Lactic fermentation of cooked navy beans by <i>Lactobacillus paracasei</i><scp>CBA L74</scp> aimed at a potential production of functional legume‐based foods
Bibliographic record
Abstract
Abstract In recent years, scientific interest in the development of non‐dairy‐based functional foods is increasing progressively and the fermentation of cereals, legumes, fruits and vegetable‐based foods is becoming an important scientific research topic for the production of new probiotic products. In particular, legumes represent a possible alternative to protein foods from animal origins and an adequate fermentation substrate as they contain high amount of nutrients, such as proteins, carbohydrates, fibres, vitamins, and minerals, which are all useful to the growth and metabolic activity of certain microorganisms. This work focuses on the feasibility of developing a dry legume‐based functional product using a fermentation process carried out on a 10% w/v navy bean suspension, in a lab‐scale stirred batch reactor. After soaking and cooking dried navy beans, the fermentation tests performed on the resulting medium using Lactobacillus paracasei CBA L74 showed a maximum bacterial count of 109 CFU/mL after 20 hours and a maximum lactic acid concentration of 1.9 g/L after 16 hours of process time. A freeze‐drying process was performed on the fermented bean suspension, showing a 2‐log microbial reduction and a bacterial viability in the resulting probiotic powder of 3.7 × 108 CFU/g.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".