Textural Hardness of Selected Ugandan Banana Cultivars under Different Processing Treatments
Bibliographic record
Abstract
Textural hardness affects cooking time, processing, fuel used and the quality of cooked bananas. In this study, textural hardness of selected Ugandan cooking and juice banana cultivars at green maturity was determined using a Texture Analyzer in raw form and at 30, 50, 70, 90, 100 and 130 min in boiled, steamed, mashed and cooled forms.Raw juice bananas (JB) were significantly harder (36.17N to 42.43N) than raw cooking bananas (CB) (22.37N to 26.72N) (p<0.05). On cooking, JB were harder than CB irrespective of cooking method and time. Boiling and steaming rapidly decreased hardness of the bananas in the first 30 min and decreased slowly thereafter. Boiling produced softer bananas than steaming while mashing resulted in intermediate hardness. Amongst JB, Kayinja was significantly harder than Ndiizi and Kisubi in boiled and steamed forms (p<0.05). Hardness of CB was not significantly different (p>0.05) for all cooking treatments, but Kibuzi was consistently softer while Kazirakwe and Nakabululu were harder than other CB cultivars.Cooling significantly increased (p<0.05) hardness of bananas under all treatments with JB being harder in all cases. Mashed and steamed bananas were harder than boiled bananas when cooled. Bananas cooked longer had lower hardness regardless of cooking method.Overall, textural hardness decreases with cooking time regardless of cooking method. Boiled bananas are softer than mashed or steamed. Cooling increases hardness which follows first order kinetics. Therefore, bananas should either be boiled or steamed and mashed for softer texture and be eaten within 30 min of serving. Juice bananas should not be cooked because of the hard texture established in this study.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".