Automated Spectral Denoising with Feature-Based Adaptive Spline Smoothing

• Activity Number: 400
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Philip Manuel Fernandes*+ and James McLellan
• Companies: Queen's University and Queen's University
• Keywords: spectral denoising ; B-splines ; knot placement ; principal components analysis ; chemometrics ; filtering

Abstract

Spline fitting is used regularly in chemometrics for denoising, analysis and correlation of infrared chemical spectra. Automated selection of the appropriate number and placement of knots is required to support high-throughput analysis, and the heteroscedastic nature of such spectra (both noise and the presence of relatively smooth regions interrupted by sharp peaks), presents challenges for existing techniques. The heuristic knot placement algorithm of Li et al. (2005) for 1D object contours is extended to spectral fitting by optimizing the denoising step for a range of spectral types and signal/noise ratios, using the following criteria: robustness to types of spectra and noise conditions, parsimony of knots, low computational demand and ease of implementation in high-throughput settings. Pareto-optimal filter configurations are determined using simulated data from factorial experimental designs. The improved heuristic algorithm uses wavelet transforms and provides improved performance in robustness, parsimony of knots and the quality of functional PCA and functional regression models used to correlate spectra with chemical composition. Reference DOI: 10.1016/j.cad.2004.09.008