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Record W4378226861 · doi:10.3390/foods12112109

Utilization of Intermediate Wheatgrass (Thinopyrum intermedium) as an Innovative Ingredient in Bread Making

2023· article· en· W4378226861 on OpenAlexaff
Buket Çetiner, Владимир Шаманин, Zeynep Hazal Tekin‐Cakmak, Inna Pototskaya, Filiz Köksel, С. С. Шепелев, Amanzhol N. Aydarov, Bayram Özdemir, Alexey Morgounov, Hamit Köksel

Bibliographic record

VenueFoods · 2023
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Manitoba
FundersMinistry of Science and Higher Education of the Russian Federation
KeywordsIngredientFood scienceGlutenBread makingChemistryWheat flourMathematics

Abstract

fetched live from OpenAlex

), a nutritionally dense and sustainable crop, is a promising novel ingredient in bakery applications. The main aim of this study was to investigate the potential of IWG as a novel ingredient in breadmaking. The second aim was to investigate the characteristics of breads substituted with 15, 30, 45, and 60% IWG flour compared to control bread produced using wheat flour. The gluten content and quality, bread quality, bread staling, yellow pigment, and phenolic and antioxidant properties were determined. Enrichment with IWG flours significantly affected the gluten content and quality and bread characteristics. Increased levels of IWG flour substitution significantly decreased the Zeleny sedimentation and gluten index values and increased the dry and wet gluten contents. The bread yellow pigment content and crumb b* colour value increased with the increasing level of IWG supplementation. IWG addition also had a positive effect on the phenolic and antioxidant properties. Bread with 15% IWG substitution had the highest bread volume (485 mL) and lowest firmness values (654 g-force; g-f) compared to the other breads, including the control (i.e., wheat flour bread). The results indicated that IWG has great potential to be used in bread production as a novel, healthy, and sustainable ingredient.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.062
GPT teacher head0.349
Teacher spread0.287 · 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
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

Citations27
Published2023
Admission routes1
Has abstractyes

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