## 3 Results and Discussion  
### Optimization of Osmotic Dehydration Process  
Response surface methodology was utilized to optimize and maximize both dependent variables, i.e., WL and SG. In the context of food processing, increasing WL is paramount in osmotic dehydration processes, where the goal is to effectively remove moisture [38]. Conversely, enhancing solid gain is desirable when enriching products with bioactive compounds, thereby improving their nutritional profiles [14; 27]. As mentioned in Section 2.3, three of the fifteen experiments were conducted with all factors set at the medium (0) level (Table 1) to verify the model's precision by calculating the average and standard deviation of these experiments. The model proved to be quite precise, with the WL and SG mean values and standard deviations of these three similar experiments being 53.06 \(\pm\) 5.52% and 12.36 \(\pm\) 1.24%, respectively. Initially, a comprehensive quadratic model, incorporating quadratic, linear, and linear two-way interaction terms, was selected to fit the data. The ANOVA results for the two key responses--WL and SG of the strawberry slices post-osmotic dehydration--are detailed in Tables S1 and S2 of the Supplementary Materials, respectively. All response data were fitted to quadratic surfaces, and the corresponding Pareto charts, and regression equations for WL% and SG% are presented in Figures 1 and 2, respectively, while their determination (R\({}^{2}\)), adjusted (R\({}^{2}\)adj), and predicted (R\({}^{2}\)pred) coefficients are presented in Table 3. In all cases, the determination and adjusted coefficient were in close agreement, indicating a good fit of the Box-Behnken model to the data, with R\({}^{2}\) and R\({}^{2}\)adj values near 1 and a difference of less than 0.2. Moreover, the non-significant lack-of-fit values (\(p=0.835\) for WL and \(p=0.562\) for SG, Tables S1 and S2 of Supplementary Materials) further confirm the robustness of the models, suggesting they are suitable for predicting these responses. Moreover, the model significance was strong, with a \(p\)-value of 0.017 for water loss (WL%) and 0.007 for solid gain (SG%), indicating statistically significant models. Notably, the model could predict WL% and SG% values for new strawberry samples with over 58% and 53% accuracy, respectively. The significance of the model terms is presented in decreasing order in the Pareto chart (Figures 1 and 2). The interaction plots for WL% and SG% (Figures 3 and 4 respectively), were analyzed to assess the influence of the interaction terms of glycerol concentration (AB, AC), immersion time (AB, BC), and temperature (AC, BC). Figure 1: Regression equation and Pareto chart showing the effect of factors on WL%. Figure 2: Regression equation and Pareto chart showing the effect of factors on SG%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **S** & **R-sq** & **R-sq (adj)** & **R-sq (pred)** \\ \hline WL\% & 4.17821 & 93.43\% & 81.61\% & 58.03\% \\ SG\% & 1.20425 & 95.46\% & 87.27\% & 53.72\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Determination (R\({}^{2}\)), adjusted (R\({}^{2}\)adj), and predicted (R\({}^{2}\)pred) coefficient for WL% and SG%. Analysis of the Pareto charts and interaction plots provides valuable insight into the factors influencing WL and SG during osmotic dehydration. According to the Pareto charts, where factors exceeding the vertical red line are considered significant, it is evident that for WL (Figure 1), immersion time (C), as well as the concentration % of glycerol in the osmotic solution (A), were the most significant factors affecting the response. Temperature (B), along with its interactions with the other factors (AB, BC), did not show a statistically significant influence on WL. This findings align with [PERSON] et al. (2019) [39], who reported a strong influence of solute concentration on the dehydration process of kiwi fruit. Similarly, [PERSON] et al.