## 3 Results
### Principal Component Analysis  
LIBS coupled with chemometrics methods offers a robust methodology to identify and classify various materials. PCA is a machine learning tool applied to data having high-ranked dimensions. The PCA test suppressed the dataset to significantly low dimensions from high-ranking dimensions correlated variables known as principal components (PCs). PCs are classified in descending order of the incorporated variance; PC1 includes the largest variance, and so on. PCA reduces the high dimensions using a scoring matrix [38; 56] and is further used for the classification of the sample. The 10 _P. harmala_ samples averaged spectra within spectral-region varies from wavelength 220-720 nm are listed in Figure 6. The different spectral resemblances were noticed from the recorded LIBS spectra (three spectra's) of the doped samples. The 10 emission spectra were chosen as an average of 5 spectrum belongs to each sample. Two significant parameters such as eigenvalues and score plots are created using the PCA technique.  
Here, we employed PCA on the LIBS-spectra of 10 _P. harmala_ samples having four classes namely; \(\alpha\), \(\beta\), \(\gamma\), and \(\Delta\). Three PCs calculated from the eigenvalues of the score matrix describe 87.6%, 4.6%, and 2.5% of variance for PC1, PC2, and PC3 respectively. The total-variance of the initial averaged LIBS spectral-data covered by the 3 PCS is -94.7%. In Figure 7, the scree plot describes the variance covered by each PC from the LIBS spectral data set. The scree plot also recommends the essential PCs regard for classification purposes. The eigenvalues corresponding to the various PCs are plotted along the \(y\)-axis, showing 4 effective PCs having a maximum variance of the LIBS data. These four efficient PCs are enough to apply for the data discriminability; however, we only consider three PCs for the study of data clustering because three dimensions can be visualized at a time. Moreover, those spectral lines with significantly high SNR were selected to sketch the 3D-cluster-plot.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Element Chemical Composition (\(\upmu\)g/g) \(\pm\) SD** & & \\ \hline  & **Sample-(\(\upmu\))** & **Sample-(\(\upmu\))** & **Sample-(\(\upmu\))** & **Sample-(\(\upmu\))** \\ \hline Li (\%) & 2.87 \(\pm\) 0.03 & 3.26 \(\pm\) 0.01 & 2.18 \(\pm\) 0.02 & 2.67 \(\pm\) 0.05 \\ C (\%) & 36.64 \(\pm\) 0.16 & 34.81 \(\pm\) 0.0  
We chose the line intensity of Ca (II), Zn (I), as well as C (I) at 393.4 nm, 481.05 nm, and 247.9 nm wavelengths, respectively. The 3D-cluster-plot was drawn with limited input-variables for discrimination of 4 _P. harmala_ samples, as shown in Figure 8. The four isolated clusters, show various well-separated _P. harmala_ samples including Ca, C, and Zn spectral lines. Hence, the spectral measurements of the three chosen elements Zn, C, and Ca present in the _P. harmala_ samples show that these spectral lines can be suitable for the discrimination of plant samples.  
Figure 6: Average LIBS spectrum of the 3 doped _P. harmala_ samples-(\(\beta\), \(\gamma\), and \(\Delta\)) and one reference un-doped sample-(\(\alpha\)).  
Figure 7: Scree plot of the PCA analysis using averaged LIBS data.