Impact of pre-freezing and microwaves on drying behavior and terpenes in hops (Humulus lupulus)
Bibliographic record
Abstract
Hop buds (Humulus lupulus) are paramount to beer bittering, flavoring, and microbiological stability. To optimize post-harvest processing, fresh and pre-frozen hops were subjected to freeze-drying, hot air and microwave-assisted hot air drying. Pre-freezing occurred at − 80°C, prior to drying at 35°C, 50°C, and 65°C, with different microwave power (0 W, 100 W and 200 W, where 0 W represented conventional hot air drying). Results show that hops drying kinetics can be described using the predictive Page and Logarithmic mathematical models. Obtained R2, SSE, and RMSE values ranged between 0.999 and 0.982, 0.035–0.001, and 0.058–0.004, respectively. Irrespective of the drying condition, pre-freezing reduced drying time by 0.17–85.9 %. Pre-freezing hop buds increases the effective moisture diffusion coefficient, and it increases with higher drying temperature and microwave power, ranging between 5.91 × 10−10 m2 s−1 and 2.43 × 10−7 m2 s−1. SEM analyses indicate that pre-freezing causes structural damage to lupulin glands. The average concentration of myrcene, limonene, caryophyllene, and humulene for fresh hops were 15.08 mg g−1, 0.27 mg g−1, 3.09 mg g−1, and 6.52 mg g−1 respectively. For the dried samples under the various conditions, the concentration ranged from 12.20 mg g−1 to 0.53 mg g−1 (myrcene), 0.26 mg g−1 to 0.12 mg g−1 (limonene), 1.49 mg g−1 to 0.31 mg g−1 (caryophyllene), and 2.69 mg g−1 to 0.52 mg g−1 (humulene). Results affirm that pre-freezing plant material prior to drying can shorten postharvest processing times, and this method can potentially be applied to other industrial crops. This study highlights the importance of controlled postharvest processing to ensure industrial crop quality.
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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) | 0.000 | 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".