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Record W2896897760 · doi:10.5539/jfr.v7n6p107

Starch and Pectin Affect Hardness of Cooked Bananas

2018· article· en· W2896897760 on OpenAlexvenueno aff
Samuel Gafuma, Diriisa Mugampoza, George William Byarugaba-Bazirake

Bibliographic record

VenueJournal of Food Research · 2018
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
FundersAfrican Development Bank Group
KeywordsPectinStarchFood scienceChemistryMiddle lamellaTexture (cosmology)Retrogradation (starch)Cell wallBiochemistryAmylose

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.131
GPT teacher head0.404
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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