Abstract

In this paper, we propose an improved subtraction algorithm for rapid recovery of Raman spectra that can substantially reduce the computation time. This algorithm is based on an improved Savitzky–Golay (SG) iterative smoothing method, which involves two key novel approaches: (a) the use of the Gauss–Seidel method and (b) the introduction of a relaxation factor into the iterative procedure. By applying a novel successive relaxation (SG-SR) iterative method to the relaxation factor, additional improvement in the convergence speed over the standard Savitzky–Golay procedure is realized. The proposed improved algorithm (the RIA-SG-SR algorithm), which uses SG-SR-based iteration instead of Savitzky–Golay iteration, has been optimized and validated with a mathematically simulated Raman spectrum, as well as experimentally measured Raman spectra from non-biological and biological samples. The method results in a significant reduction in computing cost while yielding consistent rejection of fluorescence and noise for spectra with low signal-to-fluorescence ratios and varied baselines. In the simulation, RIA-SG-SR achieved 1 order of magnitude improvement in iteration number and 2 orders of magnitude improvement in computation time compared with the range-independent background-subtraction algorithm (RIA). Furthermore the computation time of the experimentally measured raw Raman spectrum processing from skin tissue decreased from 6.72 to 0.094 s. In general, the processing of the SG-SR method can be conducted within dozens of milliseconds, which can provide a real-time procedure in practical situations.

© 2014 Optical Society of America

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