Comparing preferred attribute elicitation to trained panelists' evaluations using a novel food product
Bibliographic record
Abstract
Abstract The present study compares a new rapid sensory method, preferred attribute elicitation (PAE), to a trained panelists' evaluations, using cookies containing green tea extract (GTE) as a model. The cookies contained added amounts of 0, 2, 4, 7, and 9% GTE based on the weight of flour. Ten trained panelists evaluated the cookies. The panelists were trained for 10 one‐hour sessions followed by three replicates of testing. Additionally, 43 panelists in two different sessions (n = 25 and 18), evaluated the five samples following the PAE method. RV coefficients were used to provide a numerical value for the degree of similarity between the descriptive data obtained from the PAE sessions and the descriptive analysis panel. The RV coefficient for the two PAE sessions was .843, indicating a very high similarity between the sessions. However, the RV coefficient comparing the two PAE sessions to the descriptive analysis data were .554 and .179, respectively. These results indicate that the PAE method was not comparable to the trained panelists' evaluations. Future work needs to identify when PAE is the most suitable method to use and to modify the existing methodology to make it a more efficient method. Practical applications Preferred attribute elicitation (PAE) is a rapid sensory analysis method that uses untrained panelists to evaluate attributes of a product and determine what attributes drive consumer liking. This study investigates how the technique (using untrained panelist) compares to a trained panel when testing baked products differing in flavor intensity. There was not a relationship between the PAE sessions and trained panel for the flavors of this particular product. More work must be done to determine why the evaluations of the flavor was not consistent if PAE is to be used in future sensory trials.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".