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

Abstract

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.

Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

“…40,51,68,70,71,76 Thus, in order to effect data invariant automation, empirically or arbitrarily chosen parameters, stopping criteria, thresholds, and adjustments (i.e., increments, decrements) generally should be avoided. It is important to be When parameter search or estimation is required, it is advisable to use a subset of spectra rather than all the spectra in a potentially significantly larger set.…”

“…The success of noise reduction is reflected using an increase in the signal-to-noise ratio (SNR) of the processed measurement relative to the original, and a number of automated 82,83 and fully automated algorithms [76][77][78] exist that could be employed to this end. Figure 5 shows a typical result from the baselineflattened and despiked demonstration dataset of live human embryonic stem cells before (Fig.…”

“…We have developed a number of methods for fully automated smoothing. [76][77][78]80 All of them use the v 2 goodness-of-fit test where a moderately good fit is provided with the commonly used stopping criterion of v 2 = N, with N being the number of points in the spectrum. 124 Consequently, some built-in safeguard exists against severe smoothing-induced spectral distortions.…”

Process Analytical Utility of Raman Microspectroscopy in the Directed Differentiation of Human Pancreatic Insulin-Positive Cells

“…Spectra were processed with automated methods consisting of a small window moving average-based baseline-flattening, 35 coincident two-dimensional second difference cosmic ray-induced spike removal, 36 and an iterative Savitzky-Golay smoothing method. 37 Thereafter, aberrant spectra were removed by visual inspection. We performed principal component analyses (PCA), a method that partitions the total variance of a hyperspectral data set into subsets of size-sorted mutually orthogonal variances, on the processed data.…”

Improved Savitzky–Golay-method-based fluorescence subtraction algorithm for rapid recovery of Raman spectra

“…7(a)-7(d). First, an automated smoothing [34] was applied on the raw spectrum to reduce interference by noise. Followed by the RIA-SG-SR algorithm, the recovered Raman signal can be seen in Figs.…”