Physicochemical and Nutritional Characteristics of Solar and Sun-dried Tomato Powder
Bibliographic record
Abstract
Tomato (Solanum lycopersicum) is one of the most important vegetable plants in the world used in cooking foods. In Ghana, at peak seasons of harvesting tomato, high postharvest loses are incurred because of the absence of industrial tomato processing facilities for value addition into other convenient forms to extend shelf-life. Solar drying is a more efficient and low cost method of enhancing the quality and adding value to tomato. This study was carried out to design a natural mixed mode solar dryer suitable for drying tomato and to investigate the quality characteristics of the dried products. Fresh tomatoes were pre-treated with: a) 1% potassium metabisulfite solution; b) 1 % ascorbic acid solution; and c) used untreated as control. Pre-treated tomatoes were dried for three days on the average till there was no significant change in the final moisture content. Open-sun drying was used as a control for comparison purposes. The final moisture content for pre-treated solar dried and sun-dried tomato was 14 - 15 % and 19 - 22 %, respectively. The ash content was higher in the sun-dried samples compared to the solar dried samples, an indication of contamination with extraneous materials from the environment. Sulfur dioxide content of 740.8 mg/Kg d.w., for solar dried tomato pre-treated with potassium metabisulfite was much lower than the maximum legal limit of 2000 mg/Kg d.w., of sulfur dioxide recommended in fruits. Solar and sun-dried tomato pre-treated with potassium metabisulfite had significantly higher carotenoids, lycopene and beta carotene contents than pre-treated samples, and the controls. The quality of pre-treated solar-dried tomato was better enhanced by the use of the solar dryer compared to sun-drying.
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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 | 0.001 | 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".