Comparative performance of solar cabinet, vacuum assisted solar and open sun drying methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tomato (Lycopersicon esculentum L.var) is one of the most important vegetables in our diet and dried tomato products are becoming popular for the preparation of various food items. Though sun drying has been used for the preservation, it is a slow process and the quality of the dried product is often inferior due to contaminations. Therefore, a lab model solar cabinet and vacuum assisted solar dryers were developed to study the drying kinetics of tomato slices (4, 6 and 8 mm thicknesses) and the results were compared individually with open sun drying under the weather conditions of Montreal, Canada. The drying kinetics using thin layer drying models and the influence of weather parameters such as ambient air temperature, relative humidity, solar insolation and wind velocity on drying of tomato slices were evaluated. During drying, it was observed that the temperatures inside the solar cabinet and vacuum chamber were increased to 63 and 48oC when the maximum ambient temperature was only 30oC. The tomato slices of 4, 6 and 8 mm thicknesses could be dried from 94.0 to 11.5% wet basis moisture content, respectively in 300, 420 and 570 min using solar cabinet, in 360, 480 and 600 min using vacuum assisted solar dryer and it took 435, 615 and 735 min under open sun drying method. The quality of tomato slices in terms of physicochemical parameters such as colour retention, water activity, rehydration capacity and ascorbic acid retention were evaluated and the overall study concluded that good quality dehydrated tomato slices could be produced by using vacuum assisted solar dryer compared to solar cabinet and open sun drying methods. The Page model was found to be better in describing the drying kinetics of tomato slices in all the drying methods studied.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it