## 3 Results and Discussion
### NIRS Calibration and Validation of PLS Models
#### 3.1.1 Taro
The spectra were composed mainly of groups of peaks located, respectively, at 1200, 1465, 2050, 2140, 2314, and 2350 nm. These peaks are associated with C-H, O-H, and N-H stretching-bending vibrational modes, mainly related to starch and proteins [17]. Figure 2: NIR spectra of the seven taro accessions, with 126 corm (**A**) and 120 shoot (**B**) samples, and external set validation with 21 samples for corms (**C**) and 18 shoot samples (**D**). Wavelengths from 1100 to 2498.2 nm of the NIR range. The comparison between the calibration models created from the chemical analysis and the NIR spectra allowed the implementation of PLS equations and the prediction of the ten quality constituents with results presented in Table 2.  
The St-corn model showed the highest SEC, SECV, and SEP values of 0.982, 1.048, and 0.767, respectively. Meanwhile, the r\({}^{2}\)cv (0.964), r\({}^{2}_{\text{pred}}\) (0.977), and RPD (5.22) values indicate good predictive potential for the starch model in corn flours. The \(\Delta^{13}\)C-corn model had an excellent prediction application, according to the r\({}^{2}_{\text{pred}}\) (0.977) and RPD (2.83) values. However, the \(\Delta^{13}\)C-shoot model showed a rough prediction with RPD (1.94) and r\({}^{2}_{\text{cv}}\) (0.740), still with r\({}^{2}_{\text{pred}}\) (0.910) value showing a satisfactory prediction [17; 42]. Hence, we could identify the equations with the poorest performance, showing the lowest r\({}^{2}_{\text{cv}}\), r\({}^{2}_{\text{pred}}\), and RPD values, with 0.564, 0.717, and 1.53 for \(\delta^{15}\)N-corn, and with 0.574, 0.873, and 1.53 for \(\delta^{13}\)C-shoot, respectively. Even with the r\({}^{2}_{\text{pred}}\) slightly higher than 0.71, the RPD (1.53) values indicate that both \(\delta^{15}\)N-corn and \(\delta^{13}\)C-shoot constituents could not be satisfactorily predicted [16]. The CaOx acidity models had low and quite similar SEC and SECV values, with good RPD values in both corn and shoot models, with 0.027, 0.028, 3.58, and with 0.038, 0.040, 2.70, respectively. While the CaOx-shoot model showed the lower r\({}^{2}_{\text{pred}}\) (0.498), and even an acceptable  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Calibration Set**} & \multicolumn{6}{c}{**Validation**} \\  & & & & & & & & & & & **Independent Set** \\ \hline
**Taro Models** & **n** & **Outliers** & **Mean** & **SD** & **PLS Terms** & **SEC** & **SECV** & **r\({}^{2}_{\text{ev}}\)** & **RPD** & **n** & **r\({}^{2}_{\text{pred}}\)** & **SEP** \\ \hline  & & & & \multicolumn{6}{c}{Moisture, g/100g DW} & \\ \hline  & & & & & & & & & & & &  
RPD value (2.70), this validation set was not robust enough to have a good determination coefficient for acridity prediction. These \(\delta^{15}\)N-corn, \(\delta^{13}\)C-shoot and CaOx-shoot prediction constituents should be only used as an informative tool in germplasm screening, and not for quantitation. The remaining models--moisture, Pt, Fb and M--showed good quantitative predictions, with \(r^{2}_{\text{cv}}\) and \(r^{2}_{\text{pred}}\) higher than 0.863. They also showed high RPD values, ranging between 2.14 and 11.56 for the corn moisture and protein models, and between 2.21 and 5.97 for the shoot moisture and protein models. The simultaneous prediction of the St, Pt, Fb, and M chemical constituents on a single taro sample, could allow a fast estimation of the variety chemotype and quality with acceptable accuracy [17].