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 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 | 0.000 |
| 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 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".