Multi-objective optimization and modeling of microwave-infrared pretreatment on drying and quality characteristics of cannabis ( <i>Cannabis sativa</i> L.) using response surface methodology and artificial neural network
Bibliographic record
Abstract
Electromagnetic drying of cannabis is a fast and energy-efficient method, but prolonged exposure may impact product quality. The study aimed to explore short-time microwave-infrared (MI) pretreatment of cannabis before controlled environmental drying at 25 °C and 50% RH. Using a Box-Behnken design and response surface methodology (RSM), pretreatment time (2–5 min), infrared (75–225 W), and microwave (70–210 W) power were optimized to maximize drying rate and cannabinoid contents, with minimizing color change and energy consumption. Results showed that the drying rate, color changes and tetrahydrocannabinol (THC) of dried inflorescences increased significantly (p < 0.05), whereas the energy consumption and tetrahydrocannabinolic acid (THCA) decreased due to MI pretreatment, without affecting the total THC. The optimal parameters were determined to be 225 W infrared and 210 W microwave pretreatment for 3.36 min. Comparing to untreated cannabis drying, MI pretreatment of cannabis at optimized conditions and drying resulted in shorter drying time and lower moisture content, >65% energy savings, 43% reduction of terpenes and more porous microstructure. Artificial neural network (ANN) modeling with a 3-9-6 structure outperformed RSM in predicting the response variables. Overall, this study identified that short-time MI pretreatment improved cannabis drying efficiency and neutral cannabinoids, with ANN modeling offering accurate predictions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".