Development of a Whey Protein Recovery Process Using Sugar Kelp (Saccharina latissima) Extracts
Bibliographic record
Abstract
Whey is the largest waste product of the cheese-making industry and the current methods of extracting the nutrients from it are costly and inefficient. This study assessed the feasibility of using crude polysaccharides to flocculate proteins from liquid whey waste. The flocculants used were a sugar kelp (Saccharina latissima) extract, as well as commercial seaweed polysaccharides, alginate and k-carrageenan, to recover proteins from the liquid whey waste. Physicochemical and functional parameters including protein content, protein recovery efficiency, mineral content, total phenolic content (TPC), antioxidant capacity, color, water- and oil-holding capacity, gelling capacity, foaming activity and stability, and emulsifying activity and stability were tested on the resulting flocculates. The yield of the dried flocculates by use of alginate, the sugar kelp polysaccharide extract (SKPE), and carrageenan were 1.66, 0.98, and 1.22 g/100 g of liquid whey with protein contents of 27.4%, 45.5%, and 37.5%, respectively. The protein recovery efficiency from the whey was 57.5%, 56.2%, and 57.9% using alginate, SKPE, and carrageenan, respectively. The alginate flocculate had the highest oil-holding capacity and foaming abilities while the carrageenan flocculate had the best gelling ability and the highest emulsifying activity and stability. TPC and antioxidant activity were highest in the SKPE flocculate. All three flocculates presented slightly different compositional and functional qualities, which could be used for a variety of products. This study showed that seaweed polysaccharides present a simple and effective way to extract protein from liquid whey waste while creating a functional and high-protein ingredient.
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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".