# Infrared Spectroscopy for the Analysis of Bioactive Analytes in Wheat: A Proof-of-Concept Study
## 4 Discussion  
Overall, the NIRS results showed promise for the rapid, non-invasive prediction of most of the analytes investigated in the wheat samples, including proximate analytes (protein and moisture) and bioactive components (FRAP and TP). The best-performing  
Figure 19: Results of moving window optimisation for the prediction of FRAP using the MIR spectra. Note that the figure shows RMSECV values. Figure 20: Results of moving window optimisation for the prediction of TPC using the MIR spectra. Note that the figure shows RMSECV values. NIRS model was found to be that for protein content (R\({}^{2}\)\({}_{\text{test}}\) = 0.991; RMSEP = 0.22%). This model showed ideal statistics, with minimal bias, a slope close to 1, and an intercept close to 0. Furthermore, the RPD of the test set (RPD\({}_{\text{test}}\)) was 10.23, well above the threshold indicating an excellent prediction model (RPD of 3) [34]. The model developed here was more accurate than NIRS models previously reported for Turkish wheat (R\({}^{2}\)\({}_{\text{test}}\) = 0.97, RMSEP = 0.38%; dependent test set) [35] and triticale, a hybrid cross of wheat and rye (R\({}^{2}\)\({}_{\text{test}}\) = 0.96, RMSEP = 0.32%; test set from a different growing season) [36]. However, it was not quite as accurate as the model reported by [PERSON] et al. [13] (R\({}^{2}\)\({}_{\text{test}}\) = 0.999, RMSEP = 0.05%; dependent test set), likely due to the smaller calibration sample size used in this study. Nevertheless, the protein model's accuracy would appear to place it among the best performing models reported in the literature. Although the model linearity was somewhat lower for the prediction of TPC (R\({}^{2}\)\({}_{\text{test}}\) = 0.83), the RMSEP for this analyte was exceptionally low, at just 3.9 mg GAE/100 g. This corresponds to an RPD\({}_{\text{test}}\) of 2.49, corresponding to a good prediction accuracy [34]. The only previous study found using NIRS for the prediction of TPC in wheat flour was performed by [PERSON] et al. [23], who reported a higher R\({}^{2}\) value (R\({}^{2}\)\({}_{\text{val}}\) = 0.90), but lower precision (RMSEV = 7.1 mg GAE/100 g) on a dependent test set. No previous studies were found using infrared spectroscopy for the prediction of antioxidant capacity in wheat flour, although several studies have attempted this in other crops such as maize [37], rice [20], and gluten-free grains [21]. Although the FRAP prediction model developed here showed high linearity and a relatively low RMSEP, it should be cautioned that there were relatively few points with higher FRAP values included in the calibration and test sets. Consequently, this model had a slope that deviated from 1, indicating relative under-prediction of the highest-content sample. Nevertheless, the similar wavelength loadings between the TPC and FRAP models support the observation that this model was looking at the \"correct\" wavelengths to measure compounds with antioxidant activity. While it is likely that analysis of a larger number of wheat samples containing a broader array of FRAP values would be required to create a robust NIRS model for the prediction of this analyte, the current model appeared suitable for the rough estimation (not exact quantification) of FRAP values. Its RPD\({}_{\text{cal}}\) was 2.95, although this fell to an RPD\({}_{\text{test}}\) of 1.57 for the dependent test set, indicating that it is most suitable for screening purposes only [34]. However, with a larger sample size covering the full range of FRAP values found in wheat, it is possible that these statistics could be improved to provide more accurate prediction of antioxidant capacity. Neither NIRS nor MIRS were able to predict the total monomeric anthocyanin content. This result was not unexpected, due to the very low concentration of this compound (mean content of just 2.5 mg cyd-3-glu/100 g in the calibration set).