Novel biotechnological approaches to improving aromas and flavors of legume‐derived food products
Bibliographic record
Abstract
Abstract Background and Objectives Beany flavor and aroma pertaining to legumes and legume‐derived food products are among the reasons for their limited acceptance by Western consumers. Fermentation has been used for improving qualities of various food sources in traditional cuisines across the globe for millennia. This review is dedicated to novel ideas regarding the improvement of flavor and aroma properties of legumes and legume‐derived products using fermentation and/or enzymatic treatments. Special attention is paid to the utilization of microorganisms capable of cyclodextrin (CD) production. Findings Novel genetically engineered and/or immobilized microorganisms, such as Bacillus spp., or enzymes, such as cyclodextrin glycosyl transferases (CGTases), were recently shown to be efficient in helping remove undesirable odor‐/flavor‐active compounds from food products. Conclusions Fermentation of legumes/legume‐derived products by CD‐producers is an industrially feasible approach to the production of novel legume‐derived food products with improved odor/flavor properties. Significance and Novelty On the basis of the reviewed material, a two‐stage processing of legumes/legume‐derived products, including either acid treatment or fermentation by lactic acid bacteria (LAB) at the first stage to unbind odor‐/flavor‐active compounds from proteins and then, at the second stage, using CD‐producers (primarily Bacillus spp.) to scavenge such compounds with CDs, is proposed.
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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".