Automatic Baseline Subtraction of Vibrational Spectra Using Minima Identification and Discrimination via Adaptive, Least-Squares Thresholding

Abstract

A method of automated baseline correction has been developed and applied to Raman spectra with a low signal-to-noise ratio and surface-enhanced infrared absorption (SEIRA) spectra with bipolar bands. Baseline correction is initiated by dividing the raw spectrum into equally spaced segments in which regional minima are located. Following identification, the minima are used to generate an intermediate second-derivative spectrum where points are assigned as baseline if they reside within a locally defined threshold region. The threshold region is similar to a confidence interval encountered in statistics. To restrain baseline and band point discrimination to the local level, the calculation of the confidence region employs only a predefined number of already-accepted baseline minima as part of the sample set. Statistically based threshold criteria allow the procedure to make an unbiased assessment of baseline points regardless of the behavior of vibrational bands. Furthermore, the threshold region is adaptive in that it is further modified to consider abrupt changes in baseline. The present procedure is model-free insofar as it makes no assumption about the precise nature of the perturbing baseline nor requires treatment of spectra prior to execution.