## 3 Results and Discussion  
### iPLS and siPLS Models  
Different strategies were used to develop a suitable model for the evaluation of rice quality for industrial purposes. The raw NIR spectra of native rice flour were subjected to pre-processing procedures such as MSC plus second derivative and SNV plus second derivative, allowing for the removal of spectral noise and highlighting the differences among them (Figure 1). The irrelevant spectral variables were removed by applying the iPLS and siPLS algorithms. The subintervals characterized by the optimum number of LVs and lowest RMSECV values were selected (Figures 2 and 3). The iPLS algorithm allowed us to split the spectral region into 20 intervals of the same width; consequently, several PLS regression models were developed (Figure 2). The R and RMSECV for each sub-interval were established, and the region with the lowest RMSECV was selected (Table 1). The iPLS model for grain BD was developed after MSC plus second derivative spectral pre-processing and was characterized by R = 0.84, RMSECV = 102, and LV = 10. The RMSECV values were registered along several spectral intervals, being lowest at the region defined by 4784-4395 cm\({}^{-1}\) (Figure 2A,B). The correlation between the reference and predicted values is presented in a scatter plot in Figure 2C. Meanwhile, the siPLS models were constructed after the spectrum was split into 20 equal intervals, characterized by high R and the lowest RMSECV values (Table 1). The model for the BD parameter (R = 0.88; RMSECV = 180, and 10 LV) was developed as a combination of different intervals characterized by the lowest RMSECV values, for wavenumber ranges 8480-8180 cm\({}^{-1}\) and 5280-4640 cm\({}^{-1}\), obtained after SNV plus second derivative (Figure 3; Table 1). According to [PERSON] et al. (2007), BD at 5176 cm\({}^{-1}\) and 4363 cm\({}^{-1}\) was characterized by R = 0.98 and 0.65, respectively, being defined at 6548 cm\({}^{-1}\) and 4764 cm\({}^{-1}\)[17]. The absorption peaks at 10,792 cm\({}^{-1}\) and 6872 cm\({}^{-1}\) are related to the C-H second overtone and combinations of amylose. The main absorption bands at wavelengths 8340 cm\({}^{-1}\), 5714 cm\({}^{-1}\), 4776 cm\({}^{-1}\), and 4357 cm\({}^{-1}\) can be attributed to PV, which is similar to the results reported by [PERSON] et al. (1993) [18]. The C-H, O-H, and N-H vibrational bands found in the infrared spectra describe the combination of CH stretching and CH bending in amylose molecules [19]. The BD parameter represents the capacity of rice flour paste to reorganize, influenced by high temperature and by shear force, representing the strength of reconstituted rice paste and the damage degree of the particles through gelatinization [20]. Figure 1: Representation of NIR spectra after MSC processing. Each color represents the spectra for each samples rice. Figure 2: Evaluation of the RMSECV values related to each spectral region. The dotted line represents the RMSECV (10 LVs) for the full model. Italic numbers represent the optimal LV values for each interval model (**A**). Specific region of the NIR spectra for the iPLS model characterized by RMSECV = 102 (**B**). Correlation between measured and predicted BD values after MSC plus 2 nd derivative spectral pre-processing treatment and RMSECV evaluation in the spectral interval (**C**). Figure 3: Evaluation of the RMSECV values related to each spectral region. The dotted line represents the RMSECV (10 LVs). Italic numbers represent the optimal number of LVs in each interval model (**A**). For the siPLS model, specific regions in the NIR spectra present the lowest RMSECV values (**B**). Correlation between measured and predicted BD values after SNV plus 2 nd derivative spectral pre-processing treatment and RMSECV evaluation in several spectral intervals (R = 0.88; RMSECV = 180; 8480–8180 cm\({}^{-1}\); 5280–4640 cm\({}^{-1}\)) (**C**).