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Record W2521473484 · doi:10.1094/cchem-07-16-0185-r

Chemical and Physical Characteristics of Proso Millet (<i>Panicum miliaceum</i>)‐Based Products

2016· article· en· W2521473484 on OpenAlexafffund
Matthew B. McSweeney, Koushik Seetharaman, D. Dan Ramdath, Lisa M. Duizer

Bibliographic record

VenueCereal Chemistry · 2016
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of GuelphAgriculture and Agri-Food CanadaAcadia University
FundersMinistry of Rural AffairsOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsPanicum miliaceumFood scienceGluten freeChemistryStarchGlutenGlycemic indexAgronomyGlycemicBiotechnologyBiology

Abstract

fetched live from OpenAlex

Celiac disease and gluten sensitivities, as well as obesity and overweight‐related disorders, have led to the investigation of gluten‐free grains and development of new food products. To address this, refined proso millet and refined corn (control), both gluten‐free grains, were used to produce four different product types (muffin, couscous, extruded snack, and porridge). The products contained four different grain combinations (100% proso millet, 75% proso millet/25% corn, 25% proso millet/75% corn, and 100% corn). All products were evaluated for their nutritional composition, in vitro starch digestibility, and expected glycemic index (eGI). Products made with refined proso millet had increased protein (7.6–11.3%), lipid (1.2–6.1%), fiber (7.0–8.8%), and phenolic content (323.5–425 μg/g) compared with those incorporating corn flour (2.5–9.0%, 0.8–4.0%, 2.1–4.1%, and 213–315 μg/g, respectively). As the proso millet content increased, the eGI decreased significantly ( P &lt; 0.05). Products made from refined proso millet appear to be good candidates for producing low‐GI, gluten‐free foods.

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 score0.008
Threshold uncertainty score0.422

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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

Citations36
Published2016
Admission routes2
Has abstractyes

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