Composition of phenolics and volatiles in strawberry cultivars and influence of preharvest hexanal treatment on their profiles
Bibliographic record
Abstract
Misran, A., Padmanabhan, P., Sullivan, J. A., Khanizadeh, S. and Paliyath, G. 2015. Composition of phenolics and volatiles in strawberry cultivars and influence of preharvest hexanal treatment on their profiles. Can. J. Plant Sci. 95: 115-126. Biochemical changes of quality-determining components were evaluated in strawberry fruit subjected to preharvest spray treatments using a hexanal-containing formulation that is known to enhance shelf life and quality of fruits. Phenolic compounds and volatiles of fruits of four strawberry cultivars (Mira, Jewel, Kent, and St. Pierre) grown in southern Ontario were characterized by HPLC-MS and solid phase micro extraction (SPME) analysis. Qualitative and quantitative profiles of phenolic compounds varied among the cultivars. In all the cultivars, anthocyanins constituted the most prominent class of phenolic compounds. Volatile profiles of strawberry homogenate differed among the cultivars. Changes in phenolics and volatiles composition were determined in fruits of Mira and Jewel after spraying with a hexanal-containing formulation at weekly intervals. In Jewel, preharvest hexanal spraying altered the profiles of polyphenolic components, while minimal changes were noticed in Mira. Interestingly, very few differences were identified in ester profiles of treated and untreated Mira. In general, hexanal spray application resulted in a decrease in the abundance of several volatile components including esters, ketones, and lactones in treated Jewel compared with the control. The results suggest that cultivar-specific quality changes may result from a preharvest application of hexanal formulations, which may also imply different patterns of metabolite channeling and delay of fruit ripening processes.
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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 | 0.000 |
| 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".