The following is an abstract for the selected article. A PDF download of the full text of this article is available here. Members may download full texts at no charge. Non-members may be charged a small fee for certain articles.

Fully Automated High-Performance Signal-to-Noise Ratio Enhancement Based on an Iterative Three-Point Zero-Order SavitzkyGolay Filter

Volume 62, Number 10 (Oct. 2008) Page 1160-1166

Schulze, H. Georg; Foist, Rod B.; Ivanov, Andre; Turner, Robin F.B.

The automated processing of data from high-throughput and real-time collection procedures is becoming a pressing problem. Currently the focus is shifting to automated smoothing techniques where, unlike background subtraction techniques, very few methods exist. We have developed a filter based on the widely used and conceptually simple moving average method or zero-order Savitzky-Golay filter and its iterative relative, the Kolmogorov-Zurbenko filter. A crucial difference, however, between these filters and our implementation is that our fully automated smoothing filter requires no parameter specification or parameter optimization. Results are comparable to, or better than, Savitzky-Golay filters with optimized parameters and superior to the automated iterative median filter. Our approach, because it is based on the highly familiar moving average concept, is intuitive, fast, and straightforward to implement and should therefore be of immediate and considerable practical use in a wide variety of spectroscopy applications.