Apple flavor and its effects on sensory characteristics and consumer preference
Bibliographic record
Abstract
Abstract The focus within the apple industry is to identify varieties most preferred by consumers. To help with this, it is necessary to emphasize the discovery of flavor perceptions responsible for consumer preference in apples. The present study aimed to determine which flavor attributes are associated with different apple varieties, determine which apple varieties consumers prefer, and to determine which flavor attributes are contributing to consumer preference. Over two subsequent years, a trained sensory panel (n = 10, n = 15) evaluated 27 and 28 varieties, respectively. Intensity ratings of taste, flavor, and texture characteristics for each apple variety were recorded. This data was paired with an untrained consumer hedonic evaluation (n = 226) using a subset of apple varieties (n = 16). Results revealed that two large groups of apple consumers exist. Group 1 (29%) emphasized the importance of texture, while Group 2 (49%) was primarily driven by sweet taste, and honey and floral flavors with less focus on texture. Practical Applications The results of this research provide insight into the positive and negative preference drivers of apple consumers. By understanding flavors associated with consumer preference, the information can be used as a tool to aid breeding programs in the creation of consumer‐centric apples that will be commercialized. Additionally, through the creation of an external preference map, a point‐of‐reference has been created to serve as a predictor for upcoming apple varieties to the Ontario apple 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) | 0.003 | 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".