Study of chemical stability of lemon oil components in sodium caseinate–lactose glycoconjugate-stabilized oil-in-water emulsions using solid-phase microextraction-gas chromatography
Bibliographic record
Abstract
A headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC/MS) method was developed to quantify lemon oil components and their degradation products in oil-in-water (O/W) emulsions prepared with sodium caseinate-heated-lactose (NaC-T + Lact) glycoconjugates as wall materials at two pH values (3.0 and 6.8). NaC-T + Lact conjugates had a significantly lower solubility at both pHs. Hydrolysis prior to glycation enhanced the solubility of glycoconjugates. Glycation with lactose did not improve the emulsion activity of NaC, while caseinate glycoconjugates showed much stronger antioxidant activity than the NaC-control sample. This might be due to the presence of melanoidins formed between the sugar and amino acid compounds as supported by the increase in browning intensity. Among the SPME-fibres tested, carboxen/polydimethylsiloxane (CAR/PDMS) provided better results in terms of sensitivity and selectivity for oil lemon components and their degradation products. Storage studies of these emulsions demonstrated that glycated NaC-T + Lact showed protection against peroxidation compared to the control. However, acidic pH conditions altered their stability over storage time. The major off-flavor components (α-terpineol and carvone) were inhibited in emulsions stabilized with glycated NaC, particularly at pH 6.8. The use of NaC-T + Lact conjugates showed improved encapsulation efficiency and stability and could be used as potential food ingredient-emulsifiers for stabilising citrus oils against oxidative degradation in food and beverage applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".