## 3 Results and Discussion
### iPLS and siPLS Models
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Parameter** & **Spectral Processing** & **R\({}_{\text{cal}}\)** & **RMSEC** & **RMSECV** & **R\({}_{\text{pped}}\)** & **RMSEP** & **Spectral Region (cm\({}^{-1}\))** \\ \hline BD & iPLS (MSC + 2 nd Derivative) & 0.84 & 238 & 102 & 0.77 & 284 & 4784–4395.5 \\  & siPLS (SNV + 2 nd Derivative) & 0.88 & 182 & 180 & 0.73 & 308 & 8480–8180, 5280–640 \\  & iPLS (SNV + 2 nd Derivative) & 0.57 & 273 & 270 & 0.47 & 358 & 6700–4395.5 \\  & siPLS (SNV + 2 nd Derivative) & 0.64 & 253 & 251 & 0.65 & 233 & 7840–7520; 4960–4320 \\ PV & iPLS (SNV + 2 nd Derivative) & 0.85 & 289 & 332 & 0.86 & 321 & 4784–4395.5 \\  & siPLS (SNV + 2 nd Derivative) & 0.90 & 259 & 275 & 0.90 & 321 & 5280–4320 \\ ST & iPLS (2 nd Derivative) & 0.85 & 299 & 332 & 0.81 & 325 & 4784–4395.5 \\  & siPLS (SNV + 2 nd Derivative) & 0.88 & 253 & 297 & 0.75 & 329 & 5280–4320 \\ TR & iPLS (2 nd Derivative) & 0.85 & 152 & 332 & 0.64 & 255 & 4784–4395.5 \\  & siPLS (SNV + 2 nd Derivative) & 0.84 & 141 & 154 & 0.88 & 119 & 5280–4320 \\ \hline \end{tabular} BD—breakdown: FV—final viscosity; PV—pasting viscosity; ST—sechuck; TR—roughly; MSC—multiplicative scatter correction; SNV—standard normal variate; RMSECV—root-mean-square error of cross-validation; RMSEC—root-mean-square error of calibration; RMSEC—root-mean-square error of prediction; LVs—latent variables.  
\end{table} 
Table 1: Statistical parameters determined for each pasting model after specific pre-processing steps. higher rice quality, representing an excellent target attribute in breeding programs [20]. The fat models at 7503-5447 cm\({}^{-1}\) are defined by the primary components C-H, N-H, and O-H. The pasting parameters and specific biochemical traits showed a negative correlation between amylose and PV, TR, and BD, while ST was characterized by a positive correlation. In terms of specific loading, the models showed strong spectral regions at 8200-7440 cm\({}^{-1}\), 6500-5700 cm\({}^{-1}\), 5095, and 4570 cm\({}^{-1}\). Several works have revealed that PV, BD, FV, and ST values are directly proportional to the protein present in rice flour [26]. The viscosity registered during heating or pasting processes is associated with the PV. This value is reached at the end of the heating phase when a significant number of swollen starch granules results in pasting. PV indicates the water-holding capacity of the starch or mixture and is commonly linked with other quality components [27]. Previous studies showed a positive correlation between amylose and ST [28] but a negative correlation with PV and BD [29]. Meanwhile, the rheological properties related to the rice varieties are dissimilar not only because of the different amylose and amylopectin contents but also due to the molecular structures and properties of starch molecules [30]. Studies carried out by [PERSON] et al. (2021) showed that the suggested technique had acceptable performance in predicting several parameters such as BD and ST, being characterized by a suitable accuracy for rice quality parameters (R\({}^{2}\geq\) 0.80 and R\({}^{2}\geq\) 0.71). The results of the present research demonstrated that NIRS is a suitable technique for predicting the quality characteristics of rice and its flour [31]. Based on the siPLS and iPLS models, similar spectral regions were selected, which proves that the biomolecular data present in those intervals is fundamental for the construction of the respective models, reinforcing the importance of fractional analysis of the spectrum. Meanwhile, the siPLS models showed unparalleled advantages by combining three intervals, achieving better models defined by a reduced total number of variables (elimination of spectral noise) and better predictive capacity.