Short-Chain Fatty Acid Utilization in Cyberlindnera jadinii for Single-Cell Protein and Odd-Chain Fatty Acid Production
Bibliographic record
Abstract
In view of the growing global need for sustainable protein sources, this study explores the utilization of short-chain fatty acids into single-cell protein using the non-conventional yeast Cyberlindnera jadinii. Short-chain fatty acids can be sustainably produced via anaerobic digestion of organic waste, presenting a promising fermentation substrate for a circular bioeconomy. Cyberlindnera jadinii is demonstrated to be capable of growing on acetate, propionate and butyrate as both a carbon and energy source without strong inhibition. Bioprocess development was conducted in stirred tank bioreactors, where a fed-batch pH-stat bioprocess led to improved efficiency without substrate inhibition. The highest titer of 31.3 ± 1.0 g/L, rate of 0.67 ± 0.02 g/L/h and yield of 0.36 ± 0.01 g/g was achieved with propionate. The resulting biomass contained 41.3% crude protein, and 17.3% crude lipids with 81% unsaturated fatty acids. In contrast to acetate and butyrate, propionate as a substrate led to accumulation of 37% odd-chain fatty acids with titer, rate and yield of 1.74 ± 0.06 g/L, 0.037 ± 0.001 g/L/h and 0.020 ± 0.001 g/g. These findings confirm that short-chain fatty acids are viable fermentation substrates not only for single-cell protein, but also unsaturated and odd-chain fatty acid production with Cyberlindnera jadinii.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".