title: A fully automated iterative moving averaging (AIMA) technique for baseline correction.
abstract: Baseline correction is one of the pre-processing steps in the analysis of metabolite signals from chemometric analytical instruments. Fully automated baseline correction techniques, although more convenient to use, tend to be less accurate than semi-automated baseline correction. A fully automated baseline correction algorithm, the automated iterative moving averaging algorithm (AIMA), is presented and compared with three recently introduced semi-automated algorithms, namely the adaptive iteratively reweighted penalized least squares (airPLS), Asymmetric Least Squares baseline correction (ALS) and a parametric method, using NMR, Raman and HPLC chromatograms. AIMA's potential in increasing the accuracy of multivariate analysis via SELTI-TOF and LCMS chromatograms was also assessed. The results show that the AIMA's accuracy is comparable to these semi-automated algorithms and has the advantage of ease of use. An AIMA plug-in for an open source metabolomics analysis tool, MZmine, was also developed. The AIMA plug-in is available at http://padel.nus.edu.sg/software/padelaima.
authors.affiliation: Pharmaceutical Data Exploration Laboratory, Department of Pharmacy, National University of Singapore, Blk S4, 18 Science Drive 4, 117543, Singapore.
authors.forename: Bhaskaran David
authors.initials: BD
authors.lastname: Prakash
authors.forename: Yap Chun
authors.initials: YC
authors.lastname: Wei
mesh_terms: D000465:Algorithms; D002851:Chromatography, High Pressure Liquid / Q000379:methods; D016018:Least-Squares Analysis; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D013058:Mass Spectrometry / Q000379:methods; D015999:Multivariate Analysis; D013059:Spectrum Analysis, Raman / Q000379:methods