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

ABSTRACT 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 (NO 2 − ), calcium (Ca 2+ ), magnesium (Mg 2+ ), carbonate (CO 3 2− ), bromide (Br − ), chloride (Cl − ) and phosphate (PO 4 3− ), 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 R 2 score and mean squared error (MSE). The best results of R 2 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 R 2 score and MSE of 0.9998 and 0.696, respectively. The promising results strengthen the applicability of the proposed method in real‐world scenarios.