A review of bread qualities and current strategies for bread bioprotection: Flavor, sensory, rheological, and textural attributes
Bibliographic record
Abstract
The unequivocal economical and social values of bread as a staple food commodity lead to constant interests in optimizing its postproduction quality and extending its shelf life, which is related to the maintenance and enhancement of flavors and textural properties, and finally, to the delay of microbial spoilage. The latter has been the subject of a multitude of studies and reviews, in which the different approaches and views were discussed. However, variations in bread freshness, flavor, and textural quality are still of concerns for the bread making industry, in conjunction with the expectation from consumers for bread products with high-quality attributes and free of synthetic ingredients that satisfy their pleasure and their sustainable lifestyle. This review mainly focuses on the quality profiles of bread, including flavor, rheological, textural, and sensorial aspects; on the modalities to assess them; as well as on the conventional and emerging approaches developed so far over the past decades. The applications of lactic acid bacteria (LAB) and enzymes as bioprotective technologies are examined and discussed, along with active packaging and novel processing technologies for either the maintenance or improvement of bread qualities during storage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.005 | 0.002 |
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".