MétaCan
Menu
Back to cohort
Record W2032464243 · doi:10.1094/cchem-01-12-0003

Functional Attributes of Proteins Withdrawn from Different Stages of a Commercial Ethanol Fuel/Distillers Dried Grains with Solubles Process Using a Wheat Feedstock

2012· article· en· W2032464243 on OpenAlexafffund
Andrea K. Stone, Nicole A. Avramenko, Dawn A. Abbott, Hank Classen, Michael T. Nickerson

Bibliographic record

VenueCereal Chemistry · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - Saskatchewan
KeywordsCoproductChemistryRaw materialDistillers grainsFood scienceEmulsionEthanolEthanol fuelIngredientOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT The functional attributes of proteins withdrawn from different stages of a commercial ethanol fuel/distillers dried grains with solubles (DDGS) process that used a wheat feedstock were investigated. Specifically, protein coproducts were examined postliquefaction (PL), postdistillation (PD), and postdrying (DDGS) and related to those from the native wheat feedstock (WF). Water hydration, oil holding, foaming, and emulsifying properties of all materials were assessed. The WF and the PL coproduct showed similar water hydration capacities (WHCs), ranging between 80 and 110%, whereas WHCs for the PD and DDGS coproducts were significantly higher (ranging between 264 and 356%). WHC for all products was found to be independent over the pH range of 2.00–10.00. A similar trend was found for the oil holding capacities, for which both WF and PL materials showed lower values (≈120%) than both the PD (203%) and DDGS (171%) materials. Foam capacity and stability for the WF were found to be 32.8 and 61.4%, respectively, whereas no coproducts were foam forming. Emulsion stability decreased from ≈45% for the WF to <10% in all other materials. Findings indicated that initial changes to the protein's functional attributes occurred in the PL stage of ethanol/DDGS processing.

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 score0.026
Threshold uncertainty score0.428

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 head0.048
GPT teacher head0.241
Teacher spread0.192 · 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

Citations3
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueCereal ChemistrySame topicProteins in Food SystemsFrench-language works237,207