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Record W3043504963 · doi:10.1002/fsn3.1731

Oxidative stability of direct‐expanded chickpea–sorghum snacks

2020· article· en· W3043504963 on OpenAlexafffund
Esayas K. Bekele, Matthew G. Nosworthy, Carol J. Henry, P.J. Shand, Robert T. Tyler

Bibliographic record

VenueFood Science & Nutrition · 2020
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Saskatchewan
FundersGlobal Affairs CanadaInternational Development Research Centre
KeywordsFlavorSorghumFood scienceAromaShelf lifeChemistryPeroxide valuePeroxideAgronomyBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In contrast to other pulses, chickpea has a relatively high fat content (3%–10%). This study was designed to investigate direct‐expanded chickpea–sorghum extruded snacks (50:50, 60:40, and 70:30 chickpea:sorghum, w/w) with respect to: their oxidative stability and sensory properties during accelerated (55°C) and room temperature (25°C) storage; correlations between chemical markers (peroxide value and p ‐anisidine value) and sensory data during accelerated storage; and the shelf‐life of snacks extruded at the optimal expansion point as determined by a rotatable central composite design. Peroxide values and p ‐anisidine values were in the range of 0–2.5 mEq/Kg and 5–30, respectively, for both accelerated and room temperature storage, and increased during storage ( p < .05). 70:30 and 60:40 (w/w) chickpea–sorghum snacks had higher peroxide and p ‐anisidine values compared to the 50:50 snack during storage at either temperature ( p < .05). Rancid aroma and off‐flavor of 60:40 and 70:30 chickpea–sorghum snacks (slightly intense = 6) also were higher than that of the 50:50 snack (moderately weak = 3) ( p < .05). Significant correlations ( p < .05) were found between chemical markers and sensory attributes ( p < .05). The study illustrated that shelf‐life decreased as the percentage of chickpea in the blend increased. Therefore, in terms of shelf‐life, a 50:50 chickpea–sorghum blend is preferable.

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 score0.011
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.048
GPT teacher head0.271
Teacher spread0.223 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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