Adding Value to Brewery Industry By-Products as Novel Ingredients in Non-Alcoholic Malt Beverage Applications
Bibliographic record
Abstract
The growing population and increasing concerns about food security and sustainability demand innovative solutions to minimize food waste and transform by-products into functional ingredients valuable to the food sector. Brewery by-products, including brewer's spent grain (BSG) and brewer's spent yeast (BSY), are underutilized resources despite their high protein contents and potential as sustainable food ingredients. This study aimed to transform BSG and BSY into protein hydrolysates (BSGH and BSYH, respectively) through enzymatic hydrolysis and thus add value to these brewery industry by-products to be used in the food industry. These protein hydrolysates were incorporated into non-alcoholic malt beverages at three different concentrations, and their effects on the physicochemical properties, including color, kinematic viscosity, turbidity, foaming capacity and foam stability, of the non-alcoholic malt beverages were evaluated. Both BSGH and BSYH exhibited higher water solubility (WS) and lower water binding capacity (WBC) values when compared to their native non-hydrolyzed forms, enhancing their suitability as ideal ingredients for protein supplementation of a wide range of food and beverage products. The production of peptides of varying sizes underscored the effectiveness of enzymatic hydrolysis which resulted in an increase in cysteine and methionine levels in BSYH but a decrease in BSGH. The addition of BSGH and BSYH increased the kinematic viscosity and turbidity but reduced the lightness values in color of the non-alcoholic malt beverages. When the properties of the protein hydrolysates were compared, BSYH was more effective than BSGH in forming foams and maintaining their stability for longer periods. These findings highlight the potential of brewery by-products, after enzymatic hydrolysis, as protein-rich ingredients that can support more sustainable food systems and contribute to the nutritional enhancement of various low-protein food and beverage products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".