Enhancing the Nutritional Value and Antioxidant Activity of <i>Auricularia polytricha</i> Through Efficient Utilization of Agricultural Waste
Bibliographic record
Abstract
Auricularia polytricha , a nutritious edible wood‐rotting mushroom, faces cultivation challenges due to the limited availability of wood chips. It is urgent to find suitable flat substitutes to replace the current material. This study explores the use of 12 types of agricultural waste as alternative growth substrates, analyzing their effects on the physiological and biochemical characteristics of both mycelia and fruiting bodies. The agricultural waste that demonstrated greater suitability for the growth of A. polytricha was then selected as a substrate to evaluate its effect on the nutritional composition and antioxidant capacity of the fruiting bodies. The research findings have highlighted the potential for cotton straw, coix seed straw, and wheat straw to serve as the most efficient substrates in the cultivation of A. polytricha . The utilization of agricultural waste as a growth medium has been found to markedly enhance the activity of enzymes such as laccase, cellulase, and polyphenol oxidase within the mycelia, resulting in a significant reduction of the cultivation cycle by 16 days. These substrates also improved the nutritional composition of fruiting bodies, increasing crude fat, crude protein, total sugars, and mineral contents of iron (Fe) and zinc (Zn) in the fruiting bodies, with increases of 1.6‐fold, 2.6‐fold, 2.2‐fold, fourfold, and sevenfold, respectively. Additionally, the in vitro antioxidant activity of A. polytricha was assessed, revealing an enhancement in the DPPH free radical scavenging ability by up to 36.06%. This study highlights the utilization of agricultural waste to enhance the nutrient profile of A . polytricha , providing innovative approaches for optimizing its production. Additionally, it offers significant insights into advancing technologies related to “transforming wood‐rotting mushrooms into agents for straw degradation.”
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".