Thermodynamic affinity‐based considerations for the rational selection of biphasic systems for microbial flavor and fragrance production
Bibliographic record
Abstract
Abstract BACKGROUND Flavor and fragrance (F&F) compounds are increasingly produced by biotechnological instead of chemical means, as this allows them to be labeled natural additives. However, since most F&F compounds exhibit cytotoxicity towards common microbiological production hosts, in situ product removal strategies using two‐phase partitioning bioreactors are desirable, making the rational selection of effective non‐aqueous phases a crucial step. Here, thermodynamic first‐principles methods and the experimental determination of partition coefficients were used to differentiate between sequestering phases with high and low thermodynamic affinity towards 17 important F&F compounds. RESULTS The approach was highly successful, enabling identification of outstanding extractants for several F&F compounds. Moreover, it was shown that certain solvent classes (e.g. long‐chained alcohols and a variety of esters) function as efficient sequestering phases across all classes of target F&F compounds, whereas other solvent types (such as alkanes), and the liquid polymer silicone oil exhibit poor partitioning behavior. Finally, the tested absorptive solid polymers generally did not constitute effective sequestering phases for the target compounds, due to high fractions of hard/crystalline segments, however, this suggests that designing polymers with a higher proportion of soft segment could lead to enhanced sorptive capacity. CONCLUSIONS This study is the first in the current literature to systematically analyze sequestering phase choices for important F&F compounds and can serve as a guide for researchers working on biphasic microbial F&F production. © 2017 Society of Chemical Industry
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".