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Record W1807781152 · doi:10.1094/cchem-12-14-0267-r

Effects of Monoglycerides of Varying Fatty Acid Chain Length and Mixtures Thereof on Sponge‐and‐Dough Breadmaking Quality

2015· article· en· W1807781152 on OpenAlexaffabout
Kazuki Hattori, Brigitte Dupuis, Bin Xiao Fu, N. M. Edwards

Bibliographic record

VenueCereal Chemistry · 2015
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsCanadian International Grains Institute
FundersRIKEN
KeywordsMonoglycerideChemistryFood scienceFatty acidVolume (thermodynamics)Organic chemistry

Abstract

fetched live from OpenAlex

Monoglycerides are widely used in the baking industry because of their antistaling effects, mainly suppressing crumb firming. Commercial monoglycerides are normally prepared from hydrogenated fats, with stearate being the most common fatty acid. In a previous study, monoglycerides such as monopalmitate (C16) and monostearate (C18) had positive effects on Canadian short process bread but no improvements on sponge‐and‐dough process (SDP) bread. The objective of this study was to investigate the effects of saturated monoglycerides of varying fatty acid chain length (C14–C22) on SDP breadmaking quality by using volume judgment, crumb image analysis, and texture measurements. Higher levels (1.00–1.50%) of all monoglycerides (C14, C16, and C18) significantly ( P < 0.05) increased loaf volume and cell diameter. The larger cell diameter with increasing levels of these monoglycerides may have resulted from softer, more extensible dough handling properties and greater gas cell stability during baking. Addition of C16 and C18 caused the largest increase in crumb softness with increasing monoglyceride levels but showed relatively low resilience, which might be related to larger loaf volume (i.e., lower density of bread). However, addition of blended monoglycerides C14+C16 increased crumb softness and loaf volume while partially retaining resilience. Each monoglyceride had a different function in breadmaking quality and somewhat positive effects on SDP.

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 score0.015
Threshold uncertainty score0.510

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 head0.030
GPT teacher head0.277
Teacher spread0.247 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

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