Stability enhancement of betalain pigment extracted from Celosia cristata L. flower through copigmentation and degradation kinetics during storage
Bibliographic record
Abstract
Celosia cristata Linn., an underutilized flower, contains betalains. The stability of betalain pigments in complex food systems is a significant challenge. In this study, we investigated the potential of copigmentation using gum arabic (0.33 % to 1 %), pectin (0.33 % to 1 %), whey protein (0.33 % to 1 %), ascorbic acid (0.05 %), and calcium carbonate (0.01 %) on betalain content, color stability, and microbial counts in betalains pigments extracted from Celosia Cristata L. flowers during a 90-day of storage period. A total of seven copigmentation treatments (T1 to T7) and a control (T0) without copigmentation were applied to the betalain pigments. The degradation kinetics of betalain pigments at different temperatures were also investigated. The findings revealed that among all copigmentation treatments, T7 (0.33 % gum Arabic, 0.33 % pectin, 0.33 % whey protein, 0.05 % ascorbic acid, and 0.01 % Ca 2+ ) exhibited the highest stability in terms of betalain content and color degradation. • Celosia cristata Linn. Flower used for extraction of betalains. • Copigment treatment T7 (0.33 % gum Arabic, 0.33 % pectin, 0.33 % whey protein, 0.05 % ascorbic acid, and 0.01 % Ca 2+ ) was found best. • Degradation Kinetics of betalains pigment at 4 °C, 20 °C, and 40 °C during storage were investigated.
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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".