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Record W2904420956 · doi:10.1111/1750-3841.14419

Sodium Chloride Suppresses the Bitterness of Protein Hydrolysates by Decreasing Hydrophobic Interactions

2018· article· en· W2904420956 on OpenAlexaff
Qingbiao Xu, Hui Hong, Wenlin Yu, Xu Jiang, Xianghua Yan

Bibliographic record

VenueJournal of Food Science · 2018
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsUniversity of Alberta
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsHydrolysateChemistryFood scienceSodiumHydrophobic effectZeta potentialSalting outHydrolysisFlavorEgg whiteChromatographyBiochemistryOrganic chemistryAqueous solutionChemical engineering

Abstract

fetched live from OpenAlex

The formation of bitter off-flavor is a long-existing issue during food protein hydrolysis. The aim of this study is to determine the mechanism of sodium chloride (NaCl) suppressing the bitterness of protein hydrolysates. In this study, the bitterness of egg white hydrolysate (EWH) and hen meat hydrolysate (HPH) was determined using an electronic tongue. The results showed that the bitterness intensity of quinine hydrochloride, EWH, and HPH was suppressed significantly by NaCl in a concentration-dependent manner (P < 0.05). The particle sizes, turbidity, zeta potentials, and surface hydrophobicity of EWH and HPH were also significantly decreased by NaCl at concentrations of 0.05, 0.1, 0.3, and 0.5 M (P < 0.05). These results indicated that adding NaCl at certain concentrations led to a salting-in effect, burying hydrophobic groups and decreasing the surface hydrophobicity of peptides, resulting in the decrease of bitterness. Using NaCl is an alternative, effective, and cheap strategy to suppress protein hydrolysate bitterness by decreasing hydrophobic interactions in food industry. PRACTICAL APPLICATION: NaCl can be used as an effective bitterness masker for food protein hydrolysates by decreasing hydrophobic interactions of peptides.

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 score0.004
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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
Published2018
Admission routes1
Has abstractyes

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