## 3 Results
### RSM Model and Analysis of Variance
\begin{tabular}{c} **Reduced** \\ **Quadratic Model** \\ \end{tabular}  \\ \hline Intercept & 9.97 & 9.98 & 5.88 & 5.88 \\ X\({}_{1}\)-Temperature (\({}^{\circ}\)C) & \(-\)0.098 * & \(-\)0.098 * & 0.72 *** & 0.72 *** \\ X\({}_{2}\)-Time (Min) & \(-\)0.216 ** & \(-\)0.216 ** & 0.39 *** & 0.39 *** \\ X\({}_{3}\)-H\({}_{2}\)SO\({}_{4}\) (M) & \(-\)0.121 * & \(-\)0.212 * & 1.37 *** & 1.37 *** \\ X\({}_{1}\)X\({}_{2}\) & \(-\)0.365 ** & \(-\)0.365 *** & 0.23 ** & 0.23 ** \\ X\({}_{1}\)X\({}_{3}\) & \(-\)0.34 *** & \(-\)0.34 *** & \(-\)0.23 & - \\ X\(The effect of the interaction between any two factors (X 1, X2 and X 3) was significant for ED-F1 yield. Similar occurrence was observed for HMF by-product except for the non-significance between interactions of X1X3. The insignificant coefficients were removed from the second-order polynomial model. Thus, the reduced regression equation with the coded values was established as the following:  
\[\text{ED-F1 yield (g)}=9.97-0.098\text{X}_{1}-0.216\text{X}_{2}-0.121\text{X}_{3}-0. 722\text{X}_{1}{}^{2}-1.19\text{X}_{3}{}^{2}-0.365\text{X}_{1}\text{X}_{2}-0.34 \text{X}_{1}\text{X}_{3}-0.158\text{X}_{2}\text{X}_{3}; \tag{3}\]  
\[\text{HMF by-product (g/L)}=5.88+0.72\text{X}_{1}+0.39\text{X}_{2}+1.37\text{X}_{3}+0. 90\text{X}_{1}{}^{2}-1.05\text{X}_{3}{}^{2}+0.23\text{X}_{1}\text{X}_{2}-0.0063 \text{X}_{2}\text{X}_{3}; \tag{4}\]  
where X1 is hydrolysis temperature, X2 is hydrolysis time and X3 is H2 SO4 concentration. The reduced quadratic models were statistically significant with the \(p\)-value for both responses (ED-F1 yield and HMF) shows <0.0001, indicating the model is statistically significant at 95.0% confidence interval (\(p<0.05\)). Furthermore, the aptness of the second-order polynomial model was confirmed by the insignificant lack of fit F-values (\(p>0.05\)), indicating the lack of fit is not significant relative to the pure error in this experiment [50]. The regression coefficient (\(R^{2}\)) was well adjusted for the experimental data (ED-F1 yield adjusted \(R^{2}=0.9836\), HMF adjusted \(R^{2}=0.9616\)) which indicates that the quality of the models was retained even after the removal of some terms. Based on the adjusted \(R^{2}\) value for both models, only 1.74% and 3.94% of the total variation is not explained by both ED-F1 yield and HMF models, respectively, with the variations in the ED-F1 yield (98.36%) and HMF content (96.16%) are direct results to changes in the temperature, time and H2 SO4 concentration. The difference between the predicted \(R^{2}\) and the adjusted \(R^{2}\) for both models were in reasonable agreement (less than 0.2) while the model fitting was better as R2 value observed is closest to 1 [51]. Moreover, the lower value of CV (<5) for ED-F1 implies the low deviations between the experimental data values and the predicted data values. Models with low CV values (<10) are still considered to have good reliability and reproducibility [52]. The \"Adeq Precision\" which implies the signal-to-noise ratio, indicates an adequate signal (>4) for all models and can be used to navigate the design space [49,50,53]. Effects of Hydrolysis Parameters on the Yield of Oligosaccharides Fraction ED-F1 and HMF By-Products  
Based on Table 1, ED-F1 yield ranged from 7.65 to 10.11 g according to the changes in the levels of extraction parameters. The lowest yield was obtained under the conditions of X1: 135 \({}^{\circ}\)C, X2: 25 min and X3: 0.2 M H2 SO4, while the highest yield was observed in X1: 125 \({}^{\circ}\)C, X2: 25 min and X3: 0.13 M H2 SO4. Time of extraction (X2) (\(\beta\): \(-0.216\)) exhibited the highest significance (\(p<0.001\)) for the yield of ED-F1 as compared to both temperature (X1) (\(\beta\): \(-0.098\)) and H2 SO4 concentration (X3) (\(\beta\): \(-0.121\)) (\(p<0.05\), respectively), while for the HMF production in the extraction liquor, all three parameters are equally significant (\(p<0.001\)) with all positive values of the regression coefficients (Table 2).