Modulation of agronomic and nutritional response of <i>Pleurotus eryngii</i> strains by utilizing glycine betaine enriched cotton waste
Bibliographic record
Abstract
Abstract BACKGROUND This study aimed to evaluate the possibility of cotton waste enrichment with glycine betaine (GB) for production of two strains (P9, P10) of king oyster (Pleurotus eryngii). Cotton waste was used as (100%) control (T0 = cotton waste) and augmented with various combinations of GB, (T1 = 2 mmol L−1, T2 = 4 mmol L−1, T3 = 6 mmol L−1, T4 = 8 mmol L−1 and T5 = 10 mmol L−1). The response of king oyster to GB was evaluated by earliness, yield, biological efficiency (BE), minerals (nitrogen, phosphorus, potassium, zinc (Zn), copper (Cu), magnesium (Mg), manganese (Mn), iron (Fe), sodium (Na), calcium (Ca)), total sugars, total soluble solids, reducing sugars, non‐reducing sugars, ascorbic acid, proximate (crude protein, carbohydrates, crude fibers, ash, fats) content of fruiting body and Fourier‐transform infrared (FTIR) spectroscopy analysis compared with the control substrate (cotton waste). RESULTS The earliness, yield, and BE were higher as compared to control substrate and increased with an augmentation in the concentration of GB within the cotton waste. Two strains showed (on dry weight basis) 33.9–54.9 mg g−1 nitrogen, 6.8–12.5 mg g−1 phosphorus, 16.9–25.1 mg g−1 potassium, 40.5–64.2 mg kg−1 Zn, 17.1–37.3 mg kg−1 Cu, 1174–1325 mg kg−1 Mg, 20.1–29.1 mg kg−1 Mn, 129–265 mg kg−1 Fe, 779–835 mg kg−1 Ca), 6.3%–11.3% total sugars, 7.3–14.9 °Brix total soluble solids, 2.1–7.3% reducing sugars, 10.4–18.1% crude protein, 3.6–4.4% crude fiber and 5.6–16.7 mg (100 g)‐1 on various concentration of GB enrich cotton waste. Cotton waste enriched with GB significantly affected nutritional profile of king oyster mushroom. CONCLUSION The results revealed that GB enriched cotton waste can be used as an innovative substrate to enhance the yield and quality of king oyster mushroom. © 2019 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) | 0.001 | 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) | 0.001 | 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".