Ester Variability in Apple Varieties As Determined by Solid-Phase Microextraction and Gas Chromatography−Mass Spectrometry
Bibliographic record
Abstract
Solid-phase microextraction (SPME) with a polydimethylsiloxane fiber coupled with gas chromatography-mass spectrometry (GC-MS) was applied to the study of variability in volatiles released by 13 apple varieties. The relative amounts of 40 esters and alpha-farnesene were determined. Principal component analyses of these results clustered the apples into three groups according to skin color: red, green, and red-green. Total ester contents were highest with the red cluster apples, and the green cluster apples had the highest alpha-farnesene levels. This technology was also applied to the monitoring of changes in volatiles for apples removed from controlled-atmosphere storage with subsequent storage at 4 degrees C and room temperature. Total ester contents increased 25-fold, with the greater increases coming at room temperature, whereas alpha-farnesene levels increased only 5-fold. For apples stored at room temperature, after 11 days, the amount of increase was inversely proportional to the size of the ester: levels of smallest esters (molecular weight 116) increased 12.5-fold, and the largest esters (molecular weight 228) increased approximately 1.3-fold.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".