Regioselective synthesis of feruloylated glycosides using the feruloyl esterases expressed in selected commercial multi-enzymatic preparations as biocatalysts
Bibliographic record
Abstract
Twenty multi-enzymatic preparations were evaluated for their levels of feruloyl esterase activity and their substrate specificity using methyl ferulate and isolated feruloylated non-digestible oligosaccharides from sugar-beet pulp and wheat bran as substrates. The efficiency of feruloyl esterases expressed in the six best multi-enzymatic preparations from Bacillus spp. (Ceremix), Humicola insolens (Depol 740), Aspergillus oryzae (Flavourzyme), Bacillus amyloliquefaciens (Multifect P 3000), Bacillus subtilis (RP-1) and Trichoderma reesei (Depol 670) for the synthesis of feruloylated glycosides was investigated using a surfactant-less organic microemulsion system as reaction medium. Using n-hexane, 1-butanol and MES (3-(N-morpholino)ethanesulfonic acid)–NaOH buffer reaction mixture (51:46:3 v/v/v), feruloyl esterases expressed in Multifect P 3000 resulted in the highest bioconversion yield of feruloylated arabinose (37%), whereas those present in Depol 670 led to the highest bioconversion yield of feruloylated galactose (61%). The highest bioconversion yield of feruloylated xylose (37%) was obtained using the feruloyl esterase of Depol 670 as biocatalyst in n-hexane, 2-butanone and MES–NaOH buffer reaction mixture (51:46:3 v/v/v). Feruloylated arabinose, galactose and xylose demonstrated potential antioxidant properties as indicated by their profound radical scavenging activity. The chemical structure of the feruloylated glycosides was confirmed by APCI-MS.
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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) | 0.001 | 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 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".