Coating Guava Postharvest With the Use of Starch of Tamarind Seed and Pomegranate Seed Oil
Bibliographic record
Abstract
The effect of coatings with different concentrations of tamarind seed starch associated with pomegranate seed oil in ‘Paluma’ guava was investigated in the present work. The fruits were harvested from an orchard in the morning, packed in containers previously lined with paper, and transported to a laboratory, where they were selected, washed, sanitized, and separated at random for the application of each treatment. The experiment design used was completely randomized, in the 6 × 6 factorial scheme, six coatings and six evaluation periods, with 3 replicates made up of 2 fruits. The treatments were: T1 (control or reference sample), without coating; T2 (0.24 mL of pomegranate seed oil/mL of coating); T3 (2% tamarind starch); T4 (1% tamarind starch); T5 (2% of tamarind starch); T6 (3% tamarind starch), T4, T5 and T6 were associated with 0.24 mL/mL of the pomegranate seed oil. The treatments were applied under immersion of the fruits in the solutions and then stored in a refrigerated chamber at 10±2 °C and 80±5% RH, and the evaluations were performed at intervals of 3 days to 12 days of storage. Due to the maintenance of the quality of the fruits, and without sufficient material, it was decided to extend storage time until the 21 days. Thus, the analyses were performed at 0, 3, 6, 9, 12 and 21 days with evaluations at 0, 3, 6, 9, 12, and 21 days. The T6 treatment (3% tamarind starch + 0.24 mL/mL pomegranate seed oil) was more efficient regarding luminosity (L*) of the fruits and delaying color development , expressed by the values of C*, also showing the higher retention in the loss of firmness, lower mass loss and lower soluble solids content, suggesting that this treatment possibly inhibited the degradation of polysaccharides, delaying the ripening process of the fruits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".