## 3 Results and Discussion
### Determining the Value of the \(n\) Coefficient of the Model  
Table 1 presents all of the empirically selected values of the \(n\) coefficient and the calculated \(N\) coefficient of the model (6) for all of the measurements of water content changes, for the indicated drying temperatures, and for the thickness and type of leek slice (solid cylinder; not separated slice, symbol \"ns\" in table; separated slice, symbol \"s\"). As can be seen, the values of the empirically determined coefficient \(n\) are both greater and less than 1; therefore, \(N\) may be greater or less than 3. In other studies, where this model (6) was used, the empirical coefficient \(n\) was usually taken as greater than or equal to 1, i.e., \(1\leq N\leq 3\), e.g., for carrot [59], pumpkin [62], parsley [63], chokeberry [64], apple pomace [33]. In the case of drying leek slices, for all of the measurements of water content changes, the value of the coefficient \(n\) was ranged between 0.67 and 7; therefore, coefficient \(N\) can range between 1.11 and 201, and theoretically go to infinity [61]. When analysing the values of the \(n\) and \(N\) coefficients, it is not possible to find any dependence on the geometrical features of the leek slices in the dried samples or on the parameters of the drying process. In more cases, the coefficient \(n\) is below unity (\(N>3\)), and only for some samples (especially thin slices) the coefficient \(n\) is greater than 1 (\(N<3\)). It can be considered that the values of the \(n\) coefficient (and therefore also \(N\)) are random. Therefore, we decided to define its probability distribution. Figure 3: Model of water content changes in leek slices 12 mm thick, during drying at temperature 70 \({}^{\circ}\)C, when \(n=0.82\). The frequency of occurrence of the \(n\) coefficient was calculated in intervals of equal size. It was assumed that the mean value \(n\) coefficient from a given interval is a random variable, the probability of which corresponds to the frequency of receiving values belonging to this interval by the coefficient \(n\). The graph of frequency p(\(n\)) is shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Drying Temperature [\({}^{\circ}\)C]** & **Slice Thickness [mm]** & **Type of Slice** & **Coefficient \(n\)*** & **Coefficient \(N\)** \\ \hline \multirow{4}{*}{40} & 4 & ns & 1.01; 0.97; 1.02 & 2.94; 3.2; 2.89 \\  & 8 & ns & 0.85; 1; 0.9 & 4.64; 3; 3.86 \\  & 12 & ns & 0.8; 0.94; 1 & 6; 3.44; 3 \\ \hline \multirow{4}{*}{50} & 4 & ns & 1.42; 1.58; 1.65 & 1.88; 1.73; 1.68 \\  & 8 & ns & 1.29; 1.2; 1.25 & 2.07; 2.25; 2.14 \\  & 8 & s & 0.86; 1.26; 0.91 & 4.45; 2.12; 3.73 \\  & 12 & ns & 0.7; 0.81; 0.75 & 201; 5.65; 9 \\  & 12 & s & 0.74; 0.67; 0.82 & 10.09; 201; 5.35 \\ \hline \multirow{4}{*}{60} & 4 & ns & 1.31; 0.89; 0.85 & 2.04; 3.99; 4.46 \\  & 4 & s & 1.81; 0.88. 0.85 & 1.58; 4.13; 4.64 \\  & 8 & ns & 0.8; 1.49; 1.35 & 6; 1.8; 1.98 \\  & 8 & s & 0.83; 1.04; 0.84 & 5.08; 2.79; 4.85 \\  & 12 & ns & 0.84; 0.93; 1.06 & 4.85; 3.53; 2.79 \\  & 12 & s & 0.89; 0.88; 0.77 & 3.99; 4.13; 7.45 \\ \hline \multirow{4}{*}{70} & 4 & ns & 1.15; 2.8; 1.38 & 2.38; 1.31; 1.93 \\  & 8 & ns & 1.33; 1.74; 2.1 & 2.01; 1.62; 1.46 \\  & 8 & s & 2; 1.06. 0.9 & 1.5; 2.69; 3.86 \\  & 12 & ns & 0.87; 0.82; 0.91 & 4.28; 5.35; 3.74 \\  & 12 & s & 1.82; 1.15; 1 & 1.58; 2.38; 3 \\ \hline \multirow{4}{*}{80} & 4 & ns & 2.3; 7.35 & 1.41; 1.11; 1.26 \\  & 4 & s & 2.2; 1.05; 4.5 & 1.43; 2.74; 1.17 \\  & 182 & ns & 1.82; 3.51; 4.4 & 1.59; 1.23; 1.18 \\  & 8 & s & 1.65; 2.51; 1.13 & 1.68; 1.36; 2.44 \\  & 12 & ns & 0.89; 1.25; 0.97 & 3.99; 2.14; 3.2 \\  & 8 & s & 0.9; 0.94; 0.82 & 3.86; 3.44; 5.35 \\ \hline \hline \end{tabular}  
* for each of the 3 repetitions of water content measurements.  
\end{table} 
Table 1: Coefficients \(n\) and \(N\) of the model (6) depending on the parameters of the drying process. Figure 4: Probability distribution p(\(n\)) of dried leek. With the empirical probability distribution, it is possible to calculate the cumulative distribution function of the random variable.