Plant-based seafood alternatives: Current insights on the nutrition, protein-flavour interactions, and the processing of these foods
Bibliographic record
Abstract
Fish are an important food source; however, the sustainability of current seafood supplies is a major concern for key stakeholders. The development of plant-based seafood alternatives may be suitable products to alleviate some of the pressures on aquatic ecosystems and help support environmental sustainability. However, the wide-spread adoption of these products weighs heavily on the ingredients used in the formulations which should not only satisfy nutritional and sustainability targets but must also meet consumer approval and functionality. In this review, we highlight recent advances in our understanding of the nutritional quality and sensory challenges in particular flavour (which includes taste and aroma), that have so far proven difficult to overcome in the development of plant-based seafood alternatives. Protein interactions that contribute to flavour development in plant-based seafood alternatives and the factors that impact these interactions are also discussed. We also review the recent advances in the innovative technologies used to improve the texture of products in this emerging food category. Finally, we highlight key areas for targeted research to advance the development of this growing segment of food products.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".