A Comparative Study of Five Apple Cultivars Linking Biochemical Changes at Different Developmental Stages with Storage Textural Quality
Bibliographic record
Abstract
Biochemical changes in the cell wall composition and activity of cell wall-modifying enzymes of five apple cultivars, Royal Gala (Gala), Aurora Golden Gala (Aur), Splendour (Spl), Honeycrisp (HC), and Ambrosia (Amb), collected from the 2016 growing season in the early growth phases, namely 40 and 70 days after full bloom (DAFB), at harvest maturity, and after 20 weeks of storage were investigated in relation to the textural changes at harvest maturity through to 20 weeks of storage. Assessing apple texture with a single-point measurement of firmness using a penetration test to a depth of approximately 8 mm in apples may not be an ideal measurement for assessing apple textural quality. Pectin methyl esterase (PME) activity at early developmental stages may be predictive of textural quality after storage. This work also found that β-D-galactosidase (BGAL) and α-L-arabinofuranosidase (AFAS) activities at early developmental stages may be important factors affecting textural quality after storage. Additionally, the degree of methylesterification (DME) assessed with FTIR on apple flesh material at the early developmental stages was strongly positively correlated (r2 ≥ +0.891 to +0.963, p ≤ 0.05) with textural quality after storage, including crispness, indicating that FTIR could serve as a rapid screening tool for textural quality at early developmental stages on minimally processed starting material.
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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".