Design of a real-time monitoring system for blending ratio and purity of tobacco for hyperspectral imaging
Bibliographic record
Abstract
In recent years, hyperspectral imaging technology has a large application prospect in quality inspection in the tobacco industry.The study is based on near infrared spectroscopy technology and partial least squares regression method to establish mathematical analysis model of tobacco adulteration ratio of four components, such as expanded tobacco, stalked tobacco, large threaded tobacco and small threaded tobacco, and carry out internal and external inspection.At the same time, TLBO algorithm is used in the optimization of ELM tobacco purity grade determination model to realize the design of tobacco purity monitoring method, and then build the real-time monitoring system of tobacco blending ratio and purity.Tobacco with different purity grades were selected for experimental testing and model comparison analysis.The results show that the constructed PLS model can accurately predict the adulteration content of the four components in tobacco, and the correlation coefficients between the predicted and actual values are above 0.95 (p < 0.01), and the relative deviation of the prediction is below 3%.The accuracy of the TLBO-ELM model for identifying tobacco with different grades of purity is 88%, and the classification accuracy in the validation set is improved by 9.32% compared with the ELM model, which is within the acceptable range.It shows a better classification effect than PLS-DA in an acceptable range, which proves that the proposed method can be used for discriminating and monitoring the purity of tobacco.The monitoring system in this paper can be used in the analysis of tobacco blending ratio and purity detection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".