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Record W2021757420 · doi:10.1007/s11746-010-1601-2

Microscale Surface Roughening of Chocolate Viewed with Optical Profilometry

2010· article· en· W2021757420 on OpenAlexaff
Dérick Rousseau, Sopark Sonwai, Rizwan S. Khan

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDifferential scanning calorimetryMaterials scienceProfilometerSurface finishSurface roughnessPhase (matter)Isothermal processWavinessMicrostructureComposite materialTemperature cyclingChemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract Microtopographical roughening and fat phase melting of milk chocolate subjected to three temperature cycles between 20 and 28, 30, 32, or 34 °C were examined using optical profilometry and differential scanning calorimetry (DSC). Cycling to any of these temperatures did not lead to immediate visual bloom, though significant effects on microstructure and fat phase melting behavior were noted. The initial chocolate topography was lightly mottled and consisted of small asperities. DSC indicated the presence of form V crystals in control chocolates kept isothermally, with form VI crystals appearing with cycling to 30 and 32 °C. The fat phase of the chocolates cycled to 34 °C existed only in the form IV polymorph. As a result of cycling, the surface roughness of all samples increased, with the smallest rise seen with cycling to 28 °C. Decomposition of the roughness into low and high‐frequency components revealed a significant contribution of waviness (the low‐frequency component) to overall roughness, particularly with cycling to 34 °C. Furthermore, with the fat phase fully molten, the backbone structure consisting of the dispersed particulates also contributed to overall roughness. This study demonstrated that significant microstructural changes and deformation take place within chocolate as a result of temperature fluctuations prior to the onset of visible surface fat bloom.

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 score0.014
Threshold uncertainty score0.228

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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

Citations15
Published2010
Admission routes1
Has abstractyes

Explore more

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