Modified quality of seasoning syrup for coating and enhancing properties of a food model using xanthan gum
Bibliographic record
Abstract
In the food industry, the quantity of seasoning syrup coating on the food surface plays an essential role in determining final product quality. The effect was investigated of xanthan gum (Xan; 0%, 0.1%, and 0.2%) on the rheological properties of seasoning syrups (35% and 45% sucrose) at different temperatures (25 °C, 35 °C, 45 °C, and 55 °C). The syrups containing Xan exhibited shear-thinning behavior ( n < 1). The syrup viscosity increased with increasing Xan and sucrose concentrations but decreased with temperature. A regression model was developed for predicting syrup viscosity from Xan, sucrose, and temperature and showed good predictability. A dried, thin-sheet squid sample was used as a snack model for syrup coating. The syrup pickup increased as a function of the viscosity and approach plateau after 300% pickup. Xan enhanced the amount of syrup coating and total soluble solids ( p < 0.05) but the water activity and moisture content values did not differ significantly ( p > 0.05) among the samples with and without Xan. The results indicated that Xan could be used in the food industry to enhance the quality of syrup in terms of the viscosity for syrup pickup and the final quality of the product.
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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".