Consumer perception of collagen from different sources: An investigation using hedonic scale and check all that apply
Bibliographic record
Abstract
Consumers are adding collagen powder to their diets for its health benefits. However, few studies have investigated consumer perception of collagens produced from different sources. As such, the objective of this study was to evaluate the acceptability and sensory properties of commercially available collagen powders (bovine, marine, and mixed). Two different sensory trials were conducted. First, six different collagen powders were mixed with water and evaluated for their sensory properties and acceptability (n = 98; referred to as collagen-in-water). In the second trial, the collagen powders were mixed into strawberry smoothies and their sensory properties were assessed (n = 92; referred to as collagen-in-smoothie). Both studies used the 9-point hedonic scale and check all that apply to evaluate the collagen powders. The results indicated that the collagens could be grouped based on their source when evaluated in water and in a smoothie. Also, the aroma and taste of the marine collagens impacted their acceptability and were associated with fishy, sour, bitter, and salty attributes. Overall, collagen that was low in flavor was more acceptable to the participants in this study. PRACTICAL APPLICATION: Recently, consumers have begun to purchase collagen powder for its health benefits, specifically its positive effects on skin appearance. Understanding the sensory properties of the different collagens can allow for the ingredients to be incorporated into different food products and help promote consumer purchases. Collagen should be mixed into beverages rather than be consumed in water alone to increase acceptability. Also, marine collagen incorporation into foods should be avoided unless off-aromas and flavors can be masked by other properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".