Starch and Pectin Affect Hardness of Cooked Bananas
Bibliographic record
Abstract
Texture is an important quality attribute of fresh and processed foods. In plant foods, texture is closely related with the structural integrity of the primary cell walls and middle lamella that is mainly composed of pectic substances. Bananas mainly contain water, starch, pectin and fibre which influence texture. Cooking bananas soften on cooking but harden on cooling. Despite many studies on retrogradation of starch and its effects on texture, little is known about the effect of added starch and/or breakdown of pectin on hardness of bananas upon cooking and cooling. In this study, the effects of added pectin and starch and structural elimination of pectin on hardness of bananas during cooking and cooling were investigated. Hydrolysis of pectin resulted in significantly harder bananas during cooking and upon cooling (P<0.05). Hardness of starch-treated bananas increased significantly with increasing starch concentration upon cooking and cooling relative to the control (P<0.05). However, treatment of bananas with added pectin alone resulted in an insignificant increase in hardness relative to the control. Upon cooling, hardness of pectin-treated bananas decreased significantly with increasing pectin concentration (P<0.05). Hardness of cooked bananas treated with a combination of starch and pectin increased but was not significantly different from the control. Upon cooling, hardness of the starch-pectin treated bananas decreased with increasing concentration similar to the effect of pectin when added alone. Current results showed that starch addition increases hardness of bananas upon cooking and cooling. However, pectin addition decreases hardness of cooked bananas upon cooling while structural elimination of pectin increases hardness upon cooking and cooling. These results imply that pectin contributes to a softer texture of bananas during cooking and cooling whereas starch increases hardness in cooked bananas. Pectin can therefore be added up to 5% to decrease hardness of cooked bananas.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".