Chlorophyllase biocatalysis in an aqueous/miscible organic solvent medium containing canola oil
Bibliographic record
Abstract
Abstract Chlorophyllase catalyzes the bioconversion of chlorophyll into chlorophyllide by replacing the phytol group with a hydrogen atom. There is an increased interest in the biotechnological application of chlorophyllase for the removal of green pigments from edible oil and its potential as an alternative to the use of the conventional bleaching technique. Partially purified chlorophyllase, obtained from the alga Phaeodactylum tricornutum, was assayed for its hydrolytic activity in an aqueous/miscible organic solvent system containing refined‐bleached‐deodorized (RBD) canola oil, using chlorophyll and pheophytin as substrate models. The results indicated that chlorophyllase biocatalysis could be successfully carried out in an aqueous/miscible organic system containing RBD canola oil. The presence of 20% RBD canola oil decreased the hydrolytic activity of chlorophyllase by 2.2 and 6.7 times, using chlorophyll and pheophytin as substrates, respectively. In addition, acetone acted as an activator of chlorophyllase activity at low concentrations and an inhibitor at higher ones. The optimal reaction conditions for chlorophyllase biocatalysis in the aqueous/miscible organic system were determined to consist of 20% RBD oil and 10% acetone at a 200 rpm agitation speed and at a temperature and substrate concentration of 35°C and 12.6 μM for chlorophyll, and 30°C and 9.3 μM for pheophytin.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".