The Drying Effect of Varying Light Frequencies on the Proximate and Microbial Composition of Tomato
Bibliographic record
Abstract
Tomato samples were dried at different frequency of light using clothes of different colours with wooden dryingfabrication. The proximate composition and microbial count of the Tomato fruits were determined. Resultsshowed that temperature and relative humidity of the environment affected the rate of drying of tomato as wellas the growth of spoilage organisms in the fruits. Highest temperature values of tomato was observed in thecontrol and light red colour frequency which also had a slightly lower average bacterial count (53 × 103 cfu/g and62 × 103 cfu/g) respectively. The light purple colour had highest average bacterial count of 96 × 103 cfu/g whichwas significantly higher (P<0.05) compared with the control and other colour frequency. Tomato dried with lightgreen colour frequency had the highest amount of protein and carbohydrate (13.78% and 51.37%, respectively).Dark blue colour had the highest amount of fat (0.97%), light blue colour had the highest fibre (25.30%), whilethe highest percentage of ash was observed in black colour (54.30%). All data from the colour frequencies weresignificantly different (higher or lower) from the control at (P<0.05). Microorganisms isolated from tomato fruitduring drying were: Erwinia carotovora, Proteus sp, Bacillus sp, Micrococcus luteus, Aspergillus sp, Aspergillusniger, Rhizopus stolonifer, and Penicillium chrysogenum.
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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 | 0.001 | 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".