Identification of sensory properties driving consumers' liking of commercially available kale and arugula
Bibliographic record
Abstract
BACKGROUND: Kale and arugula are leafy green vegetables whose sensory properties have not been extensively explored. The objective was to assess the sensory properties and consumer acceptability of commercially available kale and arugula while also discovering drivers of consumer liking and barriers to consumer acceptance. Descriptive analysis and consumer testing were completed. The trained panellists (n = 11) were trained for 15 h to evaluate 11 sensory properties relating to the aroma, taste and texture of the kale and arugula. The consumer testing (n = 108) evaluated the leafy greens for overall liking and their liking of taste, aroma, texture and appearance. RESULTS: Results were analyzed using ANOVA, Tukey's HSD and external preference mapping. Approximately half of the attributes for the kale samples were found to be significantly different. Similarly, significant differences in sensory properties were found in most of the arugula samples. Consumers liked the kale and arugula varieties that were sweet and nutty. Also, they preferred arugula that was described as spicy. CONCLUSION: The majority of consumers preferred sweet and nutty leafy greens. Organic growing methods did not affect consumer liking; however, organic labels do positively affect hedonic ratings of a consumer's overall liking of the product. This study also identified that 'Baby' leafy greens are well liked by consumers, and this area of produce should be expanded. © 2021 Society of Chemical Industry.
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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 | 0.001 | 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) | 0.005 | 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".