Glycated Beef Protein Hydrolysates as Sources of Bitter Taste Modifiers
Bibliographic record
Abstract
Being averse to bitter taste is a common phenomenon for humans and other animals, which requires the pharmaceutical and food industries to source compounds that can block bitterness intensity and increase consumer acceptability. In this work, beef protein alcalase hydrolysates (BPAH) and chymotrypsin hydrolysates (BPCH) were reacted with glucose to initiate Maillard reactions that led to the formation of glycated or advanced glycation end products (AGEs), BPAH-AGEs and BPCH-AGEs, respectively. The degree of glycation was higher for the BPAH-AGEs (47–55%) than the BPCH-AGEs (30–38%). Analysis by an electronic tongue instrument showed that BPAH-AGEs and BPCH-AGEs had bitterness scores that were significantly (p < 0.05) less than quinine. The addition of BPAH-AGEs or BPCH-AGEs to quinine led to significant (p < 0.05) reductions (up to 38%) in bitterness intensity of quinine. The use of 3% hydrolysate to react with glucose yielded glycated peptides with a stronger ability to reduce quinine bitterness than when 1% was used. Calcium release from HEK293T cells stably expressing the T2R4 human bitter taste receptor was significantly (p < 0.05) attenuated by BPAH-AGEs (up to 96%) and BPCH-AGEs (up to 92%) when compared to the BPAH (62%) and BPCH (3%) or quinine (0%). We concluded that BPAH-AGEs and BPCH-AGEs may be used as bitter taste blockers to formulate better tasting foods.
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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".