Stabilisation mechanism of various inulins and hydrocolloids: Milk–sour cherry juice mixture
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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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 | 0.000 |
| 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".