Sodium reduction in crackers: optimization of process to keep sensory quality without technological impacts
Bibliographic record
Abstract
Excess sodium in foods is one of the factors in chronic non-communicable diseases whose importance is on the rise. Thus, the aim of this study was to optimize a replacement for sodium in an appetizer-type Mignon cracker on an industrial scale. For this, a mixture design consisting of seven formulations were prepared with sodium replacement ranging between 30 and 60 %. The partial sodium replacement used industrial ingredients (Nutek Salt and PuraQ NA4, and modified KCl and flavor), to assess the impact on sodium content and texture (hardness). No significant differences were found in the hardness attribute. Sodium reduction ranged from 943.43 to 637.21 mg 100 g–1, and formulation 7 (F7) with 60 % replacement could cash in on the “Reduced in sodium” appeal. A sensory Quantitative Descriptive Analysis accessed the sensory profiles of formulations, and significant differences were observed (p < 0.05) in salty taste, sweet taste, bread aroma, and formulation 4 (40 % replacement) but were not significantly different from the formulation in salty taste. In PCA, the first main component showed variability between samples of 84.6 %, while the second axis explained 11.5 % of this variability. Acceptance (taste and overall quality) and purchase intention (above > 70 %) showed that the substitution did not affect consumers’ perceptions, with no significant difference between controls, F4 and F7.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".