Effects of ultrasonic pretreatments on quality, energy consumption and sterilization of barley grass in freeze drying
Bibliographic record
Abstract
Barley grass is a plant resource for rehabilitation therapy. Its processing requires retaining nutrition well for rehabilitation cure of consumers. To meet the aim as well as low energy consumption and microbiological safety of products, ultrasonic treatments (UT) were applied to bathing materials at different power levels (10, 30, 45, 60 W/L) for 10 mins. After treatments, the bathed barley grass (100 g) was freeze-dried under vacuum −0.09 MPa with fixed power of 2 W/g. Parameters of color, microbial colony, energy consumption, glass transition temperature, moisture content, water activity, taste substances, contents of flavonoid and chlorophyll were determined after drying. In contrast with no treatment case, UT (45 W/L) decreased drying time by 14% and decreased energy consumption by 19%; UT (60 W/L) decreased total microbial colonies by 33%. Also, UT (30 W/L) yielded contents of flavonoid (9.2/kg) and chlorophyll (10.5 g/kg) of dried sample; UT power (10 W/L) yielded the highest L∗(51.5) and the lowest a∗(−9.3) value. Simultaneously, UT leads to a higher glass transition temperature (Tg), lower water activity and produces less sourness and bitterness of dried products. Ultra-sonication is an alternative to improve quality, flavor and energy consumption of barley grass in freeze drying.
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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) | 0.001 | 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".