# Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging
## 4 Conclusions  
This study formulates true-color images to extract accurate ROIs similar those from human perception. This study also proposed a novel _iso-bestic_ based Mb spectral features extraction for minced meat (Bovine, Ovine and Poultry) using an HSI-system. The extracted features describe the formation of proteins pigments, i.e., the chemical composition of meat respiratory pigments. These extracted features are later fed to a nonlinear SVM classifier for the classification of minced meat. The experimental results reveal that the proposed pipeline significantly improves the performance as compared to the state-of-the-art methods. The classical methods use spectral features such as reflectance and absorption to classify the _chunk_ meat types, whereas in this study, we used _iso-bestic_-based Mb spectral features for minced meat-type classification with an average accuracy of 88.88%. The future direction of our study is to analyze the classification accuracy based on several deep architectures, such as AlexNet, ResNet, DesNet and GoogleNet.  
Conceptualization, H.A., M.A. ([PERSON]), A.S., M.N.Y., M.A. ([PERSON]) and M.H.K.; data curation, H.A., M.A. ([PERSON]) and Z.S.; formal analysis, H.A.; investigation, H.A. and M.A. ([PERSON]); methodology, H.A., M.A. ([PERSON]), A.S. and M.A.Z.; supervision, M.A. ([PERSON]) and A.S.; validation, H.A., M.A. ([PERSON]), M.N.Y. and Z.S.; visualization, H.A., M.A. ([PERSON]), M.N.Y., M.A. ([PERSON]), M.H.K. and Z.S.; writing--original draft, H.A., M.A. ([PERSON]), M.A.Z., M.H.K. and Z.S.; writing--review and editing, H.A., M.A. ([PERSON]),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Classifier** & **Optimization** & **Accuracy** \\ \hline Reflectance Spectrum + PCA [32] & SMO & rbf ; Tolerance=0.001; C=1.0 & 82.01\% \\ Reflectance Spectrum + PCA [25] & 2-Step SVM & rbf ; Tolerance=0.001; C=1.0 & 94.00\% \\ Reflectance + PCA + GLGCM [51] & SVM & rbf ; Tolerance=0.001; C=1.0 & 72.22\% \\ \hline \multicolumn{4}{c}{**Proposed Methodology**} \\ \hline Spectral Features & SVM & rbf ; Tolerance=0.001; C=1.0 & 88.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative results with the state-of-the-art methods using same test data.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample \#**} & \multicolumn{3}{c}{**Bovine**} & \multicolumn{3}{c}{**Poultry**} & \multicolumn{3}{c}{**Ovine**} \\  & **Class 1** & **Class 2** & **Class 3** & **Class 1** & **Class 2** & **Class 3** & **Class 1** & **Class 2** & **Class 3** \\ \hline
1 & 0.980 & 0.0 & 0.200 & 0.0 & 0.999 & 0.0 & 0.185 & 0.0 & 0.8143 \\
2 & 0.9513 & 0.0 & 0.048 & 0.0 & 0.999 & 0.0 & 0.037 & 0.0 & 0.963 \\
3 & 0.833 & 0.0 & 0.166 & 0.0 & 0.999 & 0.0 & 0.7680 & 0.0 & 0.232 \\
4 & 0.929 & 0.0 & 0.070 & 0.0 & 0.999 & 0.0 & 0.0013 & 0.0 & 0.998 \\
5 & 0.209 & 0.01 & 0.790 & 0.0003 & 0.9996 & 0.0 & 0.216 & 0.0 & 0.783 \\
6 & 0.9753 & 0.0 & 0.024 & 0.0 & 0.991 & 0.009 & 0.188 & 0.0 & 0.811 \\ Accuracy & & 81.01\% & & & 99.97\% & & & 76.88\% & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Bovine, Ovine and Poultry minced meat classification performance; class1, class2 and class3 represent Bovine, Poultry and Ovine, respectively.  
A.S., M.N.Y., M.A.Z., M.A. ([PERSON]), M.H.K. and Z.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** \"This research received no external funding\"  
**Conflicts of Interest:** \"The authors declare no conflict of interest.\"  
**Declaration of Competing Interest:** KFUEIT plans to file a patent based on this work, in which [PERSON], [PERSON] and [PERSON] are inventors.