Effects of Drying and Roasting to Effectively use a Discarded Part of Asparagus (Asparagus officinalis L.)
Bibliographic record
Abstract
Abstract Asparagus (Asparagus officinalis L.) has a characteristic flavor and useful components, although the lower stem is not suitable for eating because it has a fibrous skin like wood, being normally discarded. Therefore, to propose drying and roasting as methods for using the hard lower stem of asparagus, usually discarded, useful components in the asparagus stem after drying and roasting were analyzed. The rutin content was decreased significantly by drying and roasting. The ascorbic and folic acids contents were almost unchanged by drying but decreased by roasting. The fructose content was increased by drying, although glucose was almost unchanged. Both were decreased significantly by roasting. The sucrose content was increased by drying but unchanged by roasting. The increase or decrease in some free amino acids were caused after drying, and most of free amino acids disappeared after roasting. Furthermore, characteristic volatile compounds derived from the Maillard reaction were caused by drying and roasting. Most of the useful components in the hard lower stem of asparagus can be concentrated by drying, and characteristic volatile compounds be added by drying and roasting. Therefore, it is promising that the asparagus stem, an unused resource, is able to be utilized as a useful food material by drying and roasting
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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) | 0.001 | 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) | 0.001 | 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".