Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra.
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.
Cho
Hyun-Woo
HW
Department of Industrial and Information Engineering, University of Tennessee, Knoxville, TN 37996, USA. hcho7@utk.edu
Kim
Seoung Bum
SB
Jeong
Myong K
MK
Park
Youngja
Y
Miller
Nana Gletsu
NG
Ziegler
Thomas R
TR
Jones
Dean P
DP