Multiplex Sensing of Complex Mixtures by Machine Vision Analysis of TLC-SERS Images

Thin layer chromatography in tandem with surface-enhanced Raman scattering (TLC-SERS) has demonstrated tremendous potentials as a new analytical chemistry tool to detect a wide range of substances from real-world samples. However, it still faces significant challenges of multiplex sensing from complex mixtures due to the imperfect separation by TLC and the resulting interference of SERS detection. In this article, we propose a multiplex sensing method of complex mixtures by machine vision analysis of the scanning image of the TLC-SERS results. Briefly, various pure substances in solution and the complex mixture solution are separated by TLC followed by one-dimensional SERS scanning of the entire TLC plate, which generates TLC-SERS images of all target substances along the chromatography path. After that, a machine vision method is employed to extract the template images from the TLC-SERS images of pure substance solutions. Finally, we apply a feature point matching strategy based on the Winner-take-all principle, which matches the template image of each pure substance with the mixture image to confirm the existence and derive the position of each target substance in the TLC plate, respectively. Our experimental results based on the mixture solution of five different substances show that the proposed machine vision analysis is highly selective, sensitive and does not require artificial analysis of the SERS spectra. Therefore, we envision that the proposed machine vision analysis of the TLC-SERS imaging is an objective, accurate, and efficient method for multiplex sensing of trace level of target substances from complex mixtures.