Optimal choice of baseline correction for multivariate calibration of spectra

• Published in: Applied spectroscopy Vol. 64; no. 9; p. 1007
• Main Authors: Liland, Kristian Hovde, Almøy, Trygve, Mevik, Bjørn-Helge
• Format: Journal Article
• Language: English
• Published: United States 01.09.2010
• Subjects: Algorithms; Animals; Calibration; Cattle; Fish Oils - chemistry; Goats; Least-Squares Analysis; Milk - chemistry; Multivariate Analysis; Regression Analysis; Reproducibility of Results; Salmo salar; Sheep; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods; Spectrum Analysis, Raman - methods
• ISSN: 1943-3530
• DOI: 10.1366/000370210792434350

Baselines are often chosen by visual inspection of their effect on selected spectra. A more objective procedure for choosing baseline correction algorithms and their parameter values for use in statistical analysis is presented. When the goal of the baseline correction is spectra with a pleasing appearance, visual inspection can be a satisfactory approach. If the spectra are to be used in a statistical analysis, objectivity and reproducibility are essential for good prediction. Variations in baselines from dataset to dataset means we have no guarantee that the best-performing algorithm from one analysis will be the best when applied to a new dataset. This paper focuses on choosing baseline correction algorithms and optimizing their parameter values based on the performance of the quality measure from the given analysis. Results presented in this paper illustrate the potential benefits of the optimization and points out some of the possible pitfalls of baseline correction.