Sensory characterization of yellow pea and ground chicken hybrid meat burgers using static and dynamic methodologies
Bibliographic record
Abstract
To reduce animal protein consumption, new food products need to be created. Furthermore, there is a growing number of consumers who consciously act to reduce their meat consumption. Hybrid meat products (HMP) are food items that combine both plant and animal proteins. The objective of this study was to create a hybrid meat burger (HMB) using yellow pea and chicken and to evaluate the sensory properties of the new product using static (check-all-that-apply [CATA]) and dynamic (temporal check-all-that-apply [TCATA]) methods. Yellow pea flour was added to a chicken burger at 0% (control), 10%, 20%, 30%, and 40%. A sensory trial asked participants (n = 69) to evaluate the HMBs using hedonic scales and CATA. A second sensory trial asked experienced panelists (n = 14) to evaluate the items using TCATA. The addition of the yellow pea flour decreased the liking of the burgers, except for the 10% formulation. The burgers made with higher amounts of yellow pea were associated with off-flavors (beany and nutty; significantly different from the control) during both CATA and TCATA tasks and detracted from consumers' liking. Juicy, moist, meaty, salty, and soft attributes increased the consumers' liking. The study identified that the addition of yellow pea to chicken burgers is only acceptable to consumers in small quantities (10%). In addition, the yellow peas contributed off-flavors and a dry texture that was disliked by the consumers. PRACTICAL APPLICATION: The environmental impacts of livestock production have created a need to incorporate more plant-based proteins into consumers' diets to increase sustainability. The market for meat alternatives, including hybrid meat products, is expanding; however, current products do not always meet consumers' expectations. Chicken is the fastest-growing meat sector in North America, and therefore this study's objective was to determine the sensory properties of a burger made from chicken and pulses (yellow pea). It was found that if 10% of chicken in a burger was substituted with yellow pea, then the sensory properties and consumer liking were not significantly affected.
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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 | 0.000 |
| 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) | 0.001 | 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".