The effect of sugar on the processing behaviour of palm oil: from confectionery fundamentals to predictive regression models
Bibliographic record
Abstract
This dissertation explores the impact of confectioner’s sugar and processing on key physical characteristics of palm oil. Though bulk oils such as palm oil have been well-studied, fat-continuous dispersions that include other ingredients such as sugar, cocoa solids, or milk powder are much more complex due to ingredient interactions and the resulting changes in properties such as fat crystal morphology and crystallization pathway. These implications restrict the ability to extrapolate responses from bulk oils towards multi-ingredient systems. This is particularly important given that palm oil in foods rarely exists in bulk but is usually mixed with multiple ingredients. The effects of processing on both bulk oil and oil-sugar blends were explored over four weeks of storage and clearly demonstrated that the existence of a dispersion resulted in large differences in rheology and texture compared to the bulk. Adding sugar significantly increased storage modulus and firmness of the oils while exhibiting increased sensitivity towards processing conditions. Predictive models were generated through multiple regression analysis that overcame limitations derived from extrapolation and corrected for these behavioural differences using a single binary variable accounting for the presence of confectioner’s sugar. As a result, the contributions of sugar to the rheology and texture of oil-sugar blends were modelled, a first in such systems. The results contained within this dissertation are of great industrial relevance as they: i) limit the dependence on anecdote and empiricism to explain results, ii) generate detailed process maps, iii) identify optimal process values to attain desirable rheological and textural responses, and iv) open the door for further investigation into dispersion effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".