# Infrared Spectroscopy for the Analysis of Bioactive Analytes in Wheat: A Proof-of-Concept Study
## 3 Results
### Mid-Infrared Spectra
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Parameter** & \
\begin{tabular}{c} **Spectral** \\ **Pre-Processing** \\ \end{tabular} & **Components** & \(\mathbf{R^{2}_{cv}}\) & **RMSECV** & **RPD** & \(\mathbf{R^{2}_{test}}\) & **RMSEP** & **Bias** & **Slope** & **Intercept** \\ \hline Moisture & 1d21 & 4 & 0.86 & 0.52 & 2.65 & 0.83 & 0.65 & 0.08 & 1.45 & \(-\)5.10 \\ Protein & SNV + 2d15 & 6 & 0.92 & 0.55 & 3.65 & 0.93 & 0.62 & \(-\)0.10 & 0.89 & 1.40 \\ FRAP & SNV + 1d15 & 6 & 0.88 & 5.2 & 2.93 & 0.83 & 7.0 & 1.56 & 1.20 & \(-\)4.21 \\ TPC & SNV & 5 & 0.51 & 7.8 & 1.45 & 0.73 & 5.6 & \(-\)1.53 & 0.76 & 33.8 \\ TMA & None & 1 & \(-\)0.04 & 2.1 & 0.99 & 0.02 & 1.6 & 0.54 & \(-\)1.25 & 5.55 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & \
\begin{tabular}{c} **Spectral** \\ **Pre-Processing** \\ \end{tabular} & **Components** & \(\mathbf{R^{2}_{cv}}\) & **RMSECV** & **RPD** & \(\mathbf{R^{2}_{test}}\) & **RMSEP** & **Bias** & **Slope** & **Intercept** \\ \hline Moisture & 1d21 & 4 & 0.86 & 0.52 & 2.65 & 0.83 & 0.65 & 0.08 & 1.45 & \(-\)5.10 \\ Protein & SNV + 2d15 & 6 & 0.92 & 0.55 & 3.65 & 0.93 & 0.62 & \(-\)0.10 & 0.89 & 1.40 \\ FRAP & SNV + 1d15 & 6 & 0.88 & 5.2 & 2.93 & 0.83 & 7.0 & 1.56 & 1.20 & \(-\)4.21 \\ TPC & SNV & 5 & 0.51 & 7.8 & 1.45 & 0.73 & 5.6 & \(-\)1.53 & 0.76 & 33.8 \\ TMA & None & 1 & \(-\)0.04 & 2.1 & 0.99 & 0.02 & 1.6 & 0.54 & \(-\)1.25 & 5.55 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & \
\begin{tabular}{c} **Spectral** \\ **Pre-Processing** \\ \end{tabular} & **Components** & \(\mathbf{R^{2}_{cv}}\) & **RMSECV** & **RPD** & \(\mathbf{R^{2}_{test}}\) & **RMSEP** & **Bias** & **Slope** & **Intercept** \\ \hline Moisture & 1d21 & 4 & 0.86 & 0.52 & 2.65 & 0.83 & 0.65 & 0.08 & 1.45 & \(-\)5.10 \\ Protein & SNV + 2d15 & 6 & 0.92 & 0.55 & 3.65 & 0.93 & 0.62 & \(-\)0.10 & 0.89 & 1.40 \\ FRAP & SNV + 1d15 & 6 & 0.88 & 5.2 & 2.93 & 0.83 & 7.0 & 1.56 & 1.20 & \(-\)4.21 \\ TPC & SNV & 5 & 0.51 & 7.8 & 1.45 & 0.73 & 5.6 & \(-\)1.53 & 0.76 & 33.8 \\ TMA & None & 1 & \(-\)0.04 & 2.1 & 0.99 & 0.02 & 1.6 & 0.54 & \(-\)1.25 & 5.55 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** &
\begin{tabular}{c} **Spectral** \\ **Pre-Processing** \\ \end{tabular} & **Components** & \(\mathbf{R^{2}_{cv}}\) & **RMSECV** & **RPD** & \(\mathbf{R^{2}_{test}}\) & **RMSEP** & **Bias** & **Slope** & **Intercept** \\ \hline Moisture & 1d21 & 4 & 0.86 & 0.52 & 2.65 & 0.83 & 0.65 & 0.08 & 1.45 & \(-\)5.10 \\ Protein & SNV + 2d15 & 6 & 0.92 & 0.55 & 3.65 & 0.93 & 0.62 & \(-\)0.10 & 0.89 & 1.40 \\ FRAP & SNV + 1d15 & 6 & 0.88 & 5.2 & 2.93 & 0.83 & 7.0 & 1.56 & 1.20 & \(-\)4.21 \\ TPC & SNV & 5 & 0.51 & 7.8 & 1.45 & 0.73 & 5.6 & \(-\)1.53 & 0.76 & 33.8 \\ TMA & None & 1 & \(-\)0.04 & 2.1 & 0.99 & 0.02 & 1.6 & 0.54 & \(-\)1.25 & 5.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimum PLSR models found for the prediction of the specified analytes using mid-infrared spectroscopy (MIRS), using separate calibration and dependent test sets. Again, the wheat samples included in this study displayed only low or high FRAP values (with no samples of intermediate values), while the TP contents of the samples were distributed roughly equally throughout the calibration range. Consequently, the prediction accuracy of the FRAP model (Figure 11) would likely be improved through inclusion of samples of intermediate values. The loadings for this model (Figure 12) were somewhat similar to those of the protein content model, but with the strongest influences from the CH stretch (\(\sim\)3000 cm\({}^{-1}\)) and cellulose bond (\(\sim\)1000 cm\({}^{-1}\)) regions, with moderate influence from the carbonyl (\(\sim\)1700 cm\({}^{-1}\)) and amide regions (1600-1400 cm\({}^{-1}\)). Figure 10: Loadings plot for the MIRS prediction of protein content in wheat flour. Note that “comp” stands for the number of principal components in the model.