CONSUMER PREFERENCE STUDY ON COMBINED ULTRASOUND AND SODIUM HYPOCHLORITE TREATED FRESHCUT KIWIFRUITS COATED WITH CHITOSAN USING THE FUZZY LOGIC APPROACH
Bibliographic record
Abstract
Fresh cut kiwi fruits are gaining popularity among consumers due to increased nutrition, health, and convenience. In this study ultrasound treated fresh cut kiwifruit slices were coated with different concentrations (0.6, 0.8, and 1%) of chitosan. Sensory evaluation was conducted in linguistic terms for the kiwifruit slices with a panel of 15 well-trained judges to understand the consumer preference. The linguistic approach was analysed and decoded using the fuzzy logic modeling approach to find the best sample and quality attribute responsible for consumer preference. The slices with the highest defuzzified scores were obtained for the 1% chitosan-coated sample. Ranking of kiwifruit slices was based on defuzzified scores was S1>S2>S3>S4, while ranking of the quality attributes was smell>taste>color>texture. Therefore, the fuzzy logic modeling could be a practical approach for finding the consumer preference of fresh cut kiwifruits, thus increasing product marketability.
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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) | 0.002 | 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".