Executive Summary : | Xenobiotics, such as pesticides and antibiotic residues, pose significant health risks and can cause severe health issues such as cancer, reproductive and developmental problems, immune system suppression, antibiotic resistance, and disruption of the gut microbiome. Detecting and monitoring these levels in food, water, and environmental samples is crucial for public health and safety. Current methods for detecting xenobiotics include HPLC, GC, Mass spectrometry, ELIsA, and electrochemistry. However, these methods have limitations such as tedious extraction methods, limited detection coverage, and interference due to organic solvents. Additionally, they are laboratory-based, require sophisticated instruments, and are difficult to implement in infield screening. In today's global market competition, the Indian agriculture and food sector must develop indigenous sensing technologies for detecting harmful extraneous chemicals to ensure food safety. This project proposes developing a differentially charged gold nanoparticles-based field-screening method for screening multiple xenobiotics in environmental samples where specialized instruments are not available. The color response pattern of differentially charged nanoparticles based on electrostatic interactions with xenobiotics will be analyzed using linear discriminant analysis (LDA) and a machine learning program (MLP). This protean colorimetric nanosensor array coupled with a machine learning program can detect multiple xenobiotics, making it particularly useful in situations where multiple xenobiotics are present or suspected. The developed nanosensor array is of high demand in food, medical, and public health sectors. |