Correlation awareness evolutionary sparse hybrid spectral band selection algorithm to detect aflatoxin B1 contaminated almonds using hyperspectral images
Bibliographic record
Abstract
Aflatoxin B1 is a harmful metabolite that frequently contaminates almonds, other nuts, and grains. Prolonged consumption of foods contaminated with aflatoxin B1 can lead to severe health issues. Hyperspectral imaging enables rapid, non-destructive detection of aflatoxin B1, but its high dimensionality complicates data analysis and increases complexity of classification models. This paper presents a novel hybrid spectral band selection algorithm designed to classify aflatoxin B1 in almonds, suitable for industrial applications. The algorithm operates in two main steps. Firstly, it identifies significant spectra individually based on various tree-based boosting ensemble techniques and multilayer perceptron networks. Then, the significant spectra were optimized using the correlation-aware sparse spectral band selection process. The proposed algorithm was evaluated on three hyperspectral image datasets and was compared with existing classical methods. The selected 4 to 10 spectra achieved comparable classification accuracy compared to the full spectra model and can be used in industrial applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 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".