## 2 Materials and Methods
### Drying Characteristic  
The drying characteristics of cassava leaves were studied using a high-precision hot-air laboratory dryer at the Institute of Agricultural Engineering, Hohenheim University [18]. Approximately 1 cm was removed from both tips and stalks of the leaves, which were then chopped into cross-sectional lengths of 2-3 cm. For each batch, the chopped samples were unfolded and evenly spread onto a stainless-steel mesh tray, maintaining a load of 1 kg per 1 m\({}^{2}\), with a sample thickness of approximately 4 cm. During the experiments, the leaves were dried without turning. Drying temperatures were set at 40, 50, 60, 70, and 80 \({}^{\circ}\)C, with a constant air velocity of 0.5 m/s using over- and underflow mode. The specific humidity of the drying air was maintained at 25 g/kg dry to simulate typical conditions in Thailand. The drying experiments were conducted in duplicate. During the drying process, the samples were automatically weighed every 30 min, and the mass was recorded to monitor water loss until a constant mass was achieved. The recorded sample mass was used to calculate the moisture content at specific time intervals using Equation (1). The dried samples were immediately packed and sealed in aluminum foil bags and left overnight at room temperature to achieve homogeneous moisture content. The moisture content of samples was analyzed as described in Section 2.4. The moisture ratio (MR) during drying was calculated using Equation (2), while the drying rate (DR) at each drying temperature was determined using Equation (3). Six mathematical-thin layer models ([PERSON], Page, Henderson and Pabis, Two-term exponential, Verma, and Diffusion approximation) were applied to the experimental data to identify the most suitable model describing the drying behavior of cassava leaves. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean square error (RMSE) for the drying models were calculated using Equations (4), (5), and (6), respectively. The most suitable drying model was selected based on the lowest MAPE and RMSE values, along with the highest R2 value.  
\[\text{MC}_{\text{t}} =100\times\left(1-\left(\frac{\text{W}_{\text{eq}}}{\text{W}_{ \text{t}}}\times\frac{100-\text{MC}_{\text{eq}}}{100}\right)\right) \tag{1}\] \[\text{MR} = \frac{\text{MC}_{\text{t}}-\text{MC}_{\text{eq}}}{\text{MC}_{0}- \text{MC}_{\text{eq}}}\] (2) \[\text{DR} = \frac{\text{MC}_{\text{t+dt}}-\text{MC}_{\text{t}}}{\text{dt}}\] (3) \[\text{R}^{2} = 1-\frac{\sum_{\text{t=1}}^{\text{n}}\left(\text{MR}_{\text{exp}} -\text{MR}_{\text{pre}}\right)^{2}}{\sum_{\text{t=1}}^{\text{n}}\left(\text{ MR}_{\text{exp}}-\text{MR}_{\text{pre}}\right)^{2}}\] (4) \[\text{MAPE} = \frac{100}{\text{n}}\sum_{\text{t=1}}^{\text{n}}\left|\frac{ \text{MR}_{\text{exp}}-\text{MR}_{\text{pre}}}{\text{MR}_{\text{exp}}}\right|\] (5) \[\text{RMSE} = \sqrt{\frac{1}{\text{n}}\sum_{\text{t=1}}^{\text{n}}\left(\text{ MR}_{\text{exp}}-\text{MR}_{\text{pre}}\right)^{2}} \tag{6}\]  
where t is drying time, MC0 is initial moisture content, MCt is moisture content at time t min, MCt+dt is moisture content at time t + dt min, MCeq is moisture content at equilibrium, Wt is sample weight at time t min, Weq is sample weight at equilibrium, MEexp is experimentally observed moisture ratio, and MRpre is predicted moisture ratio.