Effect of moisture content on the thermal properties of Horse-Eye Bean seeds relevant to its processing
Bibliographic record
Abstract
The specific heat, thermal conductivity and thermal diffusivity of the Horse-Eye bean (Mucuna sloanei) were determined as a function of moisture content using the method reported by A.O.A.C (2000). The sample varieties used were the Big Sized and the Small Sized Horse-Eye bean. The specific heat and the thermal conductivity were measured using a Bomb Calorimeter. The thermal diffusivity was calculated from the measured specific heat, thermal conductivity and bulk density of the samples. Within the moisture range of 10.5% to 16.87% (b.b), the specific heat, thermal conductivity and thermal diffusivity varied with the moisture content. Results showed that the specific heat, thermal conductivity and thermal diffusivity of the Horse-Eye bean seeds ranged from 116.76 to 203.29 kJ/kgK; 21.07 to 32.23 W/moC; and 3.12 x 10-7 to 9.19 x 10-7 m 2 /s, for the Big Sized varieties, and 112.06 to 194.61 kJ/kgK; 19.85 to 24.08 W/moC; and 3.05 x 10-7 to 6.71 x 10-7 m 2 /s, for the Small Sized varieties as the moisture content increases from 10.5% to 16.87%. Regression analysis were also carried out on the thermal properties of the Horse-Eye bean varieties and moisture content, and there was positive relationship between the parameters. There were significant effects of moisture content (p < 0.05) on all the parameters conducted. The findings and the data generated will create an impact in the food processing industries for Horse-Eye bean.
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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 | 0.001 |
| 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) | 0.001 | 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".