Quantifying hericenone and erinacine content in Hericium erinaceus fruiting bodies cultivated on Vancouver Island douglas-fir sawdust substrates
Bibliographic record
Abstract
Lion's mane mushroom (Hericium erinaceus) has attracted broad attention for its nerve growth factor-stimulating compounds, but the influence of regional substrate chemistry on bioactive terpenoid yields is poorly understood. This research quantified hericenone and erinacine concentrations in H. erinaceus fruiting bodies cultivated on Douglas-fir (Pseudotsuga menziesii) sawdust substrates prepared at seven carbon-to-nitrogen ratios on Vancouver Island, British Columbia, Canada. Fruiting bodies were extracted with ethanol, fractionated by column chromatography, and analysed by HPLC-UV and LC-MS/MS. Four target compounds were identified: hericenone C, hericenone D, erinacine A, and erinacine C. A substrate C:N ratio near 340:1 maximised both hericenone (8.7 mg/g dry weight) and erinacine (6.1 mg/g) accumulation, following a bell-shaped dose-response curve. Higher nitrogen supplementation (C:N below 220) suppressed terpenoid biosynthesis by favouring vegetative mycelial growth over secondary metabolism. The Douglas-fir substrate outperformed a reference alder-sawdust control by 18 to 23% for total hericenone content, possibly due to its higher resin acid and lignin composition. These results provide the first substrate-optimisation data specific to Pacific Northwest forestry residues and offer practical guidance for mycopharmacological producers seeking to maximise neuroactive compound yields.
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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 | 0.000 | 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".