## 3 Results and Discussion
### Chemical Component Prediction  
Once the parameters were optimized for both the PLSR and SVR models, the optimized parameters with the lowest RMSC were used to predict the chemical components of the meat samples. In this study, the prediction model for the meat data was also contaminated by the outliers; it was perhaps caused by a wrong system response, variability in the measurements, or experimental error. Therefore, identifying and excluding outliers from the data is significant, to prevent false data predictions. To detect outliers in the data, a widely used 3\(\sigma\) rule was applied, by removing values exceeding \(\pm\)3\(\sigma\)[31; 32]. Figure 8 presents an example of the outliers' impact on the fat-prediction accuracy in the sirloin samples. For the fat prediction, the calibration data exhibited a strong correlation and a low error rate (\(R^{2}\)c. 0.83; RMSEC: 2.81%) between the measured and predicted values (Figure 8a). However, for the prediction data set, the correlation value decreased and the model error increased (R\({}^{2}\)p: 0.69; RMSEC: 4.08%), due to the presence of outliers in the data. The outliers were identified by observing the leverage values against the standard residual values (Figure 8b), where some data surpassed the limit of \(\pm\)3\(\sigma\); they were considered outliers and excluded. After excluding outliers from the data, the prediction accuracy was improved with \(R^{2}\)p of 0.81 and RMSEC: 2.95% (Figure 8c). A similar method was used for outlier detection in the other samples and the best results are presented here. Data from the sirloin samples were subjected to moisture and fat content prediction. Compared with the PLSR model, the SVR model showed superior moisture content prediction with \(R^{2}\) of 0.84 and RMSEP of 2.48% (Figure 8d); the accuracy was decreased for the protein content prediction (Figure 8e). For the turkey data, the SVR model revealed a similar accuracy of \(R^{2}\) > 0.80 with a lower error rate for the fat and moisture prediction (Figure 9a,b); however, the accuracy fell again for the protein-content prediction (Figure 9c). A reduction in accuracy could be observed because the region from 458 to 950 nm did not provide the significant information about the protein content. Previous authors also observed moderate accuracy for the protein content prediction in the 400 to 1000 nm region [24]. Both the PLSR and SVR models accurately determined the chemical components in the tenderloin samples. However, in contrast to the PLSR model, the SVR model provided higher accuracies with lower prediction errors for both the calibration and prediction sets. For all the measured parameters, the SVR model exhibited \(R^{2}\)p > 0.84 with RMSEP < 2%, and a bias close to the error values (Figure 10). Figure 7: Counter-plot example obtained from the grid-searching method for optimizing the support-vector regression (SVR) parameters. The arrow indicates a finer grid with the lowest RMSE with the highest prediction. The overall chemical parameter prediction results from both analysis approaches are given in Table 2. Among all of the preprocessing methods, the SNV preprocessed data performed superior for  
Figure 8: Chemical-component prediction in a sirloin sample: (**a**) Prediction plot of fat content from partial least-squares regression (PLSR) model with outliers included; (**b**) Plot for outlier identification in fat data; (**c**) Prediction of fat content from PLSR model with outliers excluded; (**d**) Prediction of moisture content with SVR model; (**e**) Prediction of protein content with SVR model. The black dots are for calibration sample-set and red dots are for test-sample set. Figure 10: Chemical-component prediction in tenderloin from SVR model: (**a**) Prediction plot of fat content; (**b**) Prediction plot of moisture content; (**c**) Prediction plot of protein content.