Extraction, modification, and property characterization of dietary fiber from <i>Agrocybe cylindracea</i>
Bibliographic record
Abstract
Abstract Dietary fiber (DF) has gained a great attention owing to its potential health benefits. Agrocybe cylindracea is an edible fungus with high protein and low fat contents, which is also an enriched source of DF. However, limited study has been conducted on optimizing the conditions of A. cylindracea‐derived DF extraction and modification as well as characterizing its properties. In this study, ultrasound‐assisted enzymatic method for DF extraction was optimized as the following conditions: liquid material ratio of 29 ml/g, α‐amylase concentration of 1.50%, protamex concentration of 1.20%, and ultrasonic power of 150 W, which improved the DF extraction yield to 37.70%. Moreover, high temperature modification (HTM) and cellulase modification (CEM) were applied to modify A. cylindracea‐derived DF. The results showed that HTM had more potential capacity in converting insoluble DF into soluble DF, and DF with HTM exhibited more advantages in its physicochemical properties than DF with CEM. The DF with both HTM and CEM showed antioxidant activities, reflected by the increased reducing power as well as DPPH radical, hydroxyl radical, and ABTS+ scavenging capabilities in vitro. These findings could offer a reference for the extraction, modification, and characterizing various properties of DF from A. cylindracea, which would establish the foundation for the comprehensive application of fungi‐derived DF.
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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 | 0.001 | 0.001 |
| 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".