30 s and dried retained 96 % terpenes of control.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".