MétaCan
Menu
Back to cohort
Record W7082375463 · doi:10.1016/j.foodhyd.2025.111974

Unraveling microstructure and water behavior in diverse food matrices using low-frequency NMR (LF-NMR) on proton: a specific look at 1H-LF-NMR results interpretation

2025· article· en· W7082375463 on OpenAlexafffund

Bibliographic record

VenueFood Hydrocolloids · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l'Économie, de la Science et de l'Innovation - QuébecNovalaitMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsMicrostructureRelaxation (psychology)MacromoleculeComplex matrixCharacterization (materials science)Molecular dynamicsRheology

Abstract

fetched live from OpenAlex

Low-frequency NMR on proton ( 1 H-LF-NMR) has become a method of choice to study water mobility in food matrices. It has been applied on matrices varying in complexity such as sucrose solutions and complex hydrocolloid mixed systems. Measurements of spin-spin relaxation time (T 2 ) using Carr-Purcell-Meiboom-Gill scan sequences are often used to study hydrated matrices with long relaxation times (>1ms). Studies reported 1 to 4 water populations among which “bound water”, bulk water from serum, and separated water (sedimentation, syneresis). Experiments from our team on different food systems and results from the literature will be used to demonstrate how 1 H-LF-NMR can probe food microstructure and the care needed to avoid artifacts. Examples were taken among fermented milk, mixed polysaccharides, mixed protein-polysaccharide systems and legume purée. Water mobility shows similar patterns between these different matrices even though their type (suspension, gel, …), composition and microstructure are different in nature. For instance, in yogurt formulations the serum water mobility correlated with network heterogeneity. The serum water often gets most of the attention in studies as it can be affected by serum solutes, serum viscosity, and gel microstructure (porosity, macromolecular density). However, water mobility was also able to detect and quantify spontaneous serum separation or to detect microstructural heterogeneity due to segregative interactions. 1 H-LF-NMR when combined with other methods used for microstructure characterization allows to probe water interactions in both model and complex food systems. By revealing water interactions in food matrices, this method is a powerful, simple, and non-destructive tool to inform on macromolecular interactions and their organization. • 1 H-LF-NMR probes water mobility in food matrices. • In hydrated food matrices 1 to 4 water populations are found. • Water mobility is related to food microstructure. • 1 H-LF-NMR can detect phase separation and microstructural changes. • 1 H-LF-NMR complements information from rheology and microscopy.

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 categoriesMeta-epidemiology (narrow)
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 score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.237
Teacher spread0.224 · 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.

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

Citations7
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueFood HydrocolloidsSame topicGeochemistry and Geologic MappingFrench-language works237,207