Sensory and chemical changes in five varieties of carrot (Daucus carota L) in response to mechanical stress at harvest and post-harvest
Bibliographic record
Abstract
Carrots harvested by hand or machine and given additional mechanical stress by shaking in a transport simulator were analysed for taste, flavour and content of sugars, terpenes, 6-methoxymellein and ethanol as well as for ethylene production and respiration. Carrots stressed by shaking had higher ethylene production and respiration, higher content of ethanol and 6-methoxymellein and lower levels of total terpenes, several individual terpenes and sugars. This corresponded to a higher sensory score for ethanol flavour and odour, bitter taste, earthy flavour, terpene flavour, aftertaste and sickeningly sweet taste and a lower score for acidic taste and sweet taste as measured by an expert taste panel. Ethanol content was highly correlated with ethanol flavour and odour and sickeningly sweet taste. Of five varieties tested, ‘Bolero’ ‘Panter’ and ‘Yukon’ were most sensitive to mechanical stress, whereas ‘Napa’ and ‘Newburg’ were most resistant. Hand-harvested carrots were not significantly different from machine-harvested carrots as regards chemical or sensory variables. Principal component analysis showed only slightly different placing of these samples in the score plot. A digital carrot could monitor the degree of mechanical stress to which the carrots were subjected. © 2001 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 | 0.000 |
| 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) | 0.001 | 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".