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Record W2091217864 · doi:10.1021/jf0116956

Principal Component Similarity Analysis of Raman Spectra To Study the Effects of pH, Heating, and κ-Carrageenan on Whey Protein Structure

2002· article· en· W2091217864 on OpenAlexaff
Nooshin Alizadeh-Pasdar, Shuryo Nakai, Eunice C.Y. Li‐Chan

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRaman spectroscopyWhey proteinPrincipal component analysisChemistryBovine serum albuminWhey protein isolateBeta-lactoglobulinCarrageenanAnalytical Chemistry (journal)CrystallographyChromatographyFood science

Abstract

fetched live from OpenAlex

Raman spectroscopy was used to elucidate structural changes of beta-lactoglobulin (BLG), whey protein isolate (WPI), and bovine serum albumin (BSA), at 15% concentration, as a function of pH (5.0, 7.0, and 9.0), heating (80 degrees C, 30 min), and presence of 0.24% kappa-carrageenan. Three data-processing techniques were used to assist in identifying significant changes in Raman spectral data. Analysis of variance showed that of 12 characteristics examined in the Raman spectra, only a few were significantly affected by pH, heating, kappa-carrageenan, and their interactions. These included amide I (1658 cm(-1)) for WPI and BLG, alpha-helix for BLG and BSA, beta-sheet for BSA, CH stretching (2880 cm(-1)) for BLG and BSA, and CH stretching (2930 cm(-1)) for BSA. Principal component analysis reduced dimensionality of the characteristics. Heating and its interaction with kappa-carrageenan were identified as the most influential in overall structure of the whey proteins, using principal component similarity analysis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.210

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 head0.015
GPT teacher head0.210
Teacher spread0.195 · 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

Citations41
Published2002
Admission routes1
Has abstractyes

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