Detecting the Presence and Concentration of Nitrate in Water Using Microwave Spectroscopy

Nitrate is a common pollutant in surface waters, which water companies must monitor for regulatory and safety reasons. The presence of nitrate in deionised water is detected and concentration estimated from microwave spectroscopy measurements in the range 9 kHz-6 GHz. Experimental results were obtained for 19 solutions (18 salt solutions in deionised water and 1 deionised water), each measured ten times with 4001 points (total N = 190). The resulting data was randomly assigned into equal parts training and test data (N = 95 each). Both regression (for the estimation of nitrate concentration) and classification (for detecting the presence of nitrate) methods were considered, with a rigorous feature selection procedure used to identify two frequencies for each of the classification and regression problems. For detection classification models were applied with nitrate levels binned using 30 mg/l as the threshold. A logistic regression model achieved AUROC of 0.9875 on test data and a multi-layer perceptron achieved AUROC of 0.9871. In each case, the positive predictive value of the model could be optimized at 100% with sensitivity of 90% and specificity of 100%. For the concentration estimates, a linear regression model was able to explain 42% of the variance in the training data and 45% of the variance in the test data and an MLP model delivered similar performance, explaining 43% of variance in the training data and 47% of variance in the test data. A sensor based on this model would be appropriate for detecting the presence of nitrate above a given threshold, but poor at estimating concentration.