Temporal ranking for characterization and improved discrimination of protein beverages
Bibliographic record
Abstract
Abstract We propose a new temporal sensory method called temporal ranking (TR) in which assessors indicate and rank the three most noticeable sensations at every time point. The TR method was compared to temporal‐check‐all‐that‐apply (TCATA) in two trained‐panel studies, one study involving six ready‐to‐mix (RTM) protein beverages and one study involving seven ready‐to‐drink (RTD) protein beverages. In each study, the same attributes were used in both methods; six attributes were evaluated for RTMs and 10 attributes for RTDs. A trained sensory panel (n = 10) completed TCATA and temporal ranking (TR) training exercises, then evaluated each beverage in triplicate using each method in a replicated balanced randomized design. To evaluate each temporal method (TR and TCATA), each test beverage was compared with the sucrose‐ or sucralose‐sweetened control beverage within each study (RTM and RTD). Although results from TR and TCATA often coincided, TR better differentiated the protein beverage formulations on more sensory attributes and detected differences between the test and control beverages (p < .05) when TCATA did not. Overall, TR was found to be more sensitive in detecting sensory differences than TCATA, and thus could improve the guidance for the development and formulation of foods. Practical applications This study proposes a new temporal method, temporal ranking, which has assessors continuously rank the three most noticeable attributes when evaluating a beverage. Temporal ranking data can give improved guidance, especially for products that might have side flavors, such as natural nonnutritive sweeteners or alternative protein sources. Further application of findings and methodologies from this study may help guide development and formulation of 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".