Storage Temperature Impacts on Anthocyanins Degradation, Color Changes and Haze Development in Juice of “Merlot” and “Ruby” Grapes (Vitis vinifera)
Bibliographic record
Abstract
This study evaluated the degradation kinetics of selected anthocyanins and the change in polymeric color, browning index, and haze development of grape juices from ‘Merlot’ and ‘Ruby’ grape cultivars stored at 5, 25, and 35oC for up to 360 days. Five major anthocyanins namely malvidin-3-O-glucoside (M3G), delphinidin-3-O-glucoside (D3G), petunidin-3-O-glucoside (Pt3G), peonidin-3-O-glucoside (Pn3G), and cyanidin-3-O-glucoside (C3G) were identified. Juice from ‘Merlot’ had significantly higher (p < 0.05) content of all individual anthocyanins as compared to ‘Ruby’. During the long-term storage, total and individual anthocyanins from both cultivars degraded following first-order reaction kinetics at the rate strongly dependent on temperature. At the end of the storage, noticeably higher loss of anthocyanins (95-99.9%) was observed at 25 and 35oC as compared to storage at 5oC [50-60% (‘Merlot’); 74-81% (‘Ruby’)]. Considerably lower rate of decay was observed at 5oC (k = 0.01-0.04) as compared to 25 (k = 0.04-0.14) and 35oC (k = 0.05-0.14) storage temperatures. The most temperature sensitive anthocyanin compounds were C3G (Ea = 66.5 kJ/mol) and D3G (Ea = 63.3 kJ/mol). At higher storage temperatures, significant (p < 0.05) and strong negative correlations were observed between anthocyanin concentrations and the levels of haze, polymeric and brown color development during storage. Storing grape juice, at lower temperature conditions could reduce the sustained loss of biologically active anthocyanins as well as the development of haze and brown color.
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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".