## 3 Results and Discussion
### Chemical Component Prediction
Figure 9: Chemical-component prediction in turkey from SVR model: (**a**) Prediction plot of fat content; (**b**) Prediction plot of moisture content; (**c**) Prediction plot of protein content. chemical component prediction. As our results were compared with previous meat quality research based on hyperspectral and NIR spectroscopy [14; 24; 33], the results presented from our system showed a limited prediction accuracy. Furthermore, the error rate was higher (RMSE > 1%) than the previous recordings (previous recordings the SEP or RMSEP < 1% and \(R^{2}\) > 90 for moisture, fat, and protein prediction). It is obvious that the previous researchers applied expensive and high-resolution sensors for the meat measurements. However, considering the currently expensive use of HSI technique in the meat industry this system paves the way for small scale industries and big companies. Later, we have also compared the performance of multispectral system with our laboratory based HSI system. Compared with the result of multispectral imaging and HSI in Tables 2 and 3, the HSI performed superior for chemical prediction with all variables, more specifically for sirloin and tenderloin samples. However, except for protein prediction in sirloin and turkey samples, the multispectral imaging showed a reasonable performance for other parameters using only a few wavebands. In addition, the errors for the multispectral system were higher compared with HSI: 8% higher for tenderloin, 29% for sirloin; and 25% for moisture, 36% for fat, and 91% protein. However, in future work, we will able to optimize our system with higher accuracy and fewer errors by incorporating higher resolution LEDs with emission wavelengths in the NIR range. Moreover, to provide the optimal sensor performance, the working LED set, that is, the LED number and wavelengths, should be adopted to each case individually to take the respective chemical component and optical properties of the sample into account. We believe that a significant technical simplification of the LED-based multispectral imaging system, compare to conventional HSI system and commercial multispectral imaging systems, should result in a dramatic reduction of the devices prices without minimal reduction of the performance. \begin{table}
\begin{tabular}{c c|c|c c c|c c c} \hline  & & & \multicolumn{3}{c|}{**Calibration**} & \multicolumn{3}{c}{**Prediction**} \\ \hline
**Sample** & **Parameter** & **Analysis** & **n** & **R\({}^{2}\)c** & **RMSEC** & **n** & **R\({}^{2}\)p** & **RMSEP** & **LV** \\ \hline \multirow{3}{*}{Sirloin} & Fat & & 34 & 0.81/0.99 & 2.76/0.57 & 14 & 0.81/0.89 & 2.95/2.12 & 4 \\  & Moisture & & 34 & 0.90/0.93 & 2.03/1.76 & 14 & 0.70/0.84 & 2.77/2.48 & 7 \\  & Protein & & 34 & 0.46/0.96 & 4.65/1.26 & 14 & 0.06/0.63 & 5.33/2.65 & 3 \\ \hline \multirow{3}{*}{Turkey} & Fat & & 28 & 0.89/0.97 & 4.10/2.25 & 12 & 0.80/0.82 & 4.53/4.01 & 6 \\  & Moisture & & 28 & 0.74/0.96 & 5.55/2.46 & 12 & 0.74/0.85 & 5.59/3.86 & 3 \\  & Protein & & 28 & 0.69/0.99 & 2.83/0.49 & 12 & 0.48/0.69 & 3.51/2.73 & 4 \\ \hline \multirow{3}{*}{Tenderloin} & Fat & & 35 & 0.93/0.96 & 1.07/0.75 & 15 & 0.84/0.95 & 1.15/0.84 & 6 \\  & Moisture & & 34 & 0.90/0.91 & 0.97/0.93 & 14 & 0.84/0.85 & 1.33/1.19 & 7 \\ \cline{1-1}  & Protein & & 34 & 0.83/0.94 & 1.73/1.05 & 14 & 0.83/0.88 & 1.89/1.52 & 3 \\ \hline \end{tabular}
\end{table} 
Table 2: Chemical component prediction in meat samples based on multispectral imaging with standard normal variate (SNV) preprocessing method.