Determination of flavour profile in Iranian fragrant rice samples using cold‐fibre SPME–GC–TOF–MS
Bibliographic record
Abstract
Abstract A newly developed cold‐fibre solid‐phase microextraction (CF–SPME) device, as a powerful system for collection and concentration of volatile compounds, coupled to a gas chromatography time‐of‐flight mass spectrometer (GC–TOF–MS) system, equipped with a multi‐channel ion detector and a deconvolution software, was investigated for the analysis of volatile flavour compounds in the headspace of rice samples. The proposed combination provided a powerful system for easy and rapid screening of a wide range of flavours in fragrant rice samples. Based on four target analytes, including 2‐acetyl‐1‐pyrroline as a key odorant compound, different experimental parameters were optimized. The effect of the fibre composition, moisture present in the matrix, extraction temperature and time and desorption time were investigated. Nine Iranian and two Indian fragrant rice varieties were analysed using CF–SPME and the results were compared with commercial SPME fibres. The results revealed that uncooked rice samples can be successfully analysed even as dry kernels, without adding water, utilizing the fully automated CF–SPME–GC–TOF–MS. When using PDMS fibre, a clearly distinguishable peak was seen for 2‐acetyl‐1‐pyrroline by simultaneous cooling of the fibre and heating of rice matrices as dry whole grains. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 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".