Optimization of formulation and influence of environmental stresses on stability of lycopene-microemulsion
Bibliographic record
Abstract
The stability of two-layer lycopene-microemulsions and the degradation of lycopene in microemulsions subjected to thermal processing and under environmental stress were investigated. The formulation of microemulsions made with different ratios of whey protein isolate (WPI), high-methylester-pectin (HMP), and an oil phase containing lycopene was optimized. The oil volume fractions, concentrations of WPI and HMP, and their interactions significantly influenced the physical stability of lycopene-rich microemulsions. The two-layer microemulsion consisted of WPC and HMP was much more stable under environmental stress compared to the WPC one-layer microemulsions. The one-layer lycopene-microemulsions destabilized at low pH, but the two-layer lycopene-microemulsion became unstable when the pH was in the neutral range of 6.12 to 7.01. The stability of microemulsions declined with increasing NaCl concentrations. About 36.8 g/100 g, 23.2 g/100 g, and 11.1 g/100 g of the total lycopenes were lost in the oil-phase, the one-layer lycopene-microemulsions, and the two-layer lycopene-microemulsions after thermal treatments, respectively. The optimized microemulsions contained 0.2 g/100 g (w/w) whey protein concentrate (WPC), 0.5 g/100 g HMP (w/w), and 5 mL/100 mL oil phase fraction had the highest physical stability. The optimized lycopene-microemulsion was more stable to thermal treatment and changes of pH, but become sensitive to NaCl treatment.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".