# Prediction of Phytochemical Constituents in Cayenne Pepper Using MIR and NIR Spectroscopy
## 3 Results
### Prediction of DM, TPC, FRAP, and CUPRAC Using MIR Spectroscopy  
The investigation of the MIR spectral data by PLS regression revealed good calibration results for the four parameters of interest but poor predictive ability when applied to the validation dataset (Figure 4; Table 4). The best-performing model was for DM content, with an \(\text{R}^{2}_{\text{pred}}\) of 0.54, RMSEP of 0.51%, and RPD of 1.51. The other models showed \(\text{R}^{2}_{\text{pred}}\) of \(\leq\)0.2 and RPD values below 1.5. Again, the pre-processing method had a moderate impact on model accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Pre-Processing**} & \multirow{2}{*}{**Analytic**} & \multirow{2}{*}{**LVs (PLS)**} & \multicolumn{2}{c}{**R\({}^{2}_{\text{cal}}\)**} & \multicolumn{2}{c}{**RMSEC**} & \multicolumn{2}{c}{**R\({}^{2}_{\text{pred}}\)**} & \multicolumn{2}{c}{**RMSEP**} & \multicolumn{2}{c}{**RPD**} \\ \cline{3-13}  & & & **PLS** & **SVR** & **PLS** & **SVR** & **PLS** & **SVR** & **PLS** & **SVR** & **PLS** & **SVR** \\ \hline \multirow{2}{*}{None} & DM & 12 & 0.51 & 0.56 & 0.613 & 0.863 & 0.54 & 0.08 & 0.513 & 1.232 & 1.512 & 0.629 \\  & TPC & 12 & 0.58 & 0.84 & 113.289 & 75.956 & 0.00 & 0.77 & 482.832 & 499.78 & 0.942 & 0.910 \\  & FRAP & 12 & 0.65 & 0.89 & 42.962 & 26.14 & 0.00 & 0.17 & 128.45 & 180.553 & 0.422 & 0.300 \\  & CUPRAC & 12 & 0.88 & 0.84 & 120.36 & 148.391 & 0.00 & 0.37 & 519.759 & 618.733 & 0.833 & 0.700 \\ \hline SG1 & DM & 12 & 0.52 & 0.99 & 0.613 & 16.043 & 0.40 & 0.84 & 0.585 & 0.815 & 1.326 & 0.952 \\  & TPC & 12 & 0.71 & 0.99 & 93.607 & 3.431 & 0.00 & 0.80 & 471.339 & 533.946 & 0.964 & 0.851 \\  & FRAP & 12 & 0.59 & 0.99 & 46.706 & 9.691 & 0.00 & 0.68 & 65.721 & 0.712 & 0.824 & 0.766 \\  & CUPRAC & 12 & 0.82 & 0.99 & 116.077 & 0.006 & 0.00 & 0.79 & 491.5 & 671.999 & 0.881 & 0.644 \\ \hline SG2 & DM & 11 & 0.58 & 0.99 & 0.569 & 0.001 & 0.38 & 0.67 & 0.595 & 1.048 & 1.303 & 0.740 \\  & TPC & 11 & 0.83 & 0.99 & 72.582 & 0.204 & 0.00 & 0.79 & 474.649 & 483.197 & 0.958 & 0.941 \\  & FRAP & 10 & 0.66 & 0.99 & 42.248 & 0.078 & 0.20 & 0.96 & 46.323 & 95.737 & 1.170 & 0.566 \\  & CUPRAC & 4 & 0.87 & 0.99 & 126.191 & 0.372 & 0.10 & 0.99 & 404.672 & 477.769 & 1.070 & 0.906 \\ \hline SNV & DM & 3 & 0.49 & 0.99 & 0.631 & 0.102 & 0.32 & 0.88 & 0.624 & 0.865 & 1.243 & 1.132 \\  & TPC & 14 & 0.86 & 0.98 & 66.217 & 23.542 & 0.01 & 0.86 & 440.574 & 433.142 & 1.032 & 1.050 \\  & FRAP & 9 & 0.43 & 0.99 & 54.637 & 4.162 & 0.00 & 0.57 & 56.479 & 773.434 & 0.959 & 0.701 \\  & CUPRAC & 5 & 0.57 & 0.99 & 232.55 & 35.394 & 0.10 & 0.52 & 405.221 & 505.185 & 1.068 & 0.857 \\ \hline SNV Detrend 1 & DM & 12 & 0.5 & 0.98 & 0.623 & 0.126 & 0.30 & 0.87 & 0.633 & 0.742 & 1.225 & 1.045 \\  & TPC & 15 & 0.86 & 0.98 & 65.913 & 42.887 & 0.02 & 0.85 & 438.021 & 458.281 & 1.038 & 0.992 \\  & FRAP & 3 & 0.17 & 0.99 & 70.496 & 8.293 & 0.00 & 0.59 & 540.362 & 82.396 & 1.003 & 0.658 \\  & CUPRAC & 5 & 0.55 & 0.98 & 241.938 & 44.334 & 0.10 & 0.50 & 401.332 & 503.293 & 1.079 & 0.860 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical parameters were obtained from the application of the PLSR and SVR algorithms on the MIR spectra dataset using different pre-processing methods. Figure 4: Predicted vs. reference results for the application of MIR spectroscopy for the prediction of dry matter, TPC, FRAP, and CUPRAC contents. The use of the SVR method showed strong potential for the estimation of the four parameters of interest, as shown by the high R\({}^{2}\) values ranging from 0.86 to 0.99 for both calibration and validation. The best prediction for CUPRAC and FRAP was obtained by applying the second derivative Savitzky-Golay pre-processing method, with an R\({}^{2}\)pred of 0.99 and 0.96 and RMSEP of 478 and 96 mg/100 g, respectively. For TPC and DM, the best prediction results were obtained using SNV and MSC pre-processing, respectively.