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Record W2886379353 · doi:10.5539/gjhs.v10n9p45

Understanding the Amino Acid Profile of Whey Protein Products

2018· article· en· W2886379353 on OpenAlexvenueno aff
Kantharuben Naidoo, Rowena Naidoo, Varsha Bangalee

Bibliographic record

VenueGlobal Journal of Health Science · 2018
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
FundersNIH Office of the DirectorNational Institute of Mental HealthCommon FundFogarty International CenterNational Institutes of Health
KeywordsLabellingAmino acidWhey proteinHealth claims on food labelsFood scienceFood productsFood industryBusinessEssential amino acidBiotechnologyChemistryBiochemistryBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The South African dietary supplement market will undergo a period of transition within the next few years due to the establishment of the South African Health Products Regulatory Authority (SAHPRA), which has superseded the former Medicines Control Council (MCC). While regulatory steps are yet to be fully outlined, products such as whey protein, regarded as food, will be governed by the Department of Health R429 draft Regulations Relating to the Labelling and Advertising of Foods. The guideline provides for the minimum value of essential amino acids (plus cysteine and tyrosine) per gram of protein that products claiming to contain protein will be required to comply with. Determining the compliance levels of whey protein products currently available will assist in establishing the readiness of the dietary supplement industry for regulation, and provide an indication of the overall state of the industry.OBJECTIVES: To determine the amino acid profile of whey protein powder and compare analysed content to manufacturer stated content.To compare analysed amino acid content to the Department of Health R429 draft Regulations Relating to the Labelling and Advertising of Food template amino acid profile.METHOD: 15 of the best-selling whey protein products available in South Africa were selected for amino acid analysis. Tested amino acid content were compared to the label stated claim and the amino acid reference pattern, as stated in the Department of Health R429 draft Regulations Relating to the Labelling and Advertising of Foods.RESULTS: Sixty percent (60%) of products tested were non-compliant with the Department of Health R429 draft Regulations Relating to the Labelling and Advertising of Foods. Of the 15 products tested, 11 were manufactured in South Africa, with 8 being non-compliant to the guideline amino acid profile. Considerable variance was noted in the manufacturer stated and the tested amino acid content (ranging from 16–48% variance).CONCLUSION: Many of the whey protein products available in South Africa are not compliant to proposed industry guidelines. The considerable variance noted highlights the need for greater oversight of the industry with clearly defined regulatory procedures.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.107
GPT teacher head0.364
Teacher spread0.257 · 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 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

Citations12
Published2018
Admission routes1
Has abstractyes

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