Kinetic And Oxidative Stability Of Oil-In-Water Emulsions Prepared With Denatured Soy Whey Protiens And Soy Soluble Polysaccharides
Bibliographic record
Abstract
With increasing consumer awareness and growing demand for healthier processed food options, there is an ever-present push for the incorporation of nourishing ingredients into foods. Many health-promoting ingredients, for example Omega-3 fats, are prone to rancidity and are insoluble in water. A current challenge facing the Ontario agri-food sector is the addition of such ingredients that can normally be added to fatty foods, but not into water-based foods such as many store-bought beverages. Furthermore, oils such as flaxseed oil are also very sensitive to oxidation when in the presence of light, heat or air, resulting in the formation of undesirable odours and flavours as well as loss in nutritional properties. The use of food emulsions is considered an attractive approach to preserve their healthfulness while minimizing rancidity. The overall goal of the present thesis was to incorporate soybean or flaxseed oil as micron-sized droplets within water-continuous emulsions using biopolymers derived from soy industrial processing waste to help ‘protect’ the oil from visual phase separation and coalescence as well as oxidative rancidity. To meet the goal, an extraction protocol to purify and concentrate the soy whey proteins (SWP) was initially developed. This was followed by establishing a method to increase the surface activity of the SWP via denaturation (dSWP). Subsequently, emulsions consisting of soybean oil or flaxseed oil prepared with dSWP and commercially-available soy soluble polysaccharides (SSPS) were analyzed for their kinetic and oxidative stability. Results clearly showed that the combination of dSWP and SSPS could: i) kinetically stabilize model oil-in-water emulsions against coalescence and phase separation more so than dSWP or SSPS alone and ii) effectively protect emulsions containing flaxseed oil from oxidative rancidity to a greater extent than a commonly-used emulsifier (polysorbate 20). Overall, this thesis yielded a novel method to emulsify and protect polyunsaturated oils using soy-based proteins and polysaccharides. The outcomes of this study offer the attractive potential of using soy-based ingredients from industrial waste in value-added food products such as beverage-type emulsions. Findings from this study may be applied to non-food products where there is a need for the development and stabilization of emulsions (e.g., pharmaceutical, cosmetics).
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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 | 0.000 | 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 | 0.001 |
| 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".