An investigation into the sensory properties of luffa (Luffa cylindrica (L.)) seeds and a comparison to other seeds (flax, sunflower, chia, and hemp)
Bibliographic record
Abstract
Consumers are interested in new sustainable ingredients but are unwilling to accept undesirable sensory properties in their food products. Luffa (Luffa cylindrica) is mainly harvested and processed for its fibrous network, which is used as an exfoliator, while its seeds are usually discarded. However, the seeds have been found to have various nutritional benefits. As such, this study investigated the sensory properties of luffa seed powder added to yogurt and compared it to other seed powder (flax, sunflower, chia, and hemp). Consumers (n = 107) evaluated their liking of the different seeds added to yogurt using hedonic scales and the sensory properties using check-all-that-apply (CATA). The luffa seeds when mixed with yogurt were associated with off-colour, off-flavour, metallic, strong flavour, bitter, salty, earthy and decreased consumer liking. The flax and sunflower seeds were found to be sweet, nutty, cooked, mild flavour, and to have a smooth texture. The overall liking scores for the flax and sunflower seed samples were significantly higher than the luffa and hemp samples. Future studies should investigate different drying and roasting treatments to improve the sensory properties of the luffa seeds.
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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".