Effect of microwave freeze drying on quality and energy supply in drying of barley grass
Bibliographic record
Abstract
Abstract BACKGROUND Young barley grass leaves are well‐known for containing the antioxidant substances flavonoid and chlorophyll. However, low product quality and energy efficiency exist with respect to the dehydration of barley grass leaves. To improve energy supply and the quality of barley grass, microwave heating instead of contact heat was applied for the freeze drying of barley grass at a pilot scale at 1, 1.5 and 2 W g –1 , respectively; After drying, energy supply and quality parameters of color, moisture content, chlorophyll, flavonoids, odors of dried barley grass were determined to evaluate the feasibility of the study. RESULTS Microwave freeze drying (MFD) allowed a low energy supply and high contents of chlorophyll and flavonoids. A lightness value of 60.0, a green value of –11.5 and an energy supply of 0.61 kW h –1 g –1 were observed in 1.5 W g –1 MFD; whereas drying time (7 h) decreased by 42% compared to contact heating. Maximum content of flavonoid and chlorophyll was 11.7 and 12.8 g kg –1 barley grass. Microwave heating leads to an odor change larger than that for contact heating observed for the freeze drying of barley grass. CONCLUSION MFD retains chlorophyll and flavonoids, as well as colors and odors of samples, and also decreases energy consumption in the freeze drying of barley grass. © 2017 Society of Chemical Industry
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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".