Quantification of emulsifier adsorption onto sugar crystals dispersed in vegetable oil and associated effects on flow behaviour
Bibliographic record
Abstract
• New method to quantify emuslfier adsoprtion in confectionery products. • Method based on interfacial tension of residual emulsifier in extracted fat phase. • Emulsifier effect on viscosity & sedimentation of sugar-in-oil suspensions. • Concentration effects of four industrially-relevant surfactants tested. • Association between molecular structure, viscosity & sedimentation. Emulsifiers play an essential role in the flow behaviour of confectionery products such as chocolate. This research associated emulsifier adsorption onto sugar crystals and its effects on the flow properties of model sugar-in-oil suspensions. A new method to quantify emulsifier adsorption onto sugar crystals dispersed in oil was developed by exploiting the relationship between oil–water interfacial tension and unadsorbed emulsifier in the continuous oil phase. The model system consisted of 30 wt% sugar-in-oil suspensions to which were added up to 1 wt% soy lecithin, ammonium phosphatides (AMP), citric acid esters of mono- and diglycerides (CITREM) or polyglycerol polyricinoleate (PGPR). The link between sugar crystal surface coverage, sedimentation, aggregation state and apparent viscosity was then investigated. The lecithin, AMP and CITREM showed the largest decrease in apparent viscosity when added to the suspension at 0.05 to 0.1 wt%, which corresponded to their critical micelle concentration. The largest decrease in sugar crystal aggregation and sedimentation was also evident at this emulsifier concentration. By contrast, addition of PGPR led to a continuous decrease in viscosity and aggregation in the sugar suspension as a function of concentration, with no evident optimal concentration observed. Key advantages of this new method are its simplicity and ability to quantify emulsifier concentration in dispersed systems irrespective of emulsifier identity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".