Culinary Treatments Affect Sensory Attributes and Consumer Preference for Sweet Potato Cultivars
Bibliographic record
Abstract
Food quality and taste preference are important factors influencing the production and adoption of sweet potato (Ipomea batatas L.) cultivars in a specific growing region. There is very limited published information available on the sensory attributes and quality profile of sweet potato cultivars grown in Canada, even though there is a substantial increase in both production and consumption over the last few years. This study analyzed five different culinary treatments on sweet potato sensory attributes along with taste preferences. Oven-baked, boiled, fried, steamed and mashed culinary treatments were found significantly different (P < 0.05) for mealiness, sweetness and for taste profiles. Sweet potato cultivars were significant different (P < 0.05) for taste and sweetness attributes. A non-significant difference was recorded for interaction between culinary treatments and the tested cultivars for bitterness, mealiness, sweetness and taste profile. Expert panel preferences significantly differed (P < 0.05) for boiled, fried and steamed culinary treatments for tested cultivars, whereas, no difference was observed for oven-baked and mashed sweet potatoes. Boiled, fried, steamed and mashed culinary treatments for ‘Covington’ were most liked by the panelists, followed by ‘Radiance’. These sensory analysis and preferences of tested sweet potato cultivars can provide a reference for the food processing industry in preparing sweet potatoes for Canadian consumer consumption and the study outcomes can be used to guide sweet potato variety development for specific quality traits.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".