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Record W2580416133 · doi:10.1080/10942912.2017.1283326

Amylose and amylopectin molecular fractions and chain length distribution of amylopectin in 12 varieties of Ghanaian sweet potato (<i>Ipomoea batatas</i>) flours

2017· article· en· W2580416133 on OpenAlexfundno aff
Charles Tortoe, Papa Toah Akonor, Kristine Koch, Carolin Menzel, Kwadwo Adofo

Bibliographic record

VenueInternational Journal of Food Properties · 2017
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
FundersfRI Research
KeywordsAmylopectinIpomoeaAmyloseDry matterStarchChemistryFood scienceConvolvulaceaeBotanyHorticultureBiology

Abstract

fetched live from OpenAlex

Sweet potato (Ipomoea batatas) is a commonly cultivated root crop in tropical and subtropical regions, including Ghana. Different varieties of sweet potato have been bred, in order to expand its utilisation within the food and industrial sector. This study analysed flours made from 12 recently developed Ghanaian sweet potato varieties in terms of their amylose and amylopectin molecular fractions and amylopectin chain length distribution. Starch content of the sweet potato flours ranged from 49 to 77 g/100 g dry matter, with 11 of the varieties containing above 60 g/100 g dry matter. An orange-fleshed variety, Apomuden, had the lowest amount of starch (48.9 g/100 g dry matter), while the cream-fleshed variety Histarch had the highest (77.3 g/100 g dry matter). The flours from the 12 sweet potato varieties had intermediate amounts of amylose, within the range 10–30 g/100 g dry matter, and showed typical molecular distribution according to size-exclusion chromatography (SEC). The fine structures of amylopectin, as revealed by anion-exchange chromatography, contained features common for starches of C-type X-ray pattern, but some structural differences were also observed.

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 score0.001
Threshold uncertainty score0.003

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 integrity0.0000.000
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 head0.021
GPT teacher head0.262
Teacher spread0.241 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

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