Preparation of Salad Dressing Emulsions Using Lentil, Chickpea and Pea Protein Isolates: A Response Surface Methodology Study
Bibliographic record
Abstract
Abstract The effect of pulse protein, egg yolk and oil contents on the physical properties (i.e., static and dynamic rheological behavior, texture) of lentil‐, pea‐ and chickpea‐supplemented salad dressings was studied using a three‐factor central composite design (CCD). Multiple regression equations were developed to describe the effects of the independent variables on several response variables. In general, an increase in oil and emulsifier (pulse protein or egg yolk) contents modified the rheological and textural properties and led to either a linear or a nonlinear increase in several parameters, including , m, ηap, σ0, Q(t)% and firmness. Response surface methodology was used to optimize the salad dressing formulations based on selected response variables, which were either maximized or minimized, or targeted using average values of parameters for several commercial salad dressings. The validation test confirmed the overall adequacy of the response surface models in predicting specific properties of the set formulations. Practical Applications The use of egg yolk as an emulsifier in the formulation of salad dressings may be a concern for those with high cholesterol. Proteins prepared from pulses may be promising value‐added replacements for such reduced egg‐yolk emulsion‐type food products. We have demonstrated the feasibility of making pulse protein‐supplemented salad dressings with physical properties similar to those of commercial dressings by varying levels of pulse protein, egg yolk and oil in salad dressing emulsion systems.
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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 | 0.000 |
| 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 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".