Authentication of rapeseed variety based on hyperspectral imaging and chemometrics
Bibliographic record
Abstract
The seed authentication is crucial for quality and yield. The traditional detection methods are often destructive, time-consuming and laborious. In this study, authentication of rapeseed variety was proposed by hyperspectral image (HSI) and a partial least squares discriminant analysis (PLS-DA). Hyperspectral imaging of single rapeseed was acquired. Random frog was used to select the important variables, and PLS-DA was used to build a classification model. The validation results based on an independent test set indicated that this model could differeniate the target rapeseed variety from other one. Moreover, to extend the use of this model in practice, the rapeseed samples adulterated with 4 % and 6 % rapeseeds of other varities were prepared to validate this model. The results indicated that this model could also identify the adulteration with other vatities. Subsequently, seed purity was correctly determined by percentage of authentic rapeseeds. In summary, hyperspectral imaging combined with PLS-DA effectively determine the purity of rapeseed. This study provides a reference for rapid seed authentication of other seeds to improve breeding efficiency and optimize germplasm resources.
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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) | 0.001 | 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 | 0.001 |
| 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 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".