Matlab r

Before starting multivariate statistical analysis, each NMR spectrum (-0.04-10 ppm) was divided into integrated regions of 0.01 ppm and 0.0015 ppm width using the MATLAB (R2012a, The MathWorks Inc., Natick, MA, USA) after phase and baseline correction. The δ5.17–4.70 regions were excluded to eliminate the effect of the residual peak from suppressed water resonance [12 (link)]. The remained spectral segments were then normalized to the total sum of the spectral intensity to compensate for variations in total sample volume. The normalized integral values were then subjected to further analysis.
To distinguish metabolic pattern between the control group and DEN-treated group and characterize the changes of metabolic profile during hepatocarcinogenesis, principal component analysis (PCA) model and orthogonal partial least-squares discriminant analysis (OPLS-DA) model were performed to identify the separation between the control and DEN groups at different time points using the SIMCA 13.0 software (Umetrics, Umeå, Sweden). A more intuitive score plot of the first two principal components (PC1 and PC2) was obtained, in which each point represents an individual sample. Additionally, the corresponding variable importance in the projection (VIP) plot was adopted to identify the metabolites which contribute to the separation of the samples in the score plots.

All the data were expressed as mean ± SD and one-way ANOVA (Analysis of variance) followed by Dunnett’s test was used for the statistical analysis using SPSS software (version 16, SPSS, Inc., Chicago, IL, USA). * p < 0.05 and ** p < 0.01 were considered significant. The artificial neural network was trained using custom codes developed by the authors written in MATLAB(R) (2017b, Mathworks, Natick, MA, USA) on a standard computer station (Intel(R) Core(TM) i7-6700k 4.00 GHz, 8 cores, 8 GB RAM) machine, whereas the partial least squares method was implemented using MATLAB’s built-in plsregress function.