Effects of smoke, hexanal, and calcium chloride onpost-harvest quality of oranges [Citrus x sinensis (L.) Osbeck] cvs Msasa and Jaffa under different storage durations and conditions in Tanzania
Bibliographic record
Abstract
Experiments were conducted to assess the effects of hexanal, calcium chloride, and smoke on the post-harvest quality of oranges under ambient (room) temperature (28±2?C) and reduced temperature storage (18±2?C) conditions on two varieties of sweet orange (Citrus x sinensis (L.) Osbeck) cvs ‘Msasa’ and ‘Jaffa’. Fruit were dipped in enhanced freshness formulation (EFF) containing hexanal as the key ingredient at 0.01%, 0.02%, and 0.04% (volume/volume), or calcium chloride solution at 1%, 2%, and 4% (weight /volume) for five minutes each, or subjected to a smoking regime, simulating a popular traditional practice, by burning 0.5 kg, 1.0 kg, and 1.5 kg of dried banana leaves, or left untreated (control). Various parameters including physiological weight loss, fruit firmness, total soluble solids (TSS), titratable acidity (TA), and the TSS/TA ratio were assessed to determine effects on post-harvest quality of fruit. Results indicate that hexanal and calcium chloride treatments significantly (p < 0.001) reduced physiological weight loss, maintained fruit firmness and significantly higher TSS in both varieties compared to smoke treatment and untreated controls. Reduced temperature storage also significantly (p < 0.001) lowered physiological weight loss of hexanal- and calcium chloride-treated oranges. Based on the results of this study, post-harvest dip treatments with hexanal solution at 0.02% or calcium chloride solution at 2% coupled with reduced temperature storage at 18°C are recommended to maintain the quality of fresh oranges in Tanzania. On the contrary, the application of smoke is highly discouraged as it reduces the quality of oranges.
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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 | 0.001 | 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".