## 3 Results and Discussion
### Optimization of Osmotic Dehydration Process
(2006) concluded that the concentration of the osmotic solution was the primary factor affecting water loss during dehydration of papaya with sucrose,  
Figure 4: Interaction plots for SG% based on glycerol concentration, immersion time, and temperature. Figure 3: Interaction plots for WL% based on glycerol concentration, immersion time, and temperature. with immersion time being the next most important factor [40]. Although the interactions involving temperature and glycerol concentration (AB) were not statistically significant for WL, the interaction plots suggest that increasing both factors still contributes to higher water loss. The data show that increasing glycerol concentration correlated with higher water loss, while optimal water loss occurred at the medium level of immersion time (0), corresponding to 180 min. This observation reinforces the minor role of temperature in affecting WL, in agreement with the Pareto chart analysis. For SG (Figure 2), the Pareto charts highlight immersion time (C) as the most influential factor, followed by temperature (B) and glycerol concentration (A). In contrast to WL, all three factors had a significant effect on SG values. Notably, the interactions between glycerol concentration and immersion time (AC) and between temperature and immersion time (BC) were significant for SG. The interaction plots for SG% (Figure 4) further demonstrated that extended immersion times combined with increased temperature (BC) or higher glycerol concentration (AC) resulted in higher solid gains. These findings are consistent with the study by [PERSON] et al. (2019) [41], who reported that immersion time and temperature were the most significant factors affecting water loss and solid gains during osmotic dehydration of kinnow fruits in a sugar solution, followed by sugar concentration. Similarly, [PERSON] et al. (2006) [40] identified temperature as the primary factor influencing solid gain during the osmotic dehydration of papaya, with immersion time also being crucial. Additionally, the strong influence of temperature on SG during osmotic dehydration has been reported in studies on red algae and kiwiberries [42; 43]. During the optimization of the OD process, Minitab software's response optimizer was set to operate within the value range derived from literature data. The OD process optimization was constrained within literature-derived ranges to ensure model validity, as unconstrained optimization could lead to impractical conditions, such as excessive temperatures degrading phenolic compounds or high glycerol concentrations hindering the transfer of bioactive ingredients, thereby compromising the product's quality, nutritional value, and industrial applicability. The optimization analysis, aimed at maximizing both WL and SG, identified and suggested three optimal experiments/runs, each involving glycerol concentration and temperature close to their high level (+1) but with varying immersion times. These experimental runs are summarized in Table 4, along with their predicted responses, real experimental results, and prediction accuracy. These solutions suggest that while high glycerol concentration and temperature are crucial for achieving high WL and SG, different immersion times can also produce optimal results. This flexibility in immersion time allows for tailored processing conditions depending on specific production requirements or constraints, providing valuable adaptability in industrial applications. Table 5 presents the predicted and actual values of water loss and solid gain under the optimized conditions, as well as the prediction accuracy for each of these responses. The prediction accuracy for the optimized conditions varied, with water loss (WL%) showing high reliability in all runs (from 87.37% to 87.69%) except for the third experiment (67.81%). This indicates a strong ability of the model to predict water loss. In contrast, the accuracy for solid gain (SG%) was moderate to low, ranging from 42.80% to 64.72%, suggesting that the model was less reliable in predicting solid gain.