Analysis of the volatile compounds associated with pickling of ginger using headspace gas chromatography ‐ ion mobility spectrometry
Bibliographic record
Abstract
Abstract Pickled ginger is a popular traditional Chinese pickled food. Analysis of the volatile compounds in pickled ginger is critical for guiding production, achieving a high level of sensory quality, and maintaining a healthy diet. Ion mobility spectrometry (IMS) with gas chromatography (GC) offers a fast, sensitive, and efficient tool for detecting volatile compounds. Herein, the headspace GC‐IMS method was used to detect the volatile flavour compounds produced during the pickling of ginger. The ion mobility data were continuously processed using the principal component analysis (PCA) and fingerprint chart methods. Based on the analysis of fresh ginger, pickled ginger, and soy sauce, two main components accounted for 58% and 27% of the total variance. During pickling, the heptanal and heptanone contents decreased, while the contents of butanal, butanone, and methional increased, as determined from the GC‐IMS fingerprint, resulting in changes in the flavour of the pickled ginger. The GC‐IMS method was efficient, convenient, and useful for the detection of volatile flavour compounds produced during the ginger pickling process.
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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".