title: Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra.
abstract: This study presents three feature selection methods for identifying the metabolite features in nuclear magnetic resonance spectra that contribute to the distinction of samples among varying nutritional conditions. Principal component analysis, Fisher discriminant analysis, and Partial Least Square Discriminant Analysis (PLS-DA) were used to calculate the importance of individual metabolite feature in spectra. Moreover, an Orthogonal Signal Correction (OSC) filter was used to eliminate unnecessary variations in spectra. We evaluated the presented methods by comparing the ability of classification based on the features selected by each method. The result showed that the best classification was achieved from an OSC-PLS-DA model.
authors.affiliation: Department of Industrial and Information Engineering, University of Tennessee, Knoxville, TN 37996, USA. hcho7@utk.edu
authors.forename: Hyun-Woo
authors.initials: HW
authors.lastname: Cho
authors.forename: Seoung Bum
authors.initials: SB
authors.lastname: Kim
authors.forename: Myong K
authors.initials: MK
authors.lastname: Jeong
authors.forename: Youngja
authors.initials: Y
authors.lastname: Park
authors.forename: Nana Gletsu
authors.initials: NG
authors.lastname: Miller
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Ziegler
authors.forename: Dean P
authors.initials: DP
authors.lastname: Jones