# Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features
## 3 Results and Discussion
### Classification of Minced Meat
#### 3.2.1 3D-DL and Architecture  
In a generalization of the previous studies, deep models outperform conventional methods due to the non-linear information it preserves such as edge, shape, intensity points, and color. Therefore, in the proposed 3D-convolutional model, numerous empirics were conducted to construct effective, optimal architecture. During construction of the 3D-DL model over-fitting may occur, therefore to avoid the process of over-fitting the dropout technique [39] was implemented by using dropout layers. The details of the proposed structure are given in Table 1, in which conv3D represents the convolutional layer and max3D represents the pooling layer for downsampling. The proposed convolutional structure follows the hierarchy of 6 layers in total: A layer of 3-Dimensional input with the size of \(X,Y,\lambda\) (\(X=row,Y=colums,\lambda=channels\)). A total of two 3D convolutions are used to extract the feature maps, which is followed by a 3D max-pooling layer. A kernel size of 8 and 4 is used for each convolution layer with \(3\times 3\times 3\) filters for each layer. The activation function at each layer is'relu'.  
Furthermore, the features maps are fully connected (dense layer) with a 256-layer feature extractor through the activation function \"relu\". The output layer is then fully connected with a 3 softmax layer to predict the valuable outcome of minced meat. Moreover, in a deep model learning, rate is also a sensitive issue; therefore, in this work, an Adam [40] optimizer is implemented with a step size of 0.001. In general, the model required 30 epochs to train until its convergence and the converge model performed adequately and showed a stable convergence in both validation and training, as presented in Figure 7d.  
Figure 6: Reduced spectrum of minced meat, representing Myoglobin pigments (Mb) pigments information for each minced meat types.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layers** & **Kernels** & **Filters** & **Activation** & **Output Size** & **Params** \\ \hline Input layer & — & — & — & (None, 60, 50, 44) & 0 \\ \hline conv3d (Conv3D) & \(8\) & \(3\times 3\times 3\) & relu & (None, 58, 48, 42) & 224 \\ \hline conv3d (Conv3D) & \(8\) & \(3\times 3\times 3\) & relu & (None, 56, 46, 40) & 1736 \\ \hline max pooling & — & — & — & (None, 28, 23, 20) & 0 \\ \hline flatten (Flatten) & — & — & — & (None, 51520) & 0 \\ \hline dense (Dense) & — & — & relu & (None, 256) & 13,189,376 \\ \hline dropout (0.4) & — & — & — & (None, 256) & 0 \\ \hline dense (Dense) & — & — & — & (None, 3) & 771 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure for 3D-DL Architecture.  
Figure 7: Confusion matrix for 17 blind spatial-spectral cubes, where (**a**,**c**,**e**) represent the confusion matrix of Support Vector Machine (SVM), \(K\)-Nearest Neighbor (KNN) and Deep Learning (DL) model using proposed band reduction method; and (**b**,**d**,**f**) represents the confusion metrics for entire spectral bands; (**g**,**h**) represent the training and validation loss/accuracy curves for the proposed architecture.