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Demonstrates high classification accuracy of pollutants using AI, highlighting practical applications in monitoring.
M. To address spectral complexity, a data-driven approach combining principal component analysis (PCA) and support vector machine (SVM) classification is employed, achieving a high classification accuracy of 96.7% for single analytes as well as binary and ternary mixtures across multiple concentration levels. The proposed approach offers strong potential for multiplex detection and practical applications in environmental monitoring, food safety, and on-site analytical sensing.
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Kumar et al. (2026) studied this question.
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