Optimizing Haskap Berry (<i>Lonicera caerulea</i>) Storage Conditions and Monitoring Antioxidant Systems
Bibliographic record
Abstract
ABSTRACT With increasing demand for crops resilient to environmental variability, haskap ( Lonicera caerulea ) emerges as a promising berry for northern climates due to its rich bioactive content and distinct flavor. Nevertheless, its fragility presents major challenges for maintaining postharvest quality. This study evaluated the effect of storage conditions on haskap quality using two experimental approaches. The first experiment monitored quality attributes (color, firmness, total soluble solids, titratable acidity, weight loss, and antioxidant capacity) at temperatures (0°C, 4°C, and 8°C) over 28 days. Storage at 0°C most effectively preserved antioxidant capacity, with peak levels observed on Day 21 (1.04 ± 0.2 mM TE g −1 D.W), alongside maximal phenolic (37.38 ± 1.59 mg GAE g −1 D.W) and flavonoid (26.99 ± 0.99 mg rutin eq g −1 D.W) content. The second experiment, employing a factorial design, assessed the interactive effects of relative humidity (RH) (90% and 95%), temperature (0°C, 4°C, and 8°C), and time (1, 10, and 20 days) on haskap quality. Optimal conditions for quality preservation were identified as 0°C and 90% RH for 10 days of storage. These findings establish key storage requirements for haskap, informing postharvest practices aimed at maximizing shelf life and bioactive retention.
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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 | 0.001 | 0.001 |
| 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".