Influence of Substrate Composition on Beta-Glucans Production and Growth of Ganoderma lucidum
Bibliographic record
Abstract
Ganoderma lucidum is a medicinal mushroom known and used for centuries in China, claimed as beneficial for health due to the immunological effects provided by (1-3)Beta, (1-6)Beta-glucans present in its cell wall. Agricultural residues can be used as substrate for solid-state fermentation and turned into a product rich in Beta-glucans, that can be used for animal feeding, enhancing the immune response and, thus, reducing the utilization of antibiotics and other drugs. Therefore, colonization rate (growth), yield and concentration of (1-3)Beta, (1-6)Beta-glucans of different agricultural residues, such as soybean hulls, soybean residue and corn residue after solid state fermentation with G. lucidum were determined and evaluated according to their composition before fermentation. Specific growth rate (k) was higher for soybean hulls (k1 = 0.165) and corn residue (k3 = 0.161), but concentration of (1-3)Beta, (1-6)Beta-glucans was higher in soybean residue (234.09 mg g-1) and soybean hulls (180.32 mg g-1). Considering the nutritional composition of substrates, the concentration of (1-3)Beta, (1-6)Beta-glucan can be related to the ratio between fiber carbohydrates and total carbohydrates, demonstrating that fiber is an important feature regarding the production of Beta-glucans by the fungus. Also, colonization rate can be related to the total carbohydrates concentration and total carbohydrates/crude protein ratio, showing that carbohydrates and proteins have an important effect over the growth of the fungus. Soybean hulls showed to be the most feasible substrate for G. lucidum mycelia production presenting high concentration of (1-3)Beta, (1-6)Beta-glucans and colonization rate, with potential to be a dietary supplement for farm animals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".