pH dependent partitioning behaviour of food and beverage aroma compounds between air‐aqueous and organic‐aqueous matrices
Bibliographic record
Abstract
Abstract Aroma compounds in the Flavornet database were screened for ionizable functional groups such as carboxylic acids, aliphatic and aromatic amines, phenols, alcohols and thiols. Of the 738 aroma compounds listed in this database, 101 molecules have ionizable moieties with estimated monomeric aqueous pK a values ranging between 1.75 and 10.97. pH dependent effective air/water partitioning coefficients (K aw,eff ) and n‐octanol/water partitioning coefficients (D ow ) were estimated for all ionizable aroma compounds over the pH range from 0 to 14. The ionizable aroma compounds display a broad range of K aw,eff (1.8 × 10 ‐23 to 6.1 atm M ‐1 ) and log D ow (‐6.2 to +7.2 units) values. For many aroma compounds, pH dependent ionization will have a significant effect on K aw,eff and D ow , leading to variations in these physico‐chemical properties by up to 11 orders of magnitude over the composite pH range of common foods and beverages. Changes in food and beverage pH affect not only the relative contributions of neutral versus charged forms of ionizable aroma compounds (which directly affects analyte volatility and olfactory reception), but also partitioning between freely dissolved and sorbed forms of the analyte in solution (which indirectly affects analyte volatility). Copyright © 2015 John Wiley & Sons, Ltd.
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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".