Effect of Some Technological Factors of Extraction on Total Lentinan Content in Sapa Shiitake Mushroom Extract
Bibliographic record
Abstract
In Vietnam, the source of shiitake mushroom is very abundant and grown in many provinces/cities, the total yield reaches hundreds of thousands of tons/per year, mainly serving the demand of domestic food processing, and did not develop into medicinal mushrooms yet. Among them, Sapa shiitake mushroom is being widely cultivated in Sapa-Vietnam, and is a raw material with great potential for lentinan exploitation. Until now, researches on extracting and obtaining lentinan in Vietnam are still limited. Those are reasons to carry out this research. The effects of solvent in combination with assistance of ultrasound wave in lentinan extracting capability in Sapa shiitake mushroom were studied. Before carrying out extracting of lentinan; The dried fruit bodies of Sapa shiitake mushroom was crushed in to 1mm. Five extracting parameters include concentration of Na0H solvent (%), proportion of Na0H solvent and raw material (v:w), extracting temperature, ultrasound time and ultrasound intensity were carried out. An extraction without the use of ultrasound for 180 min was control sample. Total lentinan content was obtained during the extracting process. The results indicated that shiitake mushrooms were extracted by using 0.35% Na0H solvent, the rate of Na0H solvent and mushrooms was 12:1 (v:w), intensity of ultrasound was 58 W/cm2, frequency was 20 kHz, extracting temperature was 65 °C, time of extraction was 6 min gave total lentinan content 1.63 times higher than the control.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".