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Interaction of pea protein isolate with betanin in red beet (Beta vulgaris L.) extract: Influence on structure, antioxidant properties and stability at pH 3

2025· article· en· W4412013215 on OpenAlexafffund
Sonia Kumar, Jan K. Rainey, Roumiana Stefanova, Junzeng Zhang, Marianne Su‐Ling Brooks

Bibliographic record

VenueFood Chemistry · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Research and Applications
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBetaninAntioxidantChemistryFood scienceBETA (programming language)BiochemistryBotanyBiology

Abstract

fetched live from OpenAlex

Betanin, the red pigment in red beet extract (RBE), is susceptible to degradation under acidic conditions, limiting its application as a colorant and ingredient in food products. This study investigated complex formation between betanin in RBE with pea protein isolate (PPI) to improve the stability of betanin at pH 3. Spectroscopic analysis showed that the interactions between PPI and RBE affected the secondary and tertiary structure of PPI, and a transition from α-helical to β-sheet conformation occurred. Gel-electrophoresis confirmed that PPI proteins were involved in PPI-RBE interactions. Furthermore, the size of PPI-RBE particles was found to increase with an increase in RBE concentration. As the PPI-RBE complex had improved antioxidant activity and long-term refrigerated stability compared to the RBE control, this indicates that PPI-RBE complexes could expand the applicability of betanin as a natural colorant and antioxidant in acidic foods while also increasing nutritional value with the added pea protein.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.022
GPT teacher head0.233
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 source (direct Gemma or distilled Codex), 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

Citations6
Published2025
Admission routes2
Has abstractyes

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