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.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 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".