Close
  Indian J Med Microbiol
 

Figure 1: (a and c) Two-dimensional PLS-DA score plot derived from one-dimensional 1H CPMG and diffusion edited NMR spectra of serum samples obtained from erythromycin and control groups represented by a triangle (red) and a circle (blue) respectively. One data point stands for one subject. The performance of the PLS-DA model was evaluated using 10-fold cross validation parameters derived using the top five components (latent variables) and the corresponding R2 and Q2 values are shown in (a and c) respectively. (b and d) The loading plots showing discriminatory potential of some of the metabolites responsible for the separation of the two groups, which are color-coded according to the absolute value of the correlation coefficient; a reddish signal indicates a more significant contribution to the class separation than a bluish signal. Positive (>0) and negative (<0) loadings indicate metabolites increased and decreased in concentration in the erythromycin group, respectively

Figure 1: (a and c) Two-dimensional PLS-DA score plot derived from one-dimensional <sup>1</sup>H CPMG and diffusion edited NMR spectra of serum samples obtained from erythromycin and control groups represented by a triangle (red) and a circle (blue) respectively. One data point stands for one subject. The performance of the PLS-DA model was evaluated using 10-fold cross validation parameters derived using the top five components (latent variables) and the corresponding R<sup>2</sup> and Q<sup>2</sup> values are shown in (a and c) respectively. (b and d) The loading plots showing discriminatory potential of some of the metabolites responsible for the separation of the two groups, which are color-coded according to the absolute value of the correlation coefficient; a reddish signal indicates a more significant contribution to the class separation than a bluish signal. Positive (>0) and negative (<0) loadings indicate metabolites increased and decreased in concentration in the erythromycin group, respectively