A homogeneity evaluation method of food soft matter based on moisture content test by casting-near-infrared spectroscopy
Bibliographic record
Abstract
Quantitative evaluation of polymer dispersion homogeneity is crucial for understanding the physicochemical behavior of food soft matter and ensuring processing stability. In this study, a horizontal casting device integrated with a near-infrared (NIR) spectrometer was developed to obtain spatially resolved reflectance spectra during casting. Characteristic absorption bands at 1180 and 1260 nm were identified as moisture-sensitive indicators of structural evolution. Partial least squares (PLS) and principal component analysis (PCA) were employed to construct predictive models for moisture-related spectral responses and to evaluate homogeneity across radial positions. PLS consistently outperformed PCA, yielding higher predictive accuracy ( R 2 > 0.85) and lower error (<2%). These findings demonstrate that NIR-based spectral mapping enables real-time characterization of hydration uniformity and compatibility within soft matter dispersions. This approach offers practical value for process monitoring, formulation optimization, and quality control in hydrocolloid-based food manufacturing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".