Consumers' sensory perception and emotional response towards animal and plant-based soups (familiar food items) with the addition of shio-koji (an unfamiliar ingredient)
Why this work is in the frame
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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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it