Effect of phenolic compounds on bioconversion of glucose to ethanol by yeast <i>Saccharomyces cerevisiae</i> PE‐2
Bibliographic record
Abstract
Abstract The bioconversion of lignocellulosic biomasses to ethanol consists of three main steps: pre‐treatment, enzymatic hydrolysis, and fermentation by microorganisms. Most pre‐treatments induce the formation of substances with a potential inhibitory effect on the microbial metabolism, which may hinder the process. The objective of this study was to investigate the behaviour of Saccharomyces cerevisiae PE‐2 yeast in the presence of four phenolic compounds (vanillin, syringaldehyde, syringic acid, and acetosyringone) resulting from the alkaline pre‐treatment of eucalyptus with green liquor. Eucalyptus is a fast‐growing tree, widely exploited by the paper and pulp industry in Brazil. It was observed that yeast growth and glucose assimilation were affected to an extent directly proportional to the initial amount of vanillin in the medium. Syringaldehyde in a concentration of 1.0 g/L had an adverse effect on the fermentation parameters related to ethanol production, which exhibited a decrease of approximately 50 %. When syringic acid and acetosyringone were used, although the cell growth and glucose consumption profiles were similar to those of the control, they inhibited the conversion at concentrations of 0.1 and 1.0 g/L.
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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".