The Impact of Clean Label Packaging for Pea-Based Proteins
Bibliographic record
Abstract
There are several definitions of a clean label ranging from broad clean label claims identifying the type of ingredient (i.e., organic, non-GMO, unfamiliar/unrecognizable ingredients) to strict clean label claims identifying the number of ingredients (i.e., shortening of ingredients lists), both of which can appear as Front of Package (FOP) labeling. Therefore, the following research examines what Canadian consumers value in a clean label, how Canadian consumer attitudes toward clean labels could impact the demand for pea-based proteins, and assesses how pea-based burgers compete against hybrid and beef burgers.\nThe study relied on nationally collected data through an online survey to explore the objectives. The survey was composed of five sections featuring screener questions, discrete choice experiment (DCE), general food purchases, protein choices, and demographics, and analyzed using Multinomial Logit, Mixed Multinomial Logit, and Latent Class models.\nThe analysis revealed Canadian consumers ranked beef burger patties (60%) as the preferred choice, hybrid burger patties (22%) as the second most preferred choice, pea-based burger patties (18%) as the least preferred burger choice, with the rest selecting no purchase (1%). When exploring burger patty heterogeneity for clean label claims, individuals choosing pea-based burgers exhibited notably different taste preferences for the ingredient list length, exhibiting similar WTP between the organic attribute and shortened ingredient lists. Lastly, the respondents who were less likely to choose pea-based proteins, about 85% of individuals, valued stricter clean labels more than broad clean labels, perceiving that a reduced ingredients list signaled increased product naturalness, nutrition, and did not decrease taste. Concluding reduced ingredients list was the most significant clean label definition within the study.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".