Comparison of Raspberry Ketone Production via Submerged Fermentation in Different Bioreactors
Bibliographic record
Abstract
Raspberry ketone (RK) has high commercial value in the food and healthcare industries. A biological route to this flavour compound is an attractive prospect, considering the need to meet consumer demands and sustainable goals; however, it is yet to become an industrial reality. In this work, fungal production of raspberry ketone (RK) and raspberry compounds (RC) via submerged fermentation of Nidula niveo-tomentosa was characterized in flask, stirred-tank reactor (STR), panel bioreactor (PBR), and fluidized bed reactor (FBR) configurations. The results indicate that the panel bioreactor resulted in larger, floccose pellets accompanied by maximum titres of 20.6 mg/L RK and 50.9 mg/L RC. The stirred-tank bioreactor with impeller mixing yielded compact elliptical pellets, induced the highest volumetric productivity of 2.0 mg L−1 day−1, and showed RK selectivity of 0.45. While differing mixing strategies had clear effects on pellet morphology, RK production presented a more direct positive relationship with cultivation conditions, and showed appropriate mixing and aeration favour RK to raspberry alcohol (RA). Overall, this paper highlights the importance of bioreactor design to fungal fermentation, and gives insight into green and industrial bioproduction of value-added natural compounds.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".