Spectral recognition with a PCNN preprocessor

This is a report on work in progress. Spectral recognition is central to many areas of science and technology. Classical spectral recognition analysis techniques (least squares, partial least squares, etc.) are sensitive to offset and gain drifts and errors. This sensitivity can cause excessive costs for spectrometer resources and calibrations. Neural techniques relieve some of this sensitivity but none approach human competence. It is desirable to mimic human spectral analysis not only to improve the results but to minimize detector constraints and costs. We suggest that the first step in human analysis is peak detection. We are exploring the 1D PCNN as a peak segmenter for spectral peak finding in the presence of noise and drifts in gain and offset. We present results of 1D pulse coded neural network peak detection with both simulated and actual static spectra. We also use the PCNN to form a scale and translation invariant feature vector that may be decomposed using classical techniques such as least squares. Finally, we propose using a PCNN to exploit the temporal aspects of spectral acquisition.