Machine learning techniques for real-time UV-Vis spectral analysis to monitor dissolved nutrients in surface water
Bibliographic record
Abstract
Ultraviolet-visible (UV-Vis) spectroscopy is a well-established technique for real-time analyzing contaminants in finished drinking water and wastewater. However, it has struggled in surface water because surface water such as river water has more complex chemical compositions than drinking water and lower concentrations of nutrient contaminants such as nitrate. Previous spectrophotometric analysis using absorbance peak at UV region to estimate nitrate in drinking water performs poorly in surface water because of interference from suspended particles and dissolved organic carbon which absorb light along similar wavelengths. To overcome these challenges, the paper develops a machine learning approach to utilize the entire spectral wavelengths for accurate estimation of low concentration of dissolved nutrients from surface water background. The spectral training data used in this research are obtained by analyzing water samples collected from the US-Canada bi-nationally regulated Detroit River during agricultural seasons using A.U.G. Signals' dual channel spectrophotometer system. Confirmatory concentrations of dissolved nitrate in these samples are validated by laboratory analysis. Several commonly used supervised learning techniques including linear regression, support vector machine (SVM), and deep learning using convolutional neural network (CNN) and long short-term memory (LSTM) network are studied and compared in this work. The results conclude that the SVM with linear kernel, CNN with linear activation function, and LSTM network are the best regression models, which are able to achieve a cross validation root-mean-squared-error (RMSE) less than 0.17 ppm. The results demonstrate effectiveness of the machine learning approach and feasibility of real-time UV-Vis spectral analysis to monitor dissolved nutrient levels in the surface watersheds.
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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 | 0.001 |
| 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 | 0.001 |
| 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".