## 3 Results
### Principal Component Analysis
Therefore, increasing values of the Hausner ratio and Carr index increase the value of the first principal component. From Figure 8b, it can also be seen that hygroscopicity (100%) has a negative correlation coefficient (\(-\)0.431) on component PC-1. Furthermore, as can be seen in Table 6, the second principal factor has a positive correlation (0.663) with moisture content, while PC-2 has a negative correlation (\(-\)0.500) with WHC. The Carr index was positively related to the Hausner ratio, while these parameters had negative correlation with hygroscopicity. Moisture content was negatively associated with  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables** & **Moisture Content** & **Hue Angle** & **Carr Index** & **Hausner Ratio** & **WHC** & **Hygroscopicity (100\%)** \\ \hline Hue angle & \(-\)0.223 & & & & \\ \hline \(p\)-value & 0.212 & & & & \\ \hline Carr index & \(-\)0.355 & \(-\)0.473 & & & \\ \hline \(p\)-value & 0.042 & 0.005 & & & \\ \hline Hausner ratio & \(-\)0.353 & \(-\)0.455 & 0.996 & & \\ \hline \(p\)-value & 0.044 & 0.008 & 0.000 & & \\ \hline WHC & \(-\)0.527 & 0.903 & \(-\)0.308 & \(-\)0.295 & \\ \hline \(p\)-value & 0.002 & 0.000 & 0.081 & 0.096 & \\ \hline Hygroscopicity (100\%) & \(-\)0.914 & 0.234 & 0.330 & 0.335 & 0.523 & \\ \hline \(p\)-value & 0.000 &  
the WHC and hue angle. The first four principal components explain 89.5% of the variation in the data. It should be noted that hygroscopicity (PC1), WHC, and moisture content (PC2) are physical parameters, important in industrial applications, as they affect flowability, cohesiveness, handling, shelf life, stability, and powder quality, particularly in the food and pharmaceutical industries. High cohesiveness (PC1), as indicated by the Hausner ratio and Carr index, correlates with poor flowability, limiting suitability for automated packaging systems. The Pareto chart of the PCA results in Figure 9a shows the explained and accumulated variance for each principal component in the spray-dried potato starch at concentration of 3% [SD_3%]. According to the results presented in Figure 9a, the first two principal components (PC-1 and PC-2) have eigenvalues greater than 1. The first two components explain 76.7% of the variation in the experimental data, where component 1 and component 2 are the same. The second component 2 is the same  
2 account for 57.5% and 19.3% of the variance, respectively. In the loading plot (Figure 9b and Table 7), the variables that have the highest positive correlation with the first principal component (PC-1) are the Carr index (0.418) and Hausner ratio (0.409). From Figure 9b, it can also be seen that the variables moisture content and WHC have negative correlation coefficients (\(-\)0.460 and \(-\)0.404, respectively) on the component PC-1. The second principal factor (PC-2) has a negative correlation (\(-\)0.730) with the hue angle. As shown in Figure 9, the hue angle is negatively related to hygroscopicity and D(4.3), while the Carr index and Hausner ratio are negative related to WHC and moisture content. The first three principal components explain 85.8% of the variation in the data. The Pareto chart of the PCA results in Figure 10a shows the explained and accumulated variance for each principal component in the of spray-dried potato starch at a concentration of 5%. According to the results presented in Figure 10a, the first two principal components