Recursive Wavelength-Selection Strategy to Update Near-Infrared Spectroscopy Model with an Industrial Application
Bibliographic record
Abstract
Wavelength selection is widely accepted as an important step in near-infrared (NIR) spectroscopic model development. In quantitative online applications, the robustness of the established NIR model is often jeopardized by instrument response changes, process condition variations or new sources of chemical variation. However, to the best of our knowledge, online updating of wavelength selection has not been considered in NIR modeling and property prediction. In this article, a new model-updating approach is proposed that can adjust to process changes by recursively selecting the NIR model structure in terms of wavelength. The advantage of the presented approach is that it can recursively adjust both wavelength selection and model coefficients according to real process variations. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional PLS, locally weighted PLS, and several other updating strategies, the proposed method was found to achieve good accuracy in the prediction of diesel properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".