Effect of solid loading on the behaviour of pectin-degrading enzymes
Bibliographic record
Abstract
BACKGROUND: Pectin plays a role in the recalcitrance of plant biomass by affecting the accessibility of other cell wall components to enzymatic degradation. Elimination of pectin consequently has a positive impact on the saccharification of pectin-rich biomass. This work thus focused on the behaviour of different pectin-degrading enzymes in the presence of low (5%) to high (35%) solid loading of lemon peel. RESULTS: High solid loading of lemon peel affected pectin solubilisation differently depending on the pectinase used. Pectin lyase was less sensitive to a reduction of water content than was a mixture of endopolygalacturonase and pectin methylesterase, regardless of whether or not the latter's mode of action is processive or not. Marked changes in water mobility were observed along with enzymatic degradation depending on the enzyme used. However, the pectin lyase resulted in less pronounced shifts in water distribution than polygalacturonase-pectin methylesterase mixtures. At similar pectin concentration, pectin solutions hindered the diffusion of hydrolases more than the solid substrate. This can be attributed to the high viscosity of the highly concentrated pectin solutions while the solid substrate may provide continuous diffusion paths through pores. CONCLUSIONS: The increase in solid substrate loading reduced the efficiency of pectin-degrading enzymes catalysing hydrolysis more significantly than those catalysing β-elimination. LF-NMR experiments highlighted the impact of solid loading on water mobility. Compared to other enzymes and whatever the solid loading, pectin lyase led to longer relaxation times linked with the most destructuration of the solid substrate. This new information could benefit the biorefinery processing of pectin-rich plant material when enzymes are used in the treatment.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".