Food Puree for Seniors: The Effects of XanFlax as a New Thickener on Physicochemical and Antioxidant Properties
Bibliographic record
Abstract
With the increasing number of older adults, the elderly-friendly food market has been rapidly growing. The physicochemical and antioxidant properties of soymilk-based banana-blueberry-puree with and without flaxseed-based (XanFlax) and xanthan-gum-based (brand G) thickeners were compared as a potential senior food. Samples included a control, three treatments with XanFlax (1%, 3%, and 5%), and three treatments with brand G (1.35%, 2.7%, and 5.4%). The physicochemical (color, sugar, salinity, pH, viscosity, and hardness) and antioxidant properties [DPPH, ABTS, reducing power (RP), and total polyphenol content (TPC)] were compared. The chromaticity values (L*, a*, and b*) and pHs were similar among all treatments and the control, but the salinity of brand G showed statistical differences (p < 0.05). All samples met the Korean Industrial Standards for senior foods in terms of viscosity and hardness, while samples with brand G were harder and more viscous than those with XanFlax and the control (p < 0.001). XanFlax samples had greater ABTS radical scavenging activities than the control and brand G samples (p < 0.001). Although, the developed puree can be a possible senior food product without the addition of thickeners, XanFlax might be applied as a non-xanthan gum-based viscosity thickener with antioxidant functions for senior-friendly foods.
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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 | 0.000 | 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".