Classification of impact injury of apples using electronic nose coupled with multivariate statistical analyses
Bibliographic record
Abstract
Abstract An electronic nose equipped with a headspace sampling unit was evaluated as a non‐destructive method for determining damage degree. Fuji apples were dropped from different heights (0.2–0.8 m) onto a cement floor inflict damages. E‐nose data was evaluated by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to distinguish apples based on damage severity. LDA performed better than PCA for classifying the apples. Stepwise Discriminant Analysis (SDA), Radial Basis Function Neural Network (RBFN), Multilayer Perceptron Neural Networks (MLPN), and Back‐Propagation Neural Network (BPNN) models were employed for pattern recognition. With SDA dataset, the correct classification rate (CCR) was 97.5% for training and 93.8% testing; MLPN resulted in 100%, and RFBN performed better only with more severe damages. The BPNN model had excellent correlation with classification values for damaged apples (R2 > 0.98). Therefore, E‐nose technology with ANN and multivariate statistics is an effective way for classifying damaged apples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".