A method for automatic quantification of one-dimensional spectra with low signal-to-noise ratio

Abstract A technique for processing noisy 1-D spectra is described. Unlike standard filters designed merely for noise suppression, this technique both smooths and automatically quantifies the peaks in a spectrum. There are three stages involved; identification of peaks from slowly varying background and random noise, estimation of peak parameters, and finally smoothing of the peaks on an individual basis. Simulated spectra, representative of those obtained from biological samples, are used to test the performance of the technique. The results agree very well with theoretical predictions. Finally, a comparison is made with the performance of two filters used for noise suppression, the matched filter and the maximum entropy method. The technique has considerable advantages over these two methods for the type of spectra considered here, the most important in practical terms being the ability to obtain automatic estimates of peak parameters together with a clear definition of the accuracy of these estimates.

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