Utilization of fermented and enzymatically hydrolyzed soy press cake as ingredient for meat analogues
Bibliographic record
Abstract
The aim of the present study was to improve the properties of soy press cake to be utilized as an ingredient of meat analogues. Soy press cakes were fermented with lactobacillus strains, and separately hydrolyzed by cellulase/xylanase mixture and α-amylase. Meat analogues were produced with 10% fermented or hydrolyzed soy press cakes. The effect of applied processes on protein oxidation, physical and functional properties of soy press cakes were analyzed, as well as sensory and textural properties of meat analogues. The results indicated that soy press cake was a suitable source of fibre and energy with low content of saturated fatty acids, and provided plant-based proteins and essential amino acids. The study demonstrated the potential of lactic acid fermentation, and enzymatic hydrolysis to improve water- and oil-holding capacity and reduce protein oxidation in soy press cakes. L. acidophilus 336 and cellulase/xylanase mixture were recommended for fermentation and hydrolysis of soy press cakes, respectively, regarding reduction of protein oxidation. Fermentation of soy press cakes with L. plantarum P1 improved the texture of meat analogues. Press cakes fermentation reduced bitterness, increased juiciness, and balanced the taste of meat analogues. Fermented soy press cake was recommended for the production of meat analogues.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".