Abstract
Water is considered to be the most essential and vital resources to sustaining life. Ensuring its delivery to people with no intrusion of harmful impurities, safe, reliable, and in an affordable manner is one of huge challenge amid to the ongoing climate transformations. This demands to introduce a cost effective and notion of real-time monitoring system that can detect the microbiological contaminants in aqueous solutions in timely manner to protect the public and environment health. In this paper, the prospects of integrating non-invasive terahertz (THz) waves with machine learning (ML) enabled technique is studied. The research explores a method of using Fourier transform Infrared Spectroscopy (FTIR) system to observe the absorption spectra and characteristics of three solvents solution , including salt, sugar and glucose with various quantity in aqueous solutions in the frequency range of 1 THz to 20 THz. In this study, due to the different molecular configuration and vibration modes of substances, distinct absorption spectra peaks were achieved for different concentrations of solvent solutions at certain sensitive THz region. Moreover, using measurements observations data, meaningful features are extracted and incorporated four algorithms such as random forest (RF), support vector machine (SVM), decision tree (D-tree) and k-nearest neighbour (KNN). The results demonstrated that RF obtained a higher accuracy of 84.74% in identifying the substance in aqueous solutions. Moreover, it was also found that RF with 97.98%, outperformed other classifiers for estimation of salts concentration added in aqueous solutions. However, for sugar and glucose concentrations, SVM exhibited a higher accuracy of 93.11% and 96.88%, respectively, compared to other classifiers. Thus, proposed technique incorporating ML with THz waves, may be significant in providing an efficient, cost-effective and real-time monitoring for water quality detection system.
Hydrogen Peroxide-Based Fluorometric Assay for Real-Time Monitoring of SAM-Dependent Methyltransferases