Investigation of the efficiency of different biocatalytic systems for the bioconversion of lactose and dairy by-products into lactobionic acid
Bibliographic record
Abstract
Selected biocatalytic systems, LactoYIELD(LY)/Catazyme®(Cataz), LY/Cataz/Laccase, LY/Cataz/Laccase/mediator, were investigated to bioconvert lactose into lactobionic acid(LBA). LY expresses cellobiose oxidase that catalyzes the oxidation of lactose into LBA, while Cataz catalyzes the decomposition of hydrogen peroxide into water and oxygen. ABTS redox mediator with laccase promotes the formation of stable-radicals that act as oxidizing agents. Contrary to LY/Cataz/Laccase/mediator system, the bioconversion efficiency of LY/Cataz system was pH dependent. The bioconversion efficiency of ultrasound-assisted systems was comparable to the conventional ones, whereas microwave-assisted systems reduced the reaction time from 8 h to 2 h. In both LY/Cataz and LY/Cataz/Laccase/mediator systems, oxygen addition by bubbling improved the bioconversion yield of LBA at a controlled pH. LY/Cataz/Laccase/mediator system supplemented with oxygen showed a higher yield of 53% and productivity of 13.2 g/l.h at a high lactose concentration of 200 mg/ml compared to LY/Cataz system (42%, 10.5 g/l.h). LY/Cataz system performed better for the bioconversion of lactose (100 mg/ml) into LBA with a yield of 100%, in the presence of hydrogen peroxide (0.1–0.5%, v/v) added to generate in-situ oxygen by Cataz. The highest productivity of 17.5 and 21.1 g/l/h was obtained with whey permeate and lactose, respectively, using LY/Cataz system at a controlled pH and in the presence of 0.5%(v/v) hydrogen peroxide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".