## 3 Results and Discussion  
A central composite design was created to assess the effect and interaction of potential factors that play a role on the mucoadhesive nanoparticles of sitagliptin (MNS) percentage yield (Table 1). Twenty formulations of nanoparticles were prepared (Table 2) to identify the optimal conditions for nanoparticle preparation. Experiments were conducted to evaluate the interaction in random sequence in a face-centered manner, according to our previous study [17]. The response surface graph indicates the percentage yield of the mucoadhesive sitagliptin nanoparticle was between 64.5-91.25% (Table 1), and was not affected by transformations. The following quadratic setup suggests that the response measured is depending on three factors, albumin alcohol concentration (albumin, g), inlet temperature (\({}^{\circ}\)C), and feed flow rate (mL/h). As a result, fixing the optimal formulation will maximize the efficacy of nanoparticle yield, and minimize the effect of other factors in arriving at this point. The polynomial equation between each factor giving the mathematical relationship in coded factor (Table 3) is:  
The numerical optimization study was optimized to identify the accuracy of the experimental model to predict each variable factor in correlation to the measured surface responses. Ten optimum solutions were calculated and analyzed (Table 4) in a double-blind manner, based on the design space in this study. The optimized sitagliptin nanoparticle formulation was then selected, based on which numerical optimization equation can predict the closest results to the actual results. Then, three (3, 6, 10) were randomly selected and investigated for the percentage yield response (Table 4). Data suggest that the percentage yield response was consistent with the response predicted by the design expert software. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Study Type** & **Response Surface** & **Runs** & **20** & & & & & \\ \hline Initial Design & Central Composite & & Blocks & & & No Blocks & & & \\ Design Model & Quadratic & & & & & & & & \\ Factor & Name & Units & Type & Low Actual & High Actual & Low Coded & High Coded & Mean & Std. Dev. \\ A & Albumin Conc & \% & Numeric & 1 & 2 & \(-1\) & 1 & 1.5 & 0.353 \\ B & Inlet Temperature & degree & Numeric & 100 & 120 & \(-1\) & 1 & 110 & 7.071 \\ C & Feed Flow rate & \% & Numeric & 5 & 25 & \(-1\) & 1 & 15 & 7.071 \\ Response & Name & Units & Obs & Analysis & Minimum & Maximum & Mean & Std. Dev. & Ratio \\ Y1 & Percentage yield & \% & 20 & Polynomial & 64.5 & 91.25 & 75.658 & 7.25 & 1.41 \\ \hline \end{tabular}
\end{table} 
Table 1: Design summary of sitagliptin nanoparticles. The effect of the three resultant factors, including albumin concentration (albumin, g), feed flow rate (mL/h), and inlet temperature (\({}^{\circ}\)C) on the nanoparticle percentage yield response was then assessed (Table 5). Data concluded that the albumin concentration and inlet temperature have a significant effect on the percentage yield of the nanoparticles. ANOVA for the response surface model (Table 5) suggests that a lower inlet temperature and a higher albumin concentration resulted in a higher percentage yield (Figure 1), which is consistent with the earlier reports [18]. The relationship between the dependent (albumin concentration and inlet temperature) and independent (feed flow rate) variables on the percentage yield was further investigated using the response surface graph plots. However, data indicate that the nanoparticle percentage yield was higher when the feed flow rate was low (Figure 2), and the correlation coefficient was \(\sim\)0.59. Although the spray rate was not the only factor that was varied, the collection of nanoparticles by hand could also be contributing to this varied response. Furthermore, a decrease in both feed flow rate and inlet temperature leads to a decrease in the sitagliptin nanoparticle percentage yield (Figure 3).