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.