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Record W2988836911 · doi:10.1177/1082013219887635

Effect of barrel temperature and feed moisture on protein quality in pre-cooked Kabuli chickpea, sorghum, and maize flours

2019· article· en· W2988836911 on OpenAlexaff
Shuyang Wang, Matthew G. Nosworthy, James D. House, Shannon-Hood Niefer, Michael T. Nickerson

Bibliographic record

VenueFood Science and Technology International · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsUniversity of ManitobaUniversity of Saskatchewan
Fundersnot available
KeywordsSorghumMoistureProtein qualityLimitingFood scienceExtrusionAgronomyBiologyChemistryMaterials science

Abstract

fetched live from OpenAlex

The effect of barrel temperature (120 and 150 ℃, held constant in zones 4-6) and feed moisture (20 and 24%) on the protein quality of Kabuli chickpea, sorghum, and maize flours were examined, which included amino acid profile, in vitro protein digestibility and in vitro protein digestibility-corrected amino acid score (IV-PDCAAS). It was found that the limiting amino acid of chickpea changed from threonine to valine after extrusion, whereas both sorghum and maize were limiting in lysine before and after extrusion. The in vitro protein digestibility increased from 77 to 81% for chickpea and 73 to 76% for maize; values for sorghum remained at 74% after extrusion. However, the IV-PDCAAS for the extruded flours generally remained at the same level, 69% for chickpea, 22% for sorghum, and ∼35% for maize. The effect of extrusion temperature, moisture and their interaction was significant on protein quality of sorghum and maize, but in the case of chickpea, only the extrusion temperature was significant. Only chickpea extruded at 150 ℃ (regardless of the moisture) met the protein quality (PDCAAS > 70%) requirement to be used in food assistance products.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.188

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 head0.005
GPT teacher head0.228
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 designObservational
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

Citations20
Published2019
Admission routes1
Has abstractyes

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