DEBITTERING OF TRYPTIC DIGESTS FROM β-CASEIN AND ENZYME MODIFIED CHEESE BY X-PROLYL DIPEPTIDYLPEPTIDASE FROM LACTOBACILLUS CASEI SSP. CASEI. LLG
Bibliographic record
Abstract
The proline-rich β-casein was digested in vitro with trypsin, and the oligopeptides producedwere then isolated by RP-HPLC and subsequently identified by amino acid analysis and ion massspectrometry. The peptide fractions from the complete digestion were then treated with purified x-prolyldipeptidyl peptidase (X-PDP) extracted from Lactobacillus casei ssp. casei LLG. Two bitter peptides (f53-97and f203-209) containing X-Pro-Y-Pro in their amino acid residues were completely hydrolyzed by X-PDP,while several peptides with a high degree of hydrophobicity were also decreased in a peak area. Thedebittering effect of X-PDP from Lactobacillus casei ssp. casei LLG on enzyme modified cheese (EMC) wasalso investigated by both subjective and objective methods. The bitterness of cheddar cheese slurriessupplemented with Neutrase® 0.5 L was completely eliminated after treatment with crude enzyme extractfrom Lactobacillus casei ssp. casei LLG. Two hydrophobic peptides in EMC with Ala-Pro-Phe-Pro-Glu-Valand Phe-Leu-Leu residues were hydrolyzed by crude enzyme extract. The RP-HPLC, and subsequently, ionmass spectrometry analysis have shown that the debittering effect on EMC was due partially to the presenceof X-PDP.
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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) | 0.001 | 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 | 0.001 |
| 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".