Discovery and Identification of Tastants and Taste-Modulating <i>N</i>-Acyl Amino Acid Derivatives in Traditional Korean Fermented Dish Kimchi Using a Sensomics Approach
Bibliographic record
Abstract
Sensory-guided fractionation by means of ultrafiltration and gel permeation chromatography followed by high-performance liquid chromatography, synthesis, liquid chromatography–tandem mass spectrometry (LC–MS/MS) quantitation, and taste re-engineering experiments revealed taste-active and taste-enhancing compounds contributing to the umami, mouthful and complex taste profile of the fermented Korean dish, kimchi. Besides basic taste-active compounds, in particular, various N -acylated amino acids deriving from succinic acid and lactic acid imparted taste-modulating properties in food matrices. Taste threshold concentrations were determined to evaluate intrinsic and modulating effects. Quantitation of N -acylated amino acids in kimchi following synthesis revealed the presence of numerous derivatives showing taste–active properties. Sensory evaluation including recombination and partial addition experiments highlighted that both the N -lactoyl- and the N -succinoyl amino acid derivatives contribute to increasing the fullness, volume, and complexity of food matrices, whereas the latter directly contributes to the overall taste of kimchi in natural concentrations.
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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 | 0.000 |
| 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".