The physico-chemical properties of commercial canola protein isolate-guar gum gels
Bibliographic record
Abstract
Abstract Biopolymer mixtures impart desirable texture to foods. Dynamic rheology was used to characterize canola protein isolate (CPI)-guar gum gels. The effects of pH, salt, guar gum and protein concentrations on the gelling ability of CPI were evaluated. Factorial and response surface optimization models were used to identify the optimum conditions (20%, w/v CPI; pH 10; 1.5%, w/v guar gum; 0.05 m NaCl) that would simultaneously maximize G′ (≥28 000 Pa) and minimize tan δ (<0.17) values of CPI-guar gum gels. Although pH > 8 is unconventional in food systems, strong and elastic CPI-guar gum gels (G′ =56 440 Pa; tan δ = 0.18) were produced at pH 10, whereas gels prepared at pH 6 were less elastic (G′ = 2726 Pa; tan δ = 0.2). Under the optimum conditions, CPI alone formed a stronger gel (G′ = 64 575 Pa; tan δ = 0.15) than CPI-guar gum mixture, suggesting that guar gum interfered with protein gelation.
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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 | 0.001 |
| 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".