Incorporation of brewer's spent grain into plant‐based meat analogues: benefits to physical and nutritional quality
Bibliographic record
Abstract
Summary Brewer's spent grain (BSG) is the major by‐product of brewery industry. Due to being rich in dietary fibres and proteins, it has great potential to be upcycled in plant‐based foods and contribute to the sustainability of our food system. This study investigated the incorporation of BSG at different concentrations to soy protein‐based high‐moisture meat analogues (HMMAs). Colour, textural properties, and macrostructure of the resulting HMMAs were examined. In vitro protein digestibility of selected HMMAs was also measured. At 15% BSG incorporation level, the presence of BSG favoured texturisation and lowered the hardness of HMMAs. However, higher BSG incorporation levels impaired the formation of a fibrous structure in the HMMAs that mimics the mouthfeel of animal‐based products. When extrusion feed moisture increased from 60% to 70%, the hardness and chewiness of the HMMAs decreased from 223.18 and 188.94 N to 127.80 and 103.16 N, respectively, while the cohesiveness and springiness were not significantly affected. In general, the higher the BSG incorporation level, the darker and browner were the HMMAs. IVPD of HMMAs varied from 74.58% to 76.15% and was higher in the HMMAs containing BSG, indicating that BSG complements soy protein digestibility. BSG incorporation may improve the texture of HMMAs and contribute to the intake of dietary fibre.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".