Model Studies on the Oxygen-Induced Formation of Benzaldehyde from Phenylacetaldehyde Using Pyrolysis GC-MS and FTIR
Bibliographic record
Abstract
Benzaldehyde, a potent aroma chemical of bitter almond, can also be formed thermally from phenylalanine and may contribute to the formation of off-aroma. To identify the precursors involved in its generation during Maillard reaction, various model systems containing phenylalanine, phenylpyruvic acid, phenethylamine, or phenylacetaldehyde were studied in the presence and absence of moisture using oxidative and nonoxidative Py-GC-MS. Analysis of the data indicated that phenylacetaldehyde, the Strecker aldehyde of phenylalanine, is the most effective precursor and that both air and water significantly enhanced the rate of benzaldehyde formation from phenylacetaldehyde. Phenylpyruvic acid was the most efficient precursor under nonoxidative conditions. Phenethylamine, on the other hand, needed the presence of a carbonyl compound to generate benzaldehyde only under oxidative conditions. On the basis of the results obtained, a free radical initiated oxidative cleavage of the carbon-carbon double bond of the enolized phenylacetaldehyde was proposed as a possible major mechanism for benzaldehyde formation, and supporting evidence was provided through monitoring of the evolution of the benzaldehyde band from heated phenylacetaldehyde in the presence and absence of 1,1'-azobis(cyclohexanecarbonitrile) on the ATR crystal of an FTIR spectrophotometer. In the presence of the free radical initiator, the enol band of the phenylacetaldehyde centered at 1684 cm(-1) formed and increased over time, and after 18 min of heating time the benzaldehyde band centered at 1697 cm(-1) formed and increased at the expense of the enol band of phenylacetaldehyde, indicating a precursor product relationship.
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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".