Effect of Different Concentrations of Salicylic Acid as Post-harvest Treatment on Physicochemical Properties and Shelf Life of Mango (<i>Mangifera indica</i> cv. Bombay green)
Bibliographic record
Abstract
This study was undertaken at the Horticulture Laboratory of College of Natural Resource Management Bardibas, Mahottari, Nepal in 2023.Physiological loss in weight, fruit firmness, shelf life, pulp pH, Total soluble solids (TSS), Titratable acidity (TA), and TSS: TA ratio were to be determined for the study.The study contained 5 different concentrations of salicylic acid as five treatments (0 ppm, 50 ppm, 100 ppm, 150 ppm and 200 ppm) with four replications of each on a Completely Randomized Design (CRD).For each treatment destructive and non-destructive sample were prepared.Data obtained from various biochemical analyses of physicochemical properties (physiological loss in weight, total soluble solids, titratable acidity, pulp pH, TSS: TSS ratio, and shelf life of mango) were recorded and statistically analyzed by using Gen-Stat software.The fruits were evaluated at the three-day interval after the initial reading taken on the day of storage and further data were recorded after 3,6,9,12, and 15 days of storage.Among all the salicylic acid treatments, @200 ppm recorded the minimum physiological loss in weight, the highest total soluble solids (21.44ºBrix), maximum fruit firmness (1.91 kg/cm 2 ), highest titratable acidity (0.166%), highest TSS: TA ratio (129.4), and minimum pulp PH (6.00).The longest shelf life was observed with fruit treated with a 200 ppm concentration of salicylic acid (15.71 days) which was similar to 150 ppm of salicylic acid (15.35Days).Salicylic acid at 200 ppm showed the best performance in retarding the changes in physicochemical properties and prolonging the shelf life of mango fruits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".