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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".