Online Adulteration Analysis of Oil, Milk, and Water using Surface-Enhanced Raman Spectroscopy (SERS)

This paper presents an online sensing framework for detection and quantification of adulterants in oil, milk, and water using Surface-Enhanced Raman Spectroscopy (SERS). We describe a microfluidic sampling interface for continuous monitoring, detail the design and fabrication of plasmonic substrates, and present a data-processing pipeline that combines baseline correction, denoising, feature extraction, and machine learning to provide automated classification and concentration estimation under flow. Experiments demonstrate detection limits for common adulterants (e.g., vegetable oils in engine oil, melamine in milk, and trace organics in water) at parts-per-million to parts- per-billion levels under flow conditions. We discuss substrate lifetime, fouling mitigation, and steps toward field deployment.