Mathematical Modeling of Drying Pattern of Ogi Produced From Two Types of Maize Grain
Bibliographic record
Abstract
This paper presents thin layer modeling of ogi 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 ogi 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 ogi 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 ogi 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 ogi produced from white maize and dried in the cabinet and oven at 50 ºC for Newton model gave a lower R2, ?2, 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 R2, lower ?2, and RMSE. The ogi 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, K0, b, K1 0.04315, 0.0388995, 0.919, 2.2 × 10-3 while the R2, ?2 RMSE were 0.9933, 5.85 × 10-4 and 4.85 × 105 for ogi 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.
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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 | 0.001 |
| 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".