# Infrared Spectroscopy for the Analysis of Bioactive Analytes in Wheat: A Proof-of-Concept Study
## 3 Results
### Near-Infrared Spectra

Table 2: Optimum PLSR models found for the prediction of the specified analytes using near-infrared spectroscopy (NIRRS), using separate calibration and dependent test sets. Figure 1: The raw NIR spectra (**A**) and SNV-processed spectra (**B**) of the wheat flour samples. The model for moisture content performed acceptably, with acceptable linearity, very low bias, and a slope close to 1. However, the ratio of performance to deviation (RPD) was considerably lower than that for protein, indicating relatively higher error associated with the moisture model. This was also evidenced in the higher RMSE for the prediction of moisture in the samples from the test set. Most likely, this is due to small changes in the moisture content of the wheat samples between when the moisture analysis was conducted, and when the samples were subjected to NIR and MIR analysis. The predominant loading for the moisture content model was at 1900, corresponding to the first overtone of the OH bond [29], with more minor contributions at 1949 and 2156 nm. In contrast to the results for protein and moisture, the model for TMA content showed no predictive power, likely due to the very low concentrations of this analyte. The NIRS model for FRAP content also performed well, although it should be cautioned that there were no samples with intermediate FRAP values (Figure 4). For example, in the test set, only one sample had a high FRAP value, which was underpredicted by the model. When the outlier point was removed, the R\({}^{2}\)test fell significantly to 0.46, although  
Figure 3: Loadings plot for the NIRS prediction of protein content in wheat flour. Note that “comp” stands for the number of principal components in the model. Figure 2: Actual vs. NIRS-predicted protein values for the calibration (**A**) and test (**B**) samples, using 5 components. the RMSEP improved to 2.9 mg TE/100 g. Nevertheless, most of the other sample points fell very close to the regression line (Figure 4B). The main loadings for the FRAP model (Figure 5) showed a negative peak at 1934 nm (the N-H asymmetric stretch and amide II region) and a positive peak at 2102 nm (potentially resulting from the combination bands of ROH groups). Thus, it appears that the antioxidant capacities of the samples were negatively correlated with their protein contents and positively associated with their levels of phenolic compounds (containing ROH groups). The TP content of the samples was more evenly distributed compared to the FRAP (for both the calibration and test sets), containing samples of a range of TP contents throughout the calibration range (Figure 6). Although the linearity was lower compared to the FRAP model (R2 test = 0.73), the RMSEP was better, at just 3.9 mg GAE/100 g. This was evidenced in that most of the test set samples were very close to their true values across the entire calibration range (Figure 6B). The loadings plot for the TP model (Figure 7) appeared visually different to the FRAP model due to the use of the first derivative of the spectra in the former. However, the main influential wavelengths were quite similar, being centred at approximately 1930 nm and 2110 nm. Again, these are likely to correspond to amide and R-OH bonds. Figure 4: Actual vs. NIRS-predicted ferric reducing antioxidant potential (FRAP) values for the calibration (**A**) and test (**B**) samples, using 6 components. Figure 5: Loadings plot for the NIRS prediction of ferric reducing antioxidant potential (FRAP) in wheat flour. Note that “comp” stands for the number of principal components in the model.