Effect of pH, temperature and heating time on the formation of furan in sugar–glycine model systems
Bibliographic record
Abstract
Furan (C 4 H 4 O) has been classified as a possible animal and human carcinogen by many international agencies. The formation of furan in three sugar–glycine models using glucose, fructose, and sucrose was investigated using headspace gas chromatography mass spectrometry method (HS-GC–MS) with various dual combinations of three important heat processing conditions, i.e. pH, temperature, and heating time. Results indicated that furan levels from sugar–glycine model systems during the thermal processing can be attributed to selective sugar types, pH, temperature, and heating time. In glucose–glycine and fructose–glycine system, the lowest furan level was detected in acid condition but in sucrose–glycine system furan formed significantly lower ( P < 0.05) in acidic conditions the lowest furan level was found in alkaline conditions. The furan levels were observed to increase with heating time in all three model systems. Furthermore, less furan was generated in non-reducing sugar system (sucrose) than in reducing sugar system (glucose and fructose). Therefore, they demonstrate the possibility of limiting the formation of furan in heat processed foods by both the careful selection of carbohydrates (i.e. non-reducing sugars and reducing sugars) ingredients and appropriate processing conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".