Understanding relations between rheology, tribology, and sensory perception of modified texture foods
Bibliographic record
Abstract
Abstract The aim of this work was to examine relations between instrumental and sensory parameters in a texture modified food matrix, with and without saliva. Nine pureed carrot samples (eight thickened and a control) were developed with starch (0.4 and 0.8% wt/wt), xanthan (0.2 and 0.4% wt/wt) or starch–xanthan blends that met International Dysphagia Diet Standardisation Initiative (IDDSI) Level 4 guidelines using fork and spoon tests. Rheological and tribological tests were conducted on the food and simulated bolus prepared by adding fresh stimulated saliva to the food (1:5, saliva:food) to mimic oral processing. Perceived sensory properties were identified using a temporal dominance of sensations (TDS) test (n = 16) where panelists were given a list of nine attributes. The area under the curve was extracted from TDS curves for each attribute/sample and this was correlated with rheological (viscosity at 10 s−1, G′, G″, and tan δ at 1 Hz) and tribological (friction coefficient in three regimes) data. The viscosity of the control sample decreased after adding hydrocolloids (except Starch_0.8%) and with saliva incorporation. G′ and G″ either increased or were similar for xanthan and blends and decreased for starch‐thickened samples. Hydrocolloid addition increased friction for all samples and was higher with saliva addition. Sensory results showed that samples with starch were perceived as thick and grainy while xanthan was perceived as smooth and slippery. A greater number of sensory attributes correlated with viscoelastic parameters compared to friction coefficients. Correlations were highest with the saliva added samples, further highlighting the importance of including saliva during instrumental testing.
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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 | 0.001 | 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".