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Record W1972013252 · doi:10.1021/jf404995a

Galactose Content of Legumes, Caseinates, and Some Hard Cheeses: Implications for Diet Treatment of Classic Galactosemia

2014· article· en· W1972013252 on OpenAlexafffund
Sandra C. Van Calcar, Laurie Bernstein, Frances Rohr, Steven Yannicelli, Gerard T. Berry, Christine H. Scaman

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsGalactosemiaLactoseChemistryFood scienceGalactoseBiochemistry

Abstract

fetched live from OpenAlex

There are inconsistent reports on the lactose and/or galactose content of some foods traditionally restricted from the diet for classic galactosemia. Therefore, samples of cheeses, caseinates, and canned black, pinto, kidney, and garbanzo beans were analyzed for free galactose content using HPLC with refractive index or pulsed amperometric detection. Galactose concentrations in several hard and aged cheeses and three mild/medium Cheddars, produced by smaller local dairies, was <10 mg/100 g sample compared to 55.4 mg/100 g sample in four sharp Cheddars produced by a multinational producer. Galactose in sodium and calcium caseinate ranged from undetectable to 95.5 mg/100 g sample. Free galactose level in garbanzo beans was lower than previously reported at 24.6 mg/100 g sample; black beans contained 5.3 mg/100 g, and free galactose was not detected in red kidney or pinto beans. These data provide a basis for recommending inclusion of legumes, caseinate-containing foods, and some aged hard cheeses that had been previously restricted in the diet for individuals with galactosemia.

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 score0.228
Threshold uncertainty score0.328

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 head0.017
GPT teacher head0.228
Teacher spread0.211 · 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

Citations29
Published2014
Admission routes2
Has abstractyes

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