Stabilisation mechanism of various inulins and hydrocolloids: Milk–sour cherry juice mixture
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Bibliographic record
Abstract
Milk–fruit juice mixtures, such as the mainly acidic nutraceutical soft drinks, usually suffer from phase separation due to aggregation of caseins at low pH. In this study, short‐chain inulin (SCI), native inulin (NI), long‐chain inulin (LCI) and a combination of long‐ and short‐chain inulins (LCI:SCI) (MIX) in different ratios (20:80, 50:50 and 80:20) were added (up to 10% w/v) to a milk–sour cherry juice mixture and their stabilisation mechanisms investigated using rheological, microstructural and zeta potential observations. In addition, gum tragacanth (GT) and Persian gum (PG) as adsorbing and guar gum (GG) as nonadsorbing hydrocolloids were combined with inulin to enhance their stabilising properties. Finally, sensory analyses were carried out on the stabilised samples. According to our findings, LCI fully stabilised the mixture (8% w/v), while LCI: SCI and NI only reduced phase separation at very high concentrations, and SCI had no significant effect on the stabilisation. Moreover, no inulin aggregates and rheological changes were observed with SCI. However, LCI, LCI: SCI and NI formed inulin aggregates and the mixtures became even more viscous and thixotropic (LCI > LCI: SCL > NI). Based on these observations, it can be concluded that chain length and concentration are two important factors that affect the functionality of inulin. On the other hand, the combination of inulin with GT and PG did not have any pertinent effect on the stabilisation. However, the mixture of inulin and GG could stabilise the mixtures at certain ratios and concentrations. Furthermore, in mixtures containing GG and SCI, GG played the main role in the stabilisation by increasing the viscosity and forming gel network.
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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 | 0.002 |
| 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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it