Mineral Content in Dehydrated Mango Powder
Bibliographic record
Abstract
The study was carried out to explore mineral content in dehydrated mango powder made from immature green stage fruits. For the purpose, two type of slices from peeled and unpeeled fruits of four commercial grown varieties viz. Desi, Sindhri, Langra and Chaunsa were prepared. These slices were categorized into three groups A, B and C. In group A, slices were kept in controlled conditions in electric cabinet chamber (dehydrator) at 65oC temperature, while in group B, slices were dried by open sun drying method using muslin cloth over the cots at (43 ± 5 oC) and in group C, slices were kept in wooden glass dehydrator at (48 ± 4 oC). The statistical analysis reveals highly significant differences for all main factors including varieties, dehydration methods, type of mango powder and their interactions. Chaunsa had the highest mean calcium (389.54 mg kg-1), potassium (912.07 mg kg-1) and magnesium (90.92 mg kg-1). However, only sodium was observed more in variety Langra (467.59 mg kg-1). On the basis of dehydration methods, mean calcium (407.06 mg kg-1) and magnesium (90.11 mg kg-1) content were observed more in wooden glass drying method as compared to rest of the drying methods. The sodium (511.83 mg kg-1) and potassium (811.35 mg kg-1)content were recorded the highest in open sun drying method. The powder made from fruits without peel was observed more in all mineral content including sodium, calcium, potassium and magnesium.
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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".