Nutritional Value of Brewer’s Spent Grain and Consumer Acceptance of Its Value-Added Food Products
Bibliographic record
Abstract
Brewer’s spent grain (BSG), a byproduct of the brewing process, offers a sustainable alternative applicable to human nutrition. The nutritional composition, health advantages, and value-added uses of BSG in diverse food items, including snacks, bread, cookies, and pasta, are examined in this review. Furthermore, consumer acceptance and organoleptic attributes, including texture, taste and appearance, are discussed. BSG is composed of 60% carbohydrates (of which 50% dietary fiber), 10% lipids, and 30% proteins. BSG is also high in minerals such as calcium and phosphorous and bioactive polyphenols such as catechin, p-coumaric, and ferulic acid. BSG holds significant opportunities to be utilized in enhanced food production, biofuel generation, and other industrial applications. The reported therapeutic effects of BSG include anticarcinogenic, antiatherogenic and oxidative stress reduction. Based on sensory evaluations, the maximum amount of BSG that can be added to food products to maintain consumer acceptance is 15%. There is a need to convince manufacturers and consumers of the potential of incorporating BSG into food products, the health benefits of this, and the sustainability advantages of the use of BSG. The integration of BSG into food systems will contribute to food waste minimization and the promotion of the circular economy.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".