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Record W4401869841 · doi:10.15586/ijfs.v36i3.2487

Trehalose–whey protein conjugates prepared by structural interaction: Mechanisms for improving the multilevel structure and their water solubility and protein digestibility

2024· article· en· W4401869841 on OpenAlexaff
Mohammad Alrosan, Motasem Al-Massad, Ali Al‐Qaisi, Sana Gammoh, Muhammad H. Alu’datt, Farah R. Al Qudsi, Thuan‐Chew Tan, Ammar A. Razzak Mahmood, Ali Almajwal

Bibliographic record

VenueItalian Journal of Food Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Guelph
FundersUniversiti Sains MalaysiaJordan University of Science and TechnologyKing Saud University
KeywordsSolubilityChemistryTrehaloseWhey proteinFood scienceConjugateChromatographyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Whey proteins (WPs) are the most widely used protein supplements worldwide. This study investigates the impact of incorporating trehalose into WPs at different ratios ranging from 1% to 5% (w/v) on the structural characteristics, surface properties, and functionality of trehalose (T) conjugated to WPs (T-WP). The T-WP conjugate was produced using the pH-shifting technique. Our findings demonstrated that conjugating trehalose into WPs significantly altered the Fourier-transform infrared (FTIR) spectrum, tertiary structure, and protein conformation. The surface charge and hydrophobicity of T-WPs were changed significantly (p < 0.05). Structural modifications had a notable effect on the solubility and digestibility of T-WPs. The water solubility of T-WPs increased from 88.12% to 95.53% when conjugated with 5% (w/v) trehalose. Furthermore, the impact of the development of T-WPs on FTIR spectrum was investigated. The β-sheet, random coil, α-helix, and β-turn were changed significantly from 36.17% to 45.21%, 12.38% to 16.39%, 10.69% to 13.44%, and 40.76% to 24.94%, respectively. The results presented in this study offer structural information to enhance the creation of WP products with improved functional properties.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.020
GPT teacher head0.239
Teacher spread0.220 · 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

Citations16
Published2024
Admission routes1
Has abstractyes

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