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Record W2799303593 · doi:10.1002/cche.10054

Effect of tempering moisture and infrared heating temperature on the functionality of Desi chickpea and hull‐less barley flours

2018· article· en· W2799303593 on OpenAlexaff
Tian Bai, Andrea K. Stone, Michael T. Nickerson

Bibliographic record

VenueCereal Chemistry · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTemperingMoistureChemistryInfraredFood scienceSolubilityInfrared heaterEmulsionWater contentMaterials scienceComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Background and objectives The effect of seed tempering moisture (20% moisture content or left un‐tempered) and infrared heating surface temperature (115 or 135°C) on the functional properties of Desi chickpea and hull‐less barley was investigated. Findings Tempering and infrared heating reduced the protein solubility in both flours, whereas the ability to bind oil was unaffected. Both flours had increased water binding abilities in response to tempering and infrared heating. In the case of chickpea flour, the emulsion activity (EA) increased when the seeds were tempered and heated to 135°C, whereas both emulsion and foaming stability remained unchanged with all processing conditions. Tempering before infrared heating decreased the foaming capacity of chickpea flour. In contrast, barley flour showed a decrease in both EA and stability, and became nonfoaming with infrared heating with and without tempering. Conclusions Infrared heating has both positive and negative effects on the functional properties of Desi chickpea and hull‐less barley flours. Significance and novelty Findings from this work will help direct ingredient processors to process seeds to achieve different functionalities which is important for food product development purposes.

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 score0.080
Threshold uncertainty score0.163

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)0.0000.000

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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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