## 3 Results and Discussion
### Garson Sensitivity Analysis  
Once we obtained the artificial neural network model synthesizing the complex thermal processes inside the solar dryer in a single mathematical expression, a sensitivity analysis was performed using the Garson equation [44]. This determined the relative importance of the input variables on the drying velocity of the agricultural products evaluated. For this purpose, [PERSON]'s equation takes into consideration the values of the weights (presented in Table 6), estimating the relative importance of each variable from the following mathematical expression:  
\[I_{j}=\frac{\sum_{m=1}^{N_{h}}\left(\left(\left|W_{jm}^{ih}\right|/\sum_{k=1}^ {Ni}\left|W_{km}^{ih}\right|\right)\cdot\left|\left|W_{mn}^{ho}\right|\right| \right)}{\sum_{k=1}^{N_{h}}\left[\sum_{m=1}^{N_{h}}\left(\left(\left|W_{jm}^{ih }\right|/\sum_{k=1}^{Ni}\left|W_{km}^{ih}\right|\right)\cdot\left|\left|W_{mn}^{ ho}\right|\right|\right)\right]}, \tag{11}\]  
where \(I_{j}\) is the relative importance of the input variable \(j\) over the output variable, \(N_{i}\) and \(N_{h}\) are the numbers of input and hidden neurons, respectively, and \(W\) is the weight of the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{3}{*}{**Number of Neurons (s) a**} & \multicolumn{8}{c}{**Weights**} & \multicolumn{3}{c}{**Output Layer (\(\boldsymbol{l}=\boldsymbol{l}\)) IW (\(\boldsymbol{s}\),\(\boldsymbol{l}\))**} \\ \cline{2-10}  & \multicolumn{4}{c}{**Hidden Layer (\(\boldsymbol{s}=\boldsymbol{\delta}\), \(\boldsymbol{k}=\boldsymbol{7}\)) IW (\(\boldsymbol{s}\),\(\boldsymbol{k}\))**} & \multicolumn{2}{c}{**(\(\boldsymbol{l}=\boldsymbol{1}\)) IW (\(\boldsymbol{s}\),\(\boldsymbol{l}\))**} \\ \cline{2-10}  & \multicolumn{1}{c}{\(\boldsymbol{G_{T}}\)} & \multicolumn{1}{c}{\(\boldsymbol{T_{a}}\)} & \multicolumn{1}{c}{\(\boldsymbol{RH}\)} & \multicolumn{1}{c}{\(\boldsymbol{w_{p}}\)} & \multicolumn{1}{c}{\(\boldsymbol{T_{t}}\)} & \multicolumn{1}{c}{\(\boldsymbol{T_{o}}\)} & \multicolumn{1}{c}{\(\boldsymbol{V_{f}}\)} & \multicolumn{1}{c}{\(\boldsymbol{v_{d}}\)} & \multicolumn{1}{c}{\(\boldsymbol{b_{1}(s)}\)} & \multicolumn{1}{c}{\(\boldsymbol{b_{2}(l)}\)} \\  & \multicolumn{1}{c}{\(\boldsymbol{(k=1)}\)} & \multicolumn{1}{c}{\(\boldsymbol{(k=2)}\)} & \multicolumn{1}{c}{\(\boldsymbol{(k=3)}\)} & \multicolumn{1}{c}{\(\boldsymbol{(k=4)}\)} & \multicolumn{1}{c}{\(\boldsymbol{(k=5)}\)} & \multicolumn{1}{c}{\(\boldsymbol{(k=6)}\)} & \multicolumn{1}{c}{\(\boldsymbol{(k=7)}\)} & \multicolumn{1}{c}{\(\boldsymbol{(\boldsymbol{s},\boldsymbol{l})}\)} & \multicolumn{1}{c}{\(\boldsymbol{b_{1}(s)}\)} & \multicolumn{1}{c}{\(\boldsymbol{b_{2}(l)}\)} \\ \hline
1 & \(-1.657\) & \(-2.9817\) & \(-2.7513\) & \(2.5193\) & \(14.1473\) & \(-5.7701\) & \(5.5048\) & \(-0.9467\) & \(-4.2106\) & \(-0.6046\) \\
2 & \(-4.2833\) & \(-8.6675\) & \(-3.1531\) & \(2.8148\) & \(8.0529\) & \(-4.7781\) & \(3.778\) & \(0.2534\) & \(7.0302\) & \\
3 & \(-5.4441\) & \(5.1909\) & \(5.2031\) & \(-1.229\) & \(8.7126\) & \(0.7922\) & \(5.2029\) & \(0.4303\) & \(-10.0586\) & \\
4 & \(3.8771\) & \(3.3046\) & \(1.0957\) & \(-4.0755\) & \(2.0218\) & \(-5.9647\) & \(8.0048\) & \(0.8152\) & \(-9.7295\) & \\
5 & \(-5.1569\) & \(5.2198\) & \(5.9442\) & \(-0.798\) & \(-8.2684\) & \(0.7867\) & \(5.2382\) & \(0.7854\) & \(0.5342\) & \\
6 & \(3.9835\) & \(0.019\) & \(4.3298\) & \(-4.736\) & \(5.233\) & \(4.323\) & \(3.2496\) & \(0.3684\) & \(-15.1834\) & \\
7 & \(3.4842\) & \(-3.6018\) & \(4.2551\) & \(4.0861\) & \(12.6289\) & \(0.0656\) & \(-2.6615\) & \(-0.0366\) & \(-14.5054\) & \\
8 & \(-5.6277\) & \(-1.8747\) & \(3.4161\) & \(-1.137\) & \(12.6587\) & \(2.5455\) & \(-3.6899\) & \(0.025\) & \(-2.1126\) & \\ \hline \hline \end{tabular}  
* \(s\) is the number of neurons in the hidden layer, \(k\) is the number of neurons in the input layer, and \(l\) is the number of neurons in the output layer (\(l=1\)).  
\end{table} 
Table 6: Weighting and bias parameters obtained for the best ANN model.