Ingredient Technologies and Process Modifications for Increasing the Use of Ancient Grains in Bakery Applications
Bibliographic record
Abstract
Greater consumer demand for sustainable, nutrient-dense grains has inreased research emphasis on underutilized wheat species, such as ancient grains. However, these wheat species have different physicochemical qualities, in comparison to conventional hexaploid bread wheat. Consequently, the end-product quality of baked products developed using ancient wheat species is inferior to those that use common hexaploid bread wheat. In this review, approaches that can be used to enhance the functionality of ancient grains are explored as resolving these limitations is essential for expanding the use of these underutilized grains in bakery applications. An evaluation of the current literature suggests a need to examine existing ingredient technology-related solutions and processing techniques, as well as their anticipated impacts on the functionalities of these underutilized wheat species, for development of value-added bakery products. Furthermore, the findings indicate that an in-depth understanding of the physicochemical properties of ancient grains and the impact of different functionality enhancement techniques on the chemistry of these grains is essential to successfully utilize different ingredient technologies and processing techniques. Therefore, this is an area of research that needs further investigations, especially from an underutilized grains standpoint, to fully unravel the potential of these grains in different bakery applications.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".