## 2 Results and Discussion
### Examples
#### 2.2.1 Plant samples
\begin{table}
\begin{tabular}{l l l l} \multicolumn{4}{l}{**HILIC-LC-ESI-MS**} \\ \multicolumn{4}{l}{**1**} & \multicolumn{1}{l}{negative} & 827/14.9 & 151 \\ \multicolumn{4}{l}{2} & \multicolumn{1}{l}{negative} & 291/9.7 & 140 \\ \multicolumn{4}{l}{3} & \multicolumn{1}{l}{negative} & 947/19.5 & 101 \\ \multicolumn{4}{l}{4} & \multicolumn{1}{l}{positive} & 828/14.9 & 95 \\ \multicolumn{4}{l}{5} & \multicolumn{1}{l}{positive} & 649/12.2 & 86 \\ \multicolumn{4}{l}{6} & \multicolumn{1}{l}{negative} & 355/14 & 82 \\ \multicolumn{4}{l}{7} & \multicolumn{1}{l}{positive} & 188/9.5 & 80 \\ \multicolumn{4}{l}{8} & \multicolumn{1}{l}{negative} & 847/13.5 & 78 \\ \multicolumn{4}{l}{9} & \multicolumn{1}{l}{negative} & 289/11.5 & 76 \\ \multicolumn{4}{l}{10} & \multicolumn{1}{l}{negative} & 264/5.5 & 72 \\ \multicolumn{4}{l}{_RP-LC-ESI-MS_} \\ \multicolumn{4}{l}{**1**} & \multicolumn{1}{l}{positive} & 1261/24.5 & 232 \\ \multicolumn{4}{l}{2} & \multicolumn{1}{l}{positive} & 257/13.4 & 225 \\ \multicolumn{4}{l}{3} & \multicolumn{1}{l}{positive} & 1303/22.7 & 166 \\ \multicolumn{4}{l}{4} & \multicolumn{1}{l}{negative} & 744/7.8 & 156 \\ \multicolumn{4}{l}{5} & \multicolumn{1}{l}{positive} & 738/6.5 & 147 \\ \multicolumn{4}{l}{6} & \multicolumn{1}{l}{negative} & 315/12.9 & 135 \\ \multicolumn{4}{l}{7} & \multicolumn{1}{l}{positive} & 313/22.3 & 99 \\ \multicolumn{4}{l}{8} & \multicolumn{1}{l}{negative} & 785/11.3 & 93 \\ \multicolumn{4}{l}{9} & \multicolumn{1}{l}{negative} & 319/16.1 & 85 \\ \multicolumn{4}{l}{10} & \multicolumn{1}{l}{negative} & 530/24.7 & 71 \\ \end{tabular}
\end{table} 
Table 3: Top 10 classifiers from HILIC-LC-ESI-MS and RP-LC-ESI-MS datasets (MarkerView 1.1 peak lists). Because the GA procedure starts with a large number of subsets of components so that it provides a large collection of models, a single subset of components representative of the population is needed for prediction. Using GC/TOF-MS as an example, model number 1 was chosen for its highest classification accuracy and lowest number of feature components. The fittest model selected seven feature components with the highest frequencies. Comparing this to that without feature selection, the 2D PCA plot based on the fittest GA model showed nice clustering (Figure 3c). The heat map of the fittest GA model showed clustering of samples and clustering of the featured components (Figures 4a, 4b, 4c). **Figure 4 a-c**. Heat map plot of the fittest GA model showed better clustering/classification of HILIC **(a)**, RP **(b)** LC-ESI-MS, and GC-TOF-MS **(c)** data in the _Pinus taeda_ L. Predictors are described as mass to charge ratio which is unique characteristic of metabolite. Annotated predictors provided with substance name. **Figure 4 b-c**. Heat map plot of the fittest GA model showed better clustering/classification of HILIC **(a)**, RP **(b)** LC-ESI-MS, and GC-TOF-MS **(c)** data in the _Pinus taeda_ L. Predictors are described as mass to charge ratio which is unique characteristic of metabolite. Annotated predictors provided with substance name. The respective fittest GA model predicted with a specificity range of 0.85 to 0.98 and a sensitivity range of 0.36 to 1.0 for GC/TOF-MS data, a specificity range of 0.87 to 0.99 and a sensitivity range of 0.66 to 0.94 for HILIC-LC/ESI-MS data, and a specificity range of 0.88 to 1.0 and a sensitivity range of 0.74 to 0.96 for RP-LC/ESI-MS data. The metabolite network graph illustrated that many saccharides were interwoven heavily, indicating that carbohydrate metabolic pathways were involved (Figures 2a-c). Palmitic acid and tocopherol also showed strong connections to other molecules. Palmitic acid is a major provider of ATP under beta-oxidation in mitochondria, and long-chain fatty acids such as palmitic acid typically require an antioxidant such astocopherol to prevent peroxidation.