Multiview Ensemble Learning Framework for Real-Time UV Spectroscopic Detection of Nitrate in Water With Chemometric Modelling

The accuracy of detection of nitrate in water for quality monitoring is a significant yet challenging task. To address this, the present work proposes an ensemble machine learning–based chemometric framework for the optical detection of nitrate in water. It incorporates an absorbance-based reagent-less detection of nitrate in water to support the robustness of the model. The absorption spectra were recorded using a portable set-up in the presence and absence of interfering ions. Different interfering ions, namely, nitrite (NO₂⁻), calcium (Ca²⁺), magnesium (Mg²⁺), carbonate (CO₃²⁻), bromide (Br⁻), chloride (Cl⁻) and phosphate (PO₄³⁻), in all possible combinations (binary, ternary, quaternary, quinary, senary and septenary mixtures) are added to target analyte to validate the real-time application of the proposed algorithm. Under the multiview framework, two models, MVNPM-I and MVNPM-II, i.e., multiview nitrate prediction models, are proposed. MVNPM-I is based on an ensemble of regressors' results, and MVNPM-II uses multiple views of the dataset followed by an ensemble of their results. The performance of the models is assessed using a hold-out validation scheme with 10 repetitions and measured using R² score and mean squared error (MSE). The best results of R² score 0.9978 with a standard deviation 0.0014 and MSE of 1.1799 with a standard deviation of 0.8639 are obtained using the MVNPM-II model. Further, the performance measures of the proposed models show that they can handle the presence of interfering ions. The algorithm was also tested using real-world samples with an R² score and MSE of 0.9998 and 0.696, respectively. The promising results strengthen the applicability of the proposed method in real-world scenarios.