Application of Fermentation as a Strategy for the Transformation and Valorization of Vegetable Matrices
Bibliographic record
Abstract
This review paper addresses vegetable fermentation from a microbiological and technological point of view, with particular emphasis on the potential of lactic acid bacteria to carry out these transformations. This review paper also covers the spectrum of traditional and emerging fermented plant foods. Fermentation with lactic acid bacteria represents an accessible and appropriate strategy to increase the daily consumption of legumes and vegetables. Often, lactic fermentation is carried out spontaneously following protocols firmly rooted in the culture and traditions of different countries worldwide. Fermented plant products are microbiologically safe, nutritious, and have pleasant sensory characteristics, and some of them can be stored for long periods without refrigeration. Controlled fermentation with selected lactic acid bacteria is a promising alternative to guarantee high-quality products from a nutritional and organoleptic point of view and with benefits for the consumer’s health. Recent advances in genomics and molecular microbial ecology predict a bright future for its application in plant fermentation. However, it is necessary to promote molecular approaches to study the microbiota composition, select starters aimed at different legumes and vegetables, generate products with nutritional properties superior to those currently available, and incorporate non-traditional vegetables.
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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 | 0.001 | 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".