Consumers' sensory perception and emotional response towards animal and plant-based soups (familiar food items) with the addition of shio-koji (an unfamiliar ingredient)
Bibliographic record
Abstract
Abstract Globally, consumers continue to seek out novel foods and ingredients from different cultures and regions. Shio-koji is a fermented seasoning that is usually made by fermenting rice with koji (Aspergillus oryzae). It has been proposed that shio-koji can be used as a flavour enhancer of foods. This study investigated consumers' (n = 96; generally unfamiliar with koji) liking (hedonic scales), emotional response (using the EsSense25 profile in check-all-that-apply format), as well as their sensory perception (generalised Labelled Magnitude Scales and free comment) of shio-koji additions to food items. Participants evaluated three different soups (chicken, vegetable and tomato), a familiar food product, with and without the addition of shio-koji. The shio-koji increased the consumers' liking of the vegetable soup and increased their perception of saltiness in the vegetable and tomato soups. The bitterness and sourness intensity of the chicken soup decreased with the addition of shio-koji, while the sweetness increased. However, the umami taste of all soups was not impacted. The soups with shio-koji were also associated with positive emotions. During the free comment task, shio-koji led to an increased mention of meaty attributes to describe the vegetable soup, but the inverse occurred when the participants evaluated the chicken soup. The results indicate that shio-koji impacted consumer perceptions of both animal- and plant-based soups. Future studies should continue to investigate the use of shio-koji to enhance the flavour of different food products.
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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 | 0.002 |
| 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) | 0.004 | 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".