Identification of volatile compounds and odour activity values in quinoa porridge by gas chromatography–mass spectrometry
Bibliographic record
Abstract
BACKGROUND: Quinoa porridge is becoming popular among Asian for its nutritional values; hence, it is important to understand its aroma characteristics. RESULTS: Volatile compounds in porridge of 30 quinoa varieties were determined by gas chromatography-mass spectrometry combined with headspace-solid phase micro-extraction. In total, 53 volatile compounds were detected and grouped into 14 alkanals, four alcohols, seven ketones, 10 alkanes, 10 acids and esters, and eight heterocycles. The relative content of alkanes (22.97%), acids and esters (44.33%) was comparatively high, although alkanals (11.75%) may dominate the aroma. Most of the compounds were similar with respect to types and numbers, although they varied in amount, whereas 11 compounds varied significantly among different varieties. The 30 varieties could be divided into eight groups based on the concentrations of volatile compounds, although the same varieties would be divided into four groups if based on the relative odour activity values of twelve variable aroma compounds. CONCLUSION: Nine compounds were identified as the main contributors to the quinoa porridge aroma, including hexanal, 1-octen-3-ol, 2-pentylfuran, nonanal, (E,E)-2,4-decadienal and 6,10-dimethyl-5,9-undecadien-2-one. Heptanal, benzeneacetaldehyde and decanal may play roles in harmonizing the overall aroma. It is also interesting to note that 6,10,14-trimethyl-2-pentadecanone, with a slightly fatty aroma, showed a high content in all varieties. © 2019 Society of Chemical Industry.
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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 | 0.001 |
| 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".