Evaluation of different lignocellulosic biomass pretreatments by phenotypic microarray-based metabolic analysis of fermenting yeast
Bibliographic record
Abstract
Advanced generation biofuel production from lignocellulosic material (LCM) was investigated. A range of different thermo-chemical pre-treatments were evaluated with different LCM. The pre-treatments included; alkaline (5% NaOH at 50°C), acid (1% H2SO4 at 121°C) and autohydrolytical methods (200°C aqueous based hydrothermal) and were evaluated using samples of miscanthus, wheat-straw and willow. The liberation of sugars, presence of inhibitory compounds, and the degree of enhancement of enzymatic saccharification was accessed. The suitability of the pre-treatment generated hydrolysates (as bioethanol feedstocks for Saccharomyces cerevisiae) was also accessed using a phenotypic microarray that measured yeast metabolic output. The use of the alkaline pre-treatment liberated more glucose and arabinose into both the pre-treatment generated hydrolysate and also the hydrolysate produced after enzymatic hydrolysis (when compared with other pre-treatments). However, hydrolysates derived from use of alkaline pre-treatments were shown to be unsuitable as a fermentation medium due to issues with colloidal stability (high viscosity). Use of acid or autohydrolytical pre-treatments liberated high concentrations of monosaccharides regardless of the LCM used and the hydrolysates had good fermentation performance with measurable yeast metabolic output. Acid pre-treated wheat straw hydrolysates were then used as a model system for larger scale fermentations to confirm both the results of the phenotypic microarray and its validity as an effective high-throughput screening tool.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".