Simultaneous Measurement of Solution Concentration and Slurry Density by Raman Spectroscopy with Artificial Neural Network
Bibliographic record
Abstract
In this work, the capability of Raman spectroscopy to measure the solution concentration and slurry density simultaneously and quantitatively was studied. Paracetamol–ethanol and l -glutamic acid–water systems were chosen as model systems. Different preprocessing methods (spectra range selection, baseline removal, direct orthogonal signal correction (DOSC), or no processing) and multivariable analysis techniques (characteristic peaks regression (CPR), principal component regression (PCR), partial least-squares regression (PLSR), and artificial neural network (ANN)) were applied and compared based on the root mean squared error (RMSE). It was demonstrated that the solution and solids concentration can be extracted separately from Raman spectroscopy. On the one hand, it is found that DOSC preprocessing can improve the fitting performance of the linear regression models (CPR, PCR, and PLSR) but not for ANN model. On the other hand, the ANN method, owing to its nonlinear prediction ability, better predicted the results than the linear models when the signal was weak.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".