Development and validation of a color evaluation process for sweet potato preference characterization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it