Classification of Aflatoxin B1 Contamination Level Using Hyperspectral Images with Random Forest and QDA
Bibliographic record
Abstract
Almonds are widely accepted nuts due to their taste and healthy nutrition, but they are also susceptible to aflatoxin B1. The current aflatoxin B1 detection method for almonds is destructive, labor intensive, costly, and has sampling problems. Therefore, this study investigates the feasibility of classifying aflatoxin B1 contaminated almonds using hyperspectral images in a non-destructive way for industrial quality control applications. In this experiment, almonds are artificially contaminated at different concentration levels in a laboratory and used as reference levels. The reference level and the mean hyperspectral image were used to develop a random forest (RF) and quadratic discriminant analysis (QDA) classifier. The minimum redundancy maximum relevance (mRMR) feature selection algorithm was introduced to find the most relevant feature spectral set, which was used to develop multispectral classification models for industrial quality control applications. The full spectrum RF classifier achieved 96.37% cross-validation accuracy for standard normal variance (SNV) with 1stderivative data and QDA achieved 91.71% accuracy for Savitzky-Golay (SG) with 1stderivative data. The experimental result demonstrates that the hyperspectral images coupled with the machine learning classifier have a great potential to classify aflatoxin B1 contaminated almonds for industrial applications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".