Intelligent estimate of chemical compositions based on NIR spectra analysis
Bibliographic record
Abstract
The main chemical compositions of tobacco is one of the significant factors to decide the quality of tobacco, and it is time consuming, expensive, unrepeatable and destructive to obtain the chemical compositions in laboratories. This paper investigates the relationship between tobacco near infrared (NIR) spectra and chemical composition, thus the chemical composition can be easily obtained through the NIR spectroscopy that is a quick, convenient, accurate and low-cost technique. An intelligent method is proposed based on least squares support vector machines (LS-SVM), and a method based on partial least squares regression (PLS) is also proposed comparative study. The obtained results show that the proposed methods are effective and feasible. The best prediction accuracy for the seven different chemical compositions is obtained by the LS-SVM method, which can reach an accuracy at 99.85% for the 400 training samples and 97.92% for the 100 testing samples.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.010 | 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".