## 3 Results and Discussion
### Determination and Classification of Sediment Samples  
The determination and classification of the sediment samples contribute to the ability to distinguish the quality and characteristics of the sediments influencing the water quality. The quality and characteristics of the sediments depend on the differences and corresponding concentrations of the components in the sediments, which can be confirmed by the differences between the integrated spectral intensity and the wavelengths of the sediment measurements.  
Before the application of PCA, area normalization, a preprocessing method, was used to scale the samples to ensure that all of the data were on approximately the same scale. The signal could be compensated for spectral changes due to matrix effects and variations in experimental conditions [12]. The classification process of the spectra could extend the calculation time and increase the performance requirements of the equipment for the LIBS measurements. Hence, the removal of undesired variables and the selection of only a few crucial emission lines were significant for the multivariate analyses.  
The application of PCA transformed the full spectra into several PCs; the first seven PCs explained 90.75% of the variations in the original spectral information. Based on the score plot of the PCA, two PCs were selected as optimal PCs to explain the results of the full spectra (Figure 4a). The loading plot of the seven PCs was drawn to select the important emission lines, as shown in Figure 4b. Most of the emission lines of the main elements (Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, Pb, and Si) exhibited relatively large loading coefficient values, which were consistent with the labeled lines for each element in Figure 4b. The emission lines with high signal-to-noise ratios were selected for further analysis. A total of 11 characteristic lines corresponding to the LIBS spectral peaks of Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, Pb, and Si were chosen from the identified emission lines. A matrix of 896 by 11 (number of observations by lines) was then obtained to implement the subsequent analysis.  
Another PCA was conducted by using the LIBS spectra at the selected characteristic lines to display any variations among the four types of sediment samples. The first two PCs explained 93.56% (PC-1: 79.21% and PC-2: 15.25%) of the variations among the total spectral results; Figure 5 shows their scores and loading plots. Each point in the scatter plot represented one spectrum. Figure 5a shows that an apparent clustering could be produced with PC-1 and PC-2. The LIBS spectra of the sediments were distinguished on the side of PC-1, whereas some spectra tended to be on the positive side of PC-2. Notably, there were distinct differences among the four groups of sediment samples.  
Figure 3: RSD results with an increasing starch-mixing ratio: (**a**) Cd at 361.051 nm, (**b**) Cr at 274.898 nm, and (**c**) Pb at 537.210 nm.  
Figure 5b shows the loading plot of the PCA, which indicated the importance of the analyzed variables. The data show that Al, Ca, and Na provided the dominant contributions to PC-1 and PC-2. To completely explain the scattering of the score plot, the loading coefficient, which is shown in Figure 5b, was utilized to analyze the scattering distribution of the four types of sediments. The Nakdong and Yeongsan classes, which had relatively high concentrations of Al and Ca, were located on the positive side of PC-1, and the Han and Geum classes, which had relatively low concentrations of Al and Ca, were located on the negative side of PC-1. In addition, the classes of Nakdong and Geum, which contained relatively high concentrations of Na, were distributed on the positive side of PC-2, and the classes of Han and Yeongsan, which had relatively low concentrations of Na, were scattered on the negative side of PC-2.  
Figure 4: Score plot of the first two PCs (**a**) and loading plot of the seven PCs (**b**); the plots of the seven PCs from the PCA use the full spectra of the sediment samples.