Mathematical Modeling of Drying Pattern of Ogi Produced From Two Types of Maize Grain
Bibliographic record
Abstract
<p>This paper presents thin layer modeling of <em>ogi</em> produced from yellow and white maize at varying soaking period and dried in the cabinet and oven at 50 ºC. The moisture decrease for cabinet dried o<em>gi</em> produced from white maize from 49.0 11.5%, 49.5 to 11.32%, 46.5 to 12.33% and 46.12.29%. The drying rate for both oven and cabinet dried <em>ogi</em> produced from yellow maize decreased from 4.6 to 0.0525 kg/min, 4.5 to 0.0513 kg/min, 4.35 to 0.049 kg/min and 4.4 to 0.047 kg/min while for oven dried <em>ogi</em> followed a similar trend. The experimental data obtained were fitted to five thin layer models: Newton, Page, Herderson and Pabis, Two term and Wingh and Singh models. The values obtained for <em>ogi</em> produced from white maize and dried in the cabinet and oven at 50 ºC for Newton model gave a lower R<sup>2</sup>, ?<sup>2</sup>, RMSE compared with respective values obtained from Page, Herderson and Pabis, two term, Wing and Singh models. The two terms model appear to be the best model among the five models used in this work and had higher R<sup>2</sup>, lower ?<sup>2</sup>, and RMSE. The <em>ogi</em> produced from yellow maize at varying soaking period of 24, 48, 72 and 96 hours and dried in cabinet dryer and fitted with two term showed model constants a, K<sub>0,</sub> b, K<sub>1</sub> 0.04315, 0.0388995, 0.919, 2.2 × 10<sup>-3</sup> while the R<sup>2</sup>, ?<sup>2 </sup>RMSE were 0.9933, 5.85 × 10<sup>-4</sup> and 4.85 × 10<sup>5 </sup>for <em>ogi</em> produced for 24 hours soaking, respectively. The soaking period does not seem to affect the moisture ratio and the thin layer drying model. However, the initial moisture and equipment seems to affect significantly.</p>
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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 | 0.003 | 0.001 |
| 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".