Development and validation of a color evaluation process for sweet potato preference characterization
Bibliographic record
Abstract
Abstract This study reports on the development of a process to objectively evaluate color using descriptive analysis. Panelists established a color lexicon (hue, lightness, evenness) and a two‐dimensional reference tool. The lexicon was applied to 23 baked sweet potato cultivars, along with a flavor lexicon. Color attributes all differentiated the products; most of the variation was due to color evenness. A consumer acceptance test (n = 204) was conducted on a subset of the products and showed a strong bias for specific color attributes. Consumers liked even, light‐orange hue; however, small changes in color dimensions impacted visual appeal. Overall characterization of products is described by a three‐factor principal component analysis solution. F1 (44% variance) correlated to moist texture and a redder‐orange hue and inversely correlated to stickiness. F2 (30% variance) correlated with high evenness and inverse correlation with acidic, bitter taste, and earthy aroma. F3 (15% variance) correlated to high sweet taste and caramel aroma. Practical applications For consumers, food color is an indicator of key aspects of quality such as freshness, nutritional value, and sensory properties, and thus it is critically important for consumer liking. After creation and validation of a process for the evaluation of perceived color using a trained descriptive panel, an external preference map, which included the aspects of color, was able to identify three consumer segments with a complex preference pattern. This approach could be applied to more fully characterize other horticultural or food products where color is critical to the consumer sensory experience.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".