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Record W2964304470 · doi:10.1002/cche.10196

Structural characterization of intermediate wheatgrass (<i>Thinopyrum intermedium</i>) starch

2019· article· en· W2964304470 on OpenAlexaff
Yingxin Zhong, Juan Mogoginta, Joseph Gayin, George A. Annor

Bibliographic record

VenueCereal Chemistry · 2019
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsStarchAmyloseChemistryDextrinFood scienceMathematicsBotanyBiology

Abstract

fetched live from OpenAlex

Abstract Background and objectives Intermediate wheatgrass ( Thinopyrum intermedium , IWG), a perennial, is currently being developed into a food crop that can be used in mainstream food systems. This study focused on characterizing starches extracted from IWG kernels harvested in Roseau (IWG‐RS) and Rosemount (IWG‐RM) in Minnesota, USA, and compared with starches from Jasmine rice (JR) and hard red wheat (HRW). Thermal properties, size distribution, granule size and morphology, and the unit and internal chain profile of extracted starches were evaluated. Findings The amylose contents of IWG‐RS and IWG‐RM were 30.7% and 30.4%, respectively. IWG starches had the lowest gelatinization temperatures. Enthalpy of gelatinization (Δ H ) of HRW was similar to that of IWG‐RM. The λ max of the starches suggests that the amylose chains and internal chains of the IWG starches were longer than those of HRW and JR. IWG‐RM has the least β‐limit dextrin and longer external chain. Unit and internal chain profiles of amylopectins between IWGs were similar. Conclusions This study revealed that the properties of IWG starches were similar to those of wheat. Differences in some starch properties were also observed between the IWG grown at different locations. Significance and novelty This is the first report on the unit and internal chain profile of IWG. Understanding the microstructure of starch from IWG can potentially optimize its chemical functionality.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.020
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

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)0.0010.000

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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 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

Citations7
Published2019
Admission routes1
Has abstractyes

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