1D NMR Processing in Linux and Windows Example Script

   %Load MVAPACK   
    addpath('/opt/mvapack/'); %%This could also be used as 'pkg load mvapack' on systems where mvapack is installed

   %Load Data 
    F.dirs = glob('???');
    [F.data, F.parms, F.t] = loadnmr(F.dirs);

   %Add Classes and Labels 
    cls.Y = classes([7,8,6,8,8,8,7,8,7,8,8,8,8,8,8]);
    cls.labels = {'1','2','3','4','5','6','7','8','9','10','11','12','13','14','15'};

   %Zerofills
    F.data = zerofill(F.data, F.parms, 2); #number of zerofills can be adjusted

   %Build the Spectra/FT
    [S.data, S.ppm] = nmrft(F.data, F.parms);
    %Note: Check out the Quality (spit out a couple plots)
    plot(S.ppm,S.data(1,:)) %%  <-- Change 1 to a different number to inspect that spectrum.

   %Autophase the Spectra
    %Note, some users run this ~2-3 times for convergence
    [S.data, S.phc0, S.phc1] = autophase(S.data, F.parms); 
    [S.data, S.phc0, S.phc1] = autophase(S.data, F.parms); 
    [S.data, S.phc0, S.phc1] = autophase(S.data, F.parms); 

   %Extract the Real Spectral Components
    XF.data = realnmr(S.data, F.parms);
    XF.ppm = S.ppm;

   %Check Plot to Find Reference Standard
    plot(XF.ppm, XF.data)

   %Reference Adjustment
    XF.ppm = refadj(XF.ppm, -0.160, 0.0);

   %Icoshift
    XF.data = icoshift(XF.data, XF.ppm);

   %Remove Undesired Regions 
    %Note:Removes all values between r0 and r1, r2 and r3, etc.
    r0= findnearest(XF.ppm, min(XF.ppm));
    r1=findnearest(XF.ppm, 0.4);
    r2=findnearest(XF.ppm, 0.97);
    r3=findnearest(XF.ppm, 1.33); 
    r4=findnearest(XF.ppm, 4.5);
    r5=findnearest(XF.ppm, 5.2);
    r6=findnearest(XF.ppm, 8.5);
    r7=findnearest(XF.ppm, max(XF.ppm));
    X.rm.var = [r7:r6,r5:r4,r3:r2,r1:r0];
    [XF.data, XF.ppm] = rmvar(XF.data, XF.ppm, X.rm.var);

   %Normalize Data
    X1.data= pqnorm(XF.data);
    X1.ppm=XF.ppm

   %Binning
    [B.data, B.ppm, B.widths]=binadapt(XF.data,XF.ppm,F.parms);

Build Models

   % Principle Component Analysis 
    mdlpca= pca(X1.data); 
    mdlpca = addclasses(mdlpca, cls.Z);

   % Plot Scores  
    scoresplot(mdlpca, 2, [], true);
    print -deps -color 'FB-2-mdlPCA.eps'
    print -dpdf -color 'Fb-2-mdlPCA.pdf'
  
   %RQ Plot
    rqplot(mdlpca)
    print -deps -color 'FB-2-rqmdlPCA.eps'
    print -dpdf -color 'Fb-2-rqmdlPCA.pdf'
  
   %Save Scores
    savescores (mdlpca,FB-2-scoresPCA,3,cls.Y,cls.labels)

   % Orthogonal Projection to Latent Squares (OPLS) 
    %Note: no seperation with binned data
    mdlopls = opls(X1.data, cls.Y);
    mdlopls = addlabels(mdlopls, cls.labels);
    print -deps -color 'FB-2-mdlOPLS.eps'
    print -dpdf -color 'Fb-2-mdlOPLS.pdf'
  
  

 %Validation Stages for OPLS Models
 

   %RQ Plot
    rqplot(mdlopls);
  

   %Permutation Test
    mdl.cv.perm = permtest(mdlopls);
    permscatter(mdl.cv.perm) 
 

   %CV Anova
    mdl.cv.anova = cvanova(mdlopls);
    mdl.cv.anova