Flaxseed Gum Solution Functional Properties
Bibliographic record
Abstract
L.) meal production that is useful as a food thickener, emulsifier, and foaming agent. FG is typically recovered by hot-water extraction from flaxseed hull or whole seed. However, FG includes complex polymer structures that contain bioactive compounds. Therefore, extraction temperature can play an important role in determining its functional properties, solution appearance, and solution stability during storage. These characteristics of FG, including FG quality, determine its commercial value and utility. In this study, FG solution functional properties and storage stability were investigated for solutions prepared at 70 and 98 °C. Solutions of FG prepared at 98 °C had lower initial viscosity than solutions extracted at 70 °C; though the viscosity of these solutions was more stable during storage. Solutions prepared by extraction at both tested temperatures exhibited similar tolerance to 0.1 mol/L salt addition and freeze-thaw cycles. Moreover, the higher extraction temperature produced a FG solution with superior foaming and emulsification properties, and these properties were more stable with storage. Foams and emulsions produced from FG extracted at higher temperatures also had better stability. FG extracted at 98 °C displayed improved stability and consistent viscosity, foamability, and emulsification properties in comparison to solutions prepared at 70 °C. Therefore, the FG solution extracted at 98 °C had more stable properties and, potentially, higher commercial value. This result indicates that FG performance as a commercial food additive can influence food product quality.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".