Projective mapping as a versatile sensory profiling tool: A review of recent studies on different food products
Bibliographic record
Abstract
Abstract Descriptive analyses or trained panels are one of the most extensively used methods in the sensory evaluation field; however, they can be expensive and time‐consuming. Therefore, researchers have begun to use rapid sensory analysis methods. One of these rapid methods is projective mapping (PM). PM asks the participants to place samples on a two‐dimensional space in a way that reflects their perception of the samples' differences and similarities. This review outlines the main characteristics of 195 studies published using the PM technique. The results reveal that the majority of PM studies asked consumers (61.5%) to evaluate different food products. Most of the studies utilized the Napping variation (60.3%) of the PM method, however, about a quarter of the studies that applied the Napping method did not adhere to the guidelines of the method (e.g., did not use a rectangular space for evaluation). Furthermore, most studies (71.6%) asked the participants to evaluate their holistic perception of the samples and used multiple factor analysis to analyze the results (85.1%). Overall, the review identified the importance of the PM method and how it has been applied to a wide variety of different food products. Future studies should use a larger number of samples and examine how the PM method can be used with older adults and children. Practical Applications Results of this review provide insight for different stakeholders in the sensory evaluation and food science fields. It will assist researchers in designing experiments using the projective mapping (PM) method, as well as provide information about the type of panelists, the different variations of PM and the statistical techniques being used by researchers. In addition, it identifies the advantages and disadvantages of the PM method.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".