New Insights Into the Use of Cereals and Pseudocereals in Fermented Beverages: Trends, Challenges, and Innovations
Bibliographic record
Abstract
ABSTRACT Nowadays, cereals and pseudocereals are crucial in producing fermented drinks, conferring their nutritional, functional, and sensory properties. This review considered the transition from the traditional grains (i.e., barley, wheat, rice, and maize) to pseudocereals (i.e., buckwheat, quinoa, and amaranth) and hybrid cereals (i.e., triticale and tritordeum), induced by the demand for the gluten‐free, nutritious, and sustainable foods. The aims of this review include assessment of their compositional benefits (e.g., proteins, fiber, and antioxidants), technical challenges (e.g., enzymatic limitations and process scalability), and innovations (e.g., enzyme‐catalyzed processing, extrusion, and artificial intelligence‐based optimization) to improve brewing efficiency and the quality of the final products. As novel grains open up the market potential and promote the health and sustainability trends, assessing the technological challenges, such as raw material heterogeneity and enzymatic flexibility, presents challenging tasks for industrially viable deployment. Such emerging strategy options can redefine brewing procedures, enable innovation, and meet consumers' demands.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".