Food quality evaluation in drying: Structuring of measurable food attributes into multi-dimensional fuzzy sets
Bibliographic record
Abstract
Food quality is a fuzzy category, which could be evaluated using fuzzy logic. Our approach to food quality evaluation is based on mapping food quality attributes into a fuzzy domain as a multi-dimensional fuzzy sets. First, the data representing quality attributes are mapped into orthogonal coordinates using PCA to reduce dimensionality. Second, subtractive clustering (SC) is applied to determine a representative number of clusters. Each point in the dataset is associated with each cluster by credibilistic fuzzy C-means clustering (CFCM). After data organized in fuzzy clusters, an artificial neural network (ANN) is trained to associate each point in the dataset with its membership degree in each cluster. Trained ANN serves as a predictive model to convert real-time data stream into the multi-dimensional fuzzy domain. The application of this methodology is illustrated for real-time quality evaluation in shrimp batch drying. In this study 27 quality attributes have been merged into 9 orthonormal vectors, which have been clustered into 10 fuzzy sets. This structuring of the experimental fuzzy domain allowed the development of a multi-dimensional kinetic model, which improved the quality of shrimp drying. The computational time for quality identification in the fuzzy domain is below 1 sec, which is satisfactory for most real-time applications. This data-driven algorithm is completely automated and has unlimited potential for real-time fuzzy control and optimization.HighlightsMulti-dimensional fuzzy sets are unique identifiers of food qualityExtracting principal information in orthonormal coordinatesUsing the artificial neural network for predicting membership functionsA multi-dimensional fuzzy kinetics model was developedStructuring of fuzzy domain decreased computational time for fuzzy control
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".