This study examined the feasibility of using the autocorrelation transformation in combination with pattern recognition methods to develop search pre-filters that facilitate infrared spectral library searching of the PDQ database.
The use of the autocorrelation transformation addresses many problems encountered when transferring classification models between spectrometers. The autocorrelation transformation is also sensitive at distinguishing subtle but significant features in spectral data such as minor peaks, shoulders, and peaks with unique shapes. Previous workers have shown that peak shifts and related spectral alignment problems can be obviated by using the autocorrelation transformation. The autocorrelation transformation produces a histogram for each IR spectrum, which can be a more useful representation of spectra for pattern recognition that involves data collected on different spectrometers. Instead of directly sampling points from the histograms using classification techniques such as SIMCA to identify the informative region, each histogram was first deconvolved to better capture the signal using Coiflet wavelets with the informative wavelet coefficients identified using a genetic algorithm for pattern recognition and feature selection. (publisher abstract modified)
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