Effects of Aloe-pectin coatings and osmotic dehydration on storage stability of mango slices
Bibliographic record
Abstract
Mango (Mangifera indica L.) is one of the most important tropical fruits with inimitable taste, unique flavor, fragrance, and therapeutic traits. It is the national fruit of Pakistan and is commonly called the king of fruits, however, it is very challenging to protect the keeping quality of the fruit. To enhance the shelf life and overcome the microbial count, edible biodegradable (aloe-pectin) coatings were applied on minimally processed (osmotically treated) fresh-cut mango slices. Pre-treatment of Osmotic Dehydration (OD) was performed at 45 °C for 3 hours by 55 °Brix solutions of sucrose and glucose, respectively. The coating solution was made by aloe vera gel with combination of 0.2% CaCl2 (w/v) and 0.5% pectin (w/v). Total Fungal Count (TFC) (yeast and mold growth), Moisture Contents (MC), and Total Soluble Solids (TSS) of minimally processed (osmo-coated) mango slices were analyzed during 15 days of storage at 5 °C. A minimum increase in Total Fungal Count (TFC) from 1.00 to 2.53 Log CFU/g, TSS from 25.30 to 27.16 °Brix, and a decrease in moisture contents from 62.43 to 60.65% was observed in double-coated with osmo-sucrose treated (osmotic dehydration in 55˚Brx sucrose solution before coating) samples from 0 to 15 days respectively. However, significant changes in the TFC, moisture contents, and TSS, were observed in double-coated with osmo-glucose treated (osmotic dehydration in 55 °Brix glucose solution before coating) samples after 10 days of storage. According to the results of the current study, the aloe-pectin coating can significantly reduce the TFC, decrease the TSS, and minimize the moisture loss during storage, when used after osmo-sucrose pre-treatment.
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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".