Flocculation and Haze Removal from Crude Beer Using In-House Produced Laccase from <i>Trametes versicolor</i> Cultured on Brewer’s Spent Grain
Bibliographic record
Abstract
The potential of brewer's spent grain (BSG), a common waste from the brewing industry, as a support-substrate for laccase production by the well-known laccase producer Trametes versicolor ATCC 20869 under solid-state fermentation conditions was assessed. An attempt was made to improve the laccase production by T. versicolor through supplementing the cultures with inducers, such as 2,2-azino bis(3-ethylbenzthiazoline-6-sulfonic acid), copper sulfate, ethanol, gallic acid, veratryl alcohol, and phenol. A higher laccase activity of 13506.2 ± 138.2 IU/gds (gram dry substrate) was obtained with a phenol concentration of 10 mg/kg substrate in a tray bioreactor after 12 days of incubation time. The flocculation properties of the laccase treated crude beer samples have been studied by using various parameters, such as viscosity, turbidity, ζ potential, total polyphenols, and total protein content. The present results indicated that laccase (25 IU/L) showed promising results as a good flocculating agent. The laccase treatment showed better flocculation capacity compared to the industrial flocculation process using stabifix as a flocculant. The laccase treatments (25 IU/L) at 4 ± 1 °C and room temperature have shown almost similar flocculation properties without much variability. The study demonstrated the potential of in-house produced laccase using brewer's spent grain for the clarification and flocculation of crude beer as a sustainable alternative to traditional flocculants, such as stabifix and bentonite.
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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 | 0.001 |
| 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".