Application and Evaluation of a Pectin-Based Edible Coating Process for Quality Change Kinetics and Shelf-Life Extension of Lime Fruit (Citrus aurantifolium)
Bibliographic record
Abstract
Uncertain storage conditions lead to considerable quality loss in lime fruits, which affect their consumer acceptability. Studies aimed at quantifying the kinetics of quality changes under different storage conditions are valuable for minimizing the product quality loss and improving their marketability. The objective of this study was to quantify the effect of pectin-based coating on the kinetics of quality change in stored limes fruits using a pre-established coating process. Lime fruits were immersed in the coating emulsion and then surface dried, cooled, and evaluated after storage for different times at selected temperatures (10–25 °C). Quality characteristics evaluated include physical (texture and color), chemical (ascorbic acid, pH, titrable acidity, total soluble solids), and physiological (respiration rate) properties. Results revealed that with the passage of time, the fruits showed progressive increase in shriveling or wilting and loss in green color, and higher temperatures accelerated these changes. The respiration rate in control samples reached 79, 35, and 7 mL CO2/(kg·h) after 7 days at 25 °C and 22 days at 15 and 10 °C, respectively, while those of coated samples were limited to 40, 32, and 1.06 mL CO2/(kg·h) after 11, 25, and 32 days at the same storage temperatures. Control fruits suffered 6%, 10%, and 24% weight loss following 8 days of storage at 10, 15, and 20 °C, respectively, while the losses in coated fruits were lower (2%, 4%, and 17%, respectively). A zero-order model was found appropriate for weight loss, along with a color a value and ΔE, while a first-order model was found to be better for firmness, brix to acidity ratio, ascorbic acid, and b and L values (R2 > 0.9). The Arrhenius model was suitable for temperature sensitivity of the rate constants.
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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) | 0.000 | 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".