Surface Engineered Nanoparticles Coupled with Pattern Recognition Techniques for Rapid Identification and Discrimination of Multiple Thiols in a Real Sample Matrix

Abstract

Thiols, including Cysteine (CYS) and Glutathione (GSH), play pivotal roles in numerous physiological processes as they are integral components of many essential biomolecules and are found abundantly in foods such as additives and antioxidants. Any deviations in thiol concentrations can disrupt normal physiological functions, affecting the body’s metabolism and potentially leading to diseases such as Alzheimer’s and Parkinson’s diseases, etc. Consequently, the imperative need for developing reliable and robust techniques for thiol analysis is crucial for early disease detection and ensuring food safety. In this regard, we have decorated the surface of organic nanoparticles with metal ions, which have been characterized using various techniques such as Dynamic Light Scattering (DLS), Zeta potential, Fourier Transformation Infrared Spectroscopy (FTIR), X-ray Photoelectron Spectroscopy (XPS), and Transmission Electron Microscopy (TEM) and utilized for the detection and discrimination of various thiols (cysteine, Glutathione, 3-mercaptopropionic acid, 2-mercapto ethanol, and cysteamine). Photophysical results revealed that various thiols exhibit unique binding affinities toward sensor elements, serving as fingerprints for each thiol. These patterns can be quantitatively differentiated using linear discrimination analysis (LDA) and hierarchical clustering analysis (HCA). The sensor array effectively discriminates target thiols with 100% accuracy and high sensitivity with limit of detection values from 1.19 to 4.20 μM. Apparently, it offers required simplicity, rapid response, sensitivity, and stability, which holds promise for enhancing food safety.