Enhanced bioproduction of carvone in a two‐liquid‐phase partitioning bioreactor with a highly hydrophobic biocatalyst
Bibliographic record
Abstract
The microbial biotransformation of (-)-trans-carveol to the flavor and fragrance compound (R)-(-)-carvone by Rhodococcus erythropolis DCL14 was carried out in a 3 L two phase partitioning bioreactor with an immiscible liquid second phase in an effort to improve upon the reactor performance achieved in a single aqueous phase system. The purpose of employing the liquid second phase is to minimize biotransformation rate inhibition due to the accumulation of the toxic substrate (cis-carveol) and product (carvone) in the aqueous phase. 1-Dodecene was chosen as the solvent for this application because it is biocompatible, non-biodegradable and has a superior affinity for the target product (carvone) relative to the other solvents tested. However, when 1-dodecene was used in the biotransformation, the extremely hydrophobic R. erythropolis DCL14 created an emulsion with the organic solvent with significant sequestering of the cells into the organic phase and negligible substrate conversion. To overcome these operational difficulties, silicone oil, which is considered a liquid polymer, was used with the aim of preventing emulsification and sequestration of cells in the non-aqueous phase. Although some emulsification of the water-silicone oil was again created by the cells, operability was improved and, in fed-batch mode, the system was able to convert approximately 2(1/2) times more carveol than a benchmark single aqueous phase system before substrate/product toxicity caused the biotransformation to stop. This study has demonstrated enhancement of a microbial biotransformation for the production of a high value nutraceutical compound via the use of a second partitioning phase, along with operational challenges arising from the use of a highly hydrophobic organism in such systems.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".