Microstructure, Physical Stability, and Rheological Properties of Salad Dressing Emulsions Supplemented with Various Pulse Flours
Bibliographic record
Abstract
Significant opportunities exist for using pulses in the development of health promoting foods as consumers increasingly look for functional foods with disease prevention qualities. Pulse ingredients could be considered for use in the development of novel, value-added products such as salad dressing. In this study, the rheological properties, color, physical stability, and microstructure of model salad dressing emulsions supplemented with various types of pulse flours (red lentil, green lentil, desi chickpea, kabuli chickpea and yellow pea) were evaluated. Supplementation with pulse flours significantly increased (<em>P</em> &lt; 0.05) the consistency coefficient (<em>m</em>) and decreased (<em>P </em>&lt; 0.05) the flow behavior index (<em>n</em>) of the control dressing in accordance with the power law modelduring steady state flow tests. The pulse-supplemented dressings also showed increased recoverable strain (i.e., increased <em>Q(t)</em>% values) compared with the control. The observed rheological results were supported by scanning electron microscope (SEM) observations, where a more compact and uniform network was observed for supplemented dressings in comparison with the control sample. Addition of pulse flour increased the physical stability of the salad dressing emulsions and also modified color by increasing yellowness and redness hues depending on the type of flour used. This study, thus, demonstrated that pulse flours hold promise as ingredients that could be used in salad dressing formulations.
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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 | 0.000 |
| 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) | 0.000 | 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".