Chemical detection and-classification in Raman spectra
Date of Original Version
Because of the unique Raman spectrum of a chemical, Raman spectroscopy can be used to identify chemicals on a surface. In this paper chemical detection and classification in a stationary background are addressed. Firstly, because the autoregressive (AR) spectrum is capable of representing a wide range of spectra, both the pure background and background plus a chemical are modeled as AR spectra with different coefficients. Based on this modeling, a generalized likelihood ratio test (GLRT) is proposed to detect abnormal chemicals in the background. In essence, the GLRT detector tests if the data can be represented by a known AR background spectrum. With the AR spectrum modeling, a classifier based on the locally most powerful test is also proposed to classify the detected chemicals. Computer simulation results are given, which show the effectiveness of the proposed algorithms. Practical problems, such as setting the detection threshold, extension to nonstationary backgrounds, and the identifiability of chemicals are also discussed.
Proceedings of SPIE - The International Society for Optical Engineering
Kay, Steven, Cuichun Xu, and Darren Emge. "Chemical detection and-classification in Raman spectra." Proceedings of SPIE - The International Society for Optical Engineering 6969, (2008). doi:10.1117/12.784622.