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Record W4401715728 · doi:10.1080/10408398.2024.2388279

The solid state and nanostructure of starch: Effects on starch functionality

2024· review· en· W4401715728 on OpenAlexafffund
Henry A. Koekuyt, Alejandro G. Marangoni

Bibliographic record

VenueCritical Reviews in Food Science and Nutrition · 2024
Typereview
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAmylopectinAmyloseCrystallinityStarchRetrogradation (starch)Chemical engineeringCrystallizationMaterials scienceMaize starchChemistryFood scienceCrystallographyOrganic chemistry

Abstract

fetched live from OpenAlex

In order to determine suitable end use applications for different starches, this review characterizes and differentiates the physical components, solid state, crystalline structures, and their effects on gelatinization, retrogradation, texture and functionality. There exist four crystalline packings of starch. A-, B- and C-type packings are attributed to amylopectin, and V-type which is attributed to amylose. B- and C- type crystallinity rely on water to help coordinate their crystal structures due to the congregation of water in the large intrahelical cavity of the B-type packings. The ratio of amylose to amylopectin content largely affects the textural and functional properties of starch. Amylose largely influences retrogradation, and thus can largely impact the crystallinity, strength, cohesion and brittleness of starch gel systems. Amylose has been found to crystallize prior to amylopectin, suggesting that amylose acts as a nucleation site for further radial crystallization of amylopectin. Processing treatments such as size reduction and drying, which are typically applied to all commercial starches, also impact the physiochemical and functional characteristics of the starch. These processes can cause damage to the starch granule while reducing crystallinity in the native starch, but also increasing retrogradation in gelatinized systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.103
GPT teacher head0.404
Teacher spread0.301 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations14
Published2024
Admission routes2
Has abstractyes

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