Formula Optimization of a Low‐fat Food System Containing Whey Protein Isolate‐ Xanthan Gum Complexes as Fat Replacer
Bibliographic record
Abstract
ABSTRACT Whey protein isolate‐xanthan gum complexes (WPXC) have the potential to significantly reduce fat use in various products. However, their stability and functionality have not been extensively studied, particularly in neutral pH products. The objective of this study was to evaluate WPXC as a fat replacer in cake frostings and sandwich cookie fillings with reduced‐fat content (160 or 80 g/kg fat). Response surface methodology was used to analyze the effect of WPXC on the viscosity and textural response attributes of samples and to optimize the low‐fat formulations. It was found that WPXC had a positive effect ( P > 0.001), providing acceptable viscosity and texture attributes to the low‐fat samples. A significant moisture‐WPXC interaction ( P > 0.001) revealed that the optimum moisture‐WPXC ratio varied, depending on the targeted texture. This ratio was about 8:1 for cake frostings and about 5:1 for sandwich cookie fillings. Optimal formulations were found, and samples meeting the desirability specifications presented textural and melting profiles similar to those of the control products. However, for the production of sandwich cookie fillings, the use of other ingredients (for example humectants and emulsifiers) must be considered to maintain a low water activity (A w ) and prevent moisture migration to the cookie shell.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".