The Effects of Ripening and Cold Storage on the Volatile Profiles of Three Japanese Plum Cultivars (Prunus salicina Lindl.) and One Interspecific Plum-apricot Cultivar
Bibliographic record
Abstract
Volatile compounds contribute greatly to the flavour of fruit and can be altered by maturity and cold storage. The volatiles of three Japanese plum cultivars (‘Sapphire’, ‘Songold’, and ‘Larry Anne’) and one plumcot (‘Flavor King’) were determined at three functional stages: commercial harvest, tree-ripened fruit and cold stored fruit. HS-SPME was used for extraction and GC-TOFMS for separation and identification. A total of 52 compounds was found and classified into three groups (‘unique’ (28), ‘generic’ (10) and ‘frequent’ (14)) based on frequency of occurrence. Discriminant analysis showed that the volatile profiles of the three functional stages were distinct within each cultivar, and the main contributors to the patterns were identified. The volatile profiles of ‘Larry Anne’ and ‘Flavor King’ were the most affected by cold storage conditions. Inter-cultivar analysis indicated that the cultivars have different volatile profiles at each of the functional groups with ‘Larry Anne’ and ‘Flavor King’ showing the largest differences. ‘Flavor King’, a plumcot, presented a ripe volatile profile that was much diverged from that of the true plums.
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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 | 0.000 | 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 | 0.001 |
| 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".