Partial projective mapping and ultra‐flash profile with and without red light: A case study with white wine
Bibliographic record
Abstract
Abstract Partial projective mapping (PM) is a sensory method that is used to evaluate one sensory modality (flavor, texture, or appearance). The objective of the study was to determine if the use of red light to mask subtle differences in the colors of the wine influenced the consumers' flavor descriptions of white wines. The results of partial PM tasks for flavor completed under white light (n = 45 and n = 52) and under red light (n = 66 and n = 67) were compared. The participants who completed the projective task under white light could adequately separate the wines based on the grapes used during production and adequately described the wines. The participants' evaluations were altered when red light was used, and mouthfeel characteristics were emphasized. The participants could not categorize the wines based on grape varieties. There was no correlation between the evaluations completed under white light and those completed under red light (RV = 0.211, 0.318, 0.220, and 0.277). These results indicated that although the participants were asked to concentrate on the flavor attributes of the white wine, the color masking task affected their evaluations. Future work needs to explore when it is best to mask the color of samples during a partial PM task. Practical Applications Partial projective mapping (PM) tasks ask panelists to focus on a specific sensory modality and have led to better discrimination than global PM (Marcano et al., Food Research International, 67, 323–330). In a trial involving only one sensory modality (flavor or texture), red light may be used to mask differences in the appearance or color of the products. This study investigates the effect of red light on untrained participants' evaluation during a partial PM task for flavor. No correlation was found between the participants' evaluations under red light when compared to those who completed the task under white light. Further examination is necessary; however, this shows that the red light influences the participants' evaluation and affected the description of the products being tested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".