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Record W3027518327 · doi:10.1111/ijfs.14626

Colour change with longitudinal compression supports hypothesis of multilayer interference as cause for meat iridescence

2020· article· en· W3027518327 on OpenAlexaff
Chiara Ruedt, Monika Gibis, Shai Barbut, Jochen Weiß

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Guelph
FundersForschungskreis der ErnährungsindustrieUniversität Hohenheim
KeywordsIridescenceInterference (communication)Reflection (computer programming)OpticsWavelengthMaterials scienceRefractionCompression (physics)ReflectivityStructural colorationOptoelectronicsComposite materialPhysicsTelecommunicationsPhotonic crystalChannel (broadcasting)Computer science

Abstract

fetched live from OpenAlex

Summary The mechanism of iridescence in meat and meat products is still not fully understood but a widely accepted hypothesis is that it originates from a multilayer interference from sarcomere discs. In multilayer interference, the reflected wavelength is affected by the refraction angles, the thicknesses and refractive indices of the intermittent layers. A variation of these factors should therefore cause a disappearance or shift of iridescent colours. To test this hypothesis, we progressively compressed iridescent rolled fillets of ham longitudinal to the long axes of the muscle fibres and measured the interference colours by reflection spectrophotometry. We observed an interference colour shift from longer to shorter wavelengths and reflection intensity decreased with increasing compression pressure. Our data indicate that the compression decreased the layer thickness, so that constructive interference occurred at shorter wavelengths. The results thus provide support for multilayer interference being a primary cause and mechanism for meat iridescence.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.143
GPT teacher head0.314
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2020
Admission routes1
Has abstractyes

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