Single-cell proteins: fermentation pathways, nutritional quality and digestibility, and a techno-economic and environmental outlook for sustainable scale-up
Bibliographic record
Abstract
The growing global population and rising living standards are driving an increased demand for protein-rich animal-derived foods. However, traditional livestock-based protein production poses serious environmental concerns, including greenhouse gas emissions, land degradation, and high-water usage. Single-cell proteins (SCPs), defined as protein-rich microbial biomass derived from organisms such as bacteria, yeasts, fungi, and microalgae, have emerged as a promising alternative. SCPs offer high nutritional value with essential amino acids such as lysine, methionine, and threonine, along with additional nutrients including carbohydrates, fats, vitamins, and minerals. This review aims to provide a comprehensive evaluation of SCPs as a sustainable and scalable protein source, with a specific focus on their production through diverse fermentation strategies including solid-state, semisolid-state, liquid, and gas fermentation. Particular attention is given to the use of agro-industrial by-products and renewable gases as cost-effective substrates that align with circular economy principles. In addition to outlining the nutritional and environmental advantages of SCPs, this review critically examines their current limitations, including production scalability, potential safety concerns, and consumer acceptance. By integrating recent findings on life cycle assessments, nutritional profiling, and digestibility, this work highlights key technological innovations needed to accelerate industrial adoption of SCPs. The purpose of this review is therefore to synthesize current advances while identifying the challenges and research priorities that must be addressed for SCPs to achieve widespread application in global food systems.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".