## 3 Results and Discussion
### Mathematical Modeling of The Drying Curves  
In this study, to better describe the solar drying kinetics of _Arbuttus untedo_ L. fruits, the experimental drying data were fitted into nine commonly used thin-layer drying models, listed in Table 1. The suitable fitting model was selected based on both the highest (\(R^{2}\)) and the least (\(\chi^{2}\)). All tested models had high \(R^{2}\) and \(\chi^{2}\) with values ranging from 0.9869 to 0.9980 and 0.0171 to 0.0424, respectively. Table 2 highlights the statistical analysis generated for each of the nine models. Although, Logarithmic, Page, and Midilli-Kucuk models fitted well the experimental data, the Midilli-Kucuk model was the best fitting model with the highest \(R^{2}\) and least \(\chi^{2}\). Similar findings were reported for various fruits drying [65]. The fitting results of Midilli-Kucuk model for all drying experiments are presented in Figure 4. These results prove that the Midilli- Kucuk model fits well with the drying kinetics of _Arbuttus untedo_ L. fruits.  
Figure 4: Experimental drying data of moisture ratio versus drying time as fitted with Midilli-Kucuk model.  
The validation of the chosen model was completed by comparing the model predicted moisture ratios and the experimental at each studied air-drying temperature. The Figures 4 and 5 show its performance at the three drying temperatures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Statistical Parameters**} \\ \cline{3-5}  & & \(R^{2}\) & \(\chi^{2}\) \\ \hline
**Newton** & k & & & \\
60 & 0.0084 & & 0.9951 & 0.0274 \\
70 & 0.0153 & & 0.9920 & 0.0003 \\
80 & 0.226 & & 0.9839 & 0.0469 \\ Average & & & **0.9903** & **0.0248** \\
**Page** & k & n & & \\
60 & 0.0057 & 1.0820 & 0.9964 & 0.0242 \\
70 & 0.0160 & 0.9888 & 0.9920 & 0.0358 \\
80 & 0.0296 & 0.9286 & 0.9849 & 0.0469 \\ Average & & & **0.9911** & **0.0356** \\
**Wang and Singh** & a & b & & \\
60 & \(-\)0.0067 & 0.0000 & 0.9965 & 0.0237 \\
70 & \(-\)0.0121 & 0.0000 & 0.9861 & 0.0472 \\
80 & \(-\)0.0180 & 0.0000 & 0.9782 & 0.0564 \\ Average & & & **0.9869** & **0.0424** \\
**Logarithmic** & a & k & c & \\
60 & 1.2398 & 0.0056 & \(-\)0.2593 & 0.9988 & 0.0136 \\
70 & 1.1184 & 0.0106 & \(-\)0.1720 & 0.9955 & 0.0277 \\
80 & 1.1438 & 0.0139 & \(-\)0.2292 & 0.9915 & 0.0364 \\ Average & & & & **0.9952** & **0.0259** \\
**Diffusion** & a & k & b & & \\
**approximation** & & & & & \\
60 & \(5.926\times 10^{3}\) & 0.0040 & 0.9998 & 0.9983 & 0.0166 \\
70 & \(3.626\times 10^{3}\) & 0.0098 & 0.9998 & 0.9928 & 0.0350 \\
80 & \(3.707\times 10^{3}\) & 0.0151 & 0.9998 & 0.9844 & 0.049 For a given temperature \(\theta\) in \({}^{\circ}\)C, the temperature dependency of the model Midilli-Kucuk parameters was computed. The values of a, b, k and n are used to compute, at any time during the drying process, the moisture content of the product with a greater precision.  
\[\text{a}=0.9366+0.0025\theta-2.2\times 10^{-5}\theta^{2}\qquad\text{n}=0.3998-0.001 43\theta+1.30\times 10^{-4}\theta^{2}\]  
\[\text{k}=-0.0355+0.0012\theta-9.5\times 10^{-6}\theta^{2}\qquad\text{b}=0.5502+0.0231 \theta-3.06\times 10^{-4}\theta^{2}\]