OPTIMIZING THE TEXTURE ATTRIBUTES OF A FAT‐BASED SPREAD USING INSTRUMENTAL MEASUREMENTS
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Bibliographic record
Abstract
ABSTRACT Response surface methodology (RSM) was used to investigate the effects of a commercial stabilizer and hemp oil content on the texture of pumpkin seed oil press‐cake spreads, using a penetration test as part of instrumental texture profile analysis. The response variables were the most significant spread texture attributes: hardness, penetration work, elasticity and adhesiveness. Spreads were formulated according to a central composite, two factorial experimental designs on five levels. Both independent variables significantly affected the texture of the obtained spreads, which had an appearance and texture comparable to commercial peanut butter. In terms of the selected texture attributes determined by the instrumental analysis, the optimum combination of variables with 1–1.2% of added stabilizer and 20–40% of added hemp oil produced desirable spreads that mimic commercial peanut butter; however, they were not sticky. RSM helped to optimize the composition of the spreads to obtain the product with the minimum hardness, penetration work and adhesiveness with the maximum value for elasticity. PRACTICAL APPLICATIONS This paper describes how the texture attributes of a novel fat‐based spread can be predicted using instrumental measurements, specifically a penetration test, while having peanut butter as a control sample to set desired value boundaries for each texture attribute separately. The penetration test was used to predict the texture of the spreads, compared with commercial peanut butter, without conducting the expensive and time‐consuming sensory evaluation of the product. The use of the response surface methodology and mathematical models could describe and predict experimental data of the component's content and to optimize the variables that mostly affected the spreads' texture. The described process can help in designing the spreads based on the pumpkin seed oil press‐cake with optimum texture properties. This screening technique can help the food industry to select formulations when new products are developed without using sensory panels at the product designing stage.
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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 it