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Technical Note: Comparison of Chromatographic Profile of Glycomacropeptide from Cheese Whey Isolated Using Different Methods

2004· article· en· W1983137925 on OpenAlexafffund
Esther W. Y. Li, Yoshinori Mine

Bibliographic record

VenueJournal of Dairy Science · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Guelph
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaDairy Farmers of Ontario
KeywordsChemistryChromatographyUltrafiltration (renal)Ethanol precipitationFractionationElutionEthanolPrecipitationBiochemistryExtraction (chemistry)

Abstract

fetched live from OpenAlex

Glycomacropeptide (GMP) has heterogeneous carbohydrates, and this attributes to its various biological activities. In this study, we compared the chromatographic profiles of GMP isolated by three methods (trichloracetic acid fractionation, ethanol precipitation, and ultrafiltration) from whey protein isolate (WPI). Seven sharp heterogeneous GMP peaks were eluted from GMP prepared by ethanol precipitation and ultrafiltration using Mono Q anionic chromatography, while only 5 peaks were seen in TCA treated sample. The TCA pretreatment recovered only sialo-GMP (glycosylated) and eliminated all contaminated proteins; however, the recovery rate was the lowest (6.7% of the initial WPI). Ethanol precipitation recovered 20.4% of GMP from WPI and 75.7% was glycosylated, but the heating process might lead to degradation of glycosidic residues. Ultrafiltration was found to be the most effective in recovering GMP. The recovery rate was 33.9% with 81.6% sialo-GMP. We concluded that carbohydrate profile of GMP varied widely and depended on the isolation method. Based on the high recovery of sialo-GMP, the combination of ultrafiltration and anionic chromatography might be a suitable and practical approach on an industrial scale.

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)0.001
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 score0.055
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.024
GPT teacher head0.352
Teacher spread0.329 · 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

Citations36
Published2004
Admission routes2
Has abstractyes

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