Estimation of Shelf Life of Mango Juice Produced Using Small-Scale Processing Techniques
Bibliographic record
Abstract
The objective of this study was to estimate shelf life of mango juice produced using small-scale processing techniques. Juice was processed and packaged in 80 plastic bottles and stored at 13 oC and 30 oC. At each temperature, 20 bottles had preservative (0.5 mg/l sodium benzoate) and 20 bottles had no preservative. The juices were analyzed for pH, vitamin C, sensory attributes and microbial load at two week intervals for six weeks. From week 2 to week 6, juices stored at 30 oC had lower pH values (3.15 – 2.80 with preservative, 3.60 – 2.6 without preservative) than juices stored at 13 oC (4.20 - 3.80 with preservative, 4.15 - 3.70 without preservative ) and the differences were significant (p<0.05). At 6 weeks, vitamin C loss was highest (79%) in juice without preservative stored at 30 oC, followed by juice stored at 30 oC with preservative (71.43%). The loss was lowest (26.98%) in juice with preservative stored at 13 oC. Significant differences (p<0.05) in color were observed at week 6 between juices stored at 13 oC (4.5 with preservative and 4.66 without preservative) and 30 oC (5.02 with preservative and 7.00 without preservative). Juices stored at 30 oC were rated ‘bad’ from week 2, in smell (5.91 and 6.25) and taste (5.66 and 6.91) while at that time, juices stored at 13 oC were rated ‘almost similar’ to fresh juice in smell (4.25) and taste (4.25 and 4.58). Storage at 13 oC with preservative resulted in lowest bacteria (2.04 x 104 CFU/ml) and yeast and mold (1.72 x 104 CFU/ml) counts whilst highest bacteria (2.10 x 108 CFU/ml) and yeast and mold (1.96 x 108 CFU/ml) counts were observed in juices stored at 30 oC without preservative. The shelf life was estimated based on taste and smell as 2 weeks and 4 weeks for juices stored at 30 oC and 13 oC, respectively. Chilling combined with use of preservative slowed down rate of vitamin C loss, deterioration of sensory attributes and microbial growth.
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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 | 0.001 |
| 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 | 0.001 |
| 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".