Yeast cell factories for sustainable whey-to-ethanol valorisation towards a circular economy
Bibliographic record
Abstract
Cheese whey is the major by-product of the dairy industry, and its disposal constitutes an environmental concern. The production of cheese whey has been increasing, with 190 million tonnes per year being produced nowadays. Therefore, it is emergent to consider different routes for cheese whey utilization. The great nutritional value of cheese whey turns it into an attractive substrate for biotechnological applications. Currently, cheese whey processing includes a protein fractionating step that originates the permeate, a lactose-reach stream further used for valorisation. In the last decades, yeast fermentation has brought several advances to the search for biorefinery alternatives. From the plethora of value-added products that can be obtained from cheese whey, ethanol is the most extensively explored since it is the alternative biofuel most used worldwide. Thus, this review focuses on the different strategies for ethanol production from cheese whey using yeasts as promising biological systems, including its integration in lignocellulosic biorefineries. These valorisation routes encompass the improvement of the fermentation process as well as metabolic engineering techniques for the introduction of heterologous pathways, resorting mainly to Kluyveromyces sp. and Saccharomyces cerevisiae strains. The solutions and challenges of the several strategies will be unveiled and explored in this review.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".