## 3 Results and Discussion
### PCA Analysis  
PCA is an unsupervised method that reduces variable dimensions into smaller principal components without losing important data. This method is useful because it identifies sources of variance, clustering patterns, trends, and outliers [48; 49]. Accordingly, PCA was performed on the raw and pre-treatment data. This was carried out to reveal the effect of pre-treatment and to identify a product from the point of view of the absorbance spectrum. Figure 4a depicts a spread of data that does not allow for a clear visual separation of the groups, although only two principal components explain the variance of all the data. Moreover, the distance between the groups increased using the SNV pre-treatment, but the number of PCs increased to account for all the explained variance, as shown in Figure 4b and Table 1. Then, the initial pre-treatment led to a good separation between the groups with an increase in the PC [50]. Figure 4c reveals a clear separation of the groups as well as a slight increase in the variation explained by principal component 1 (PC-1) with SNV + pre-treatment with detrending. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Cumulative Explained Variance \%**} \\ \cline{2-6}
**Data Pre-Treatment** & **PC-1** & **PC-2** & **PC-3** & **PC-4** & **PC-5** \\ \hline Raw data & 95.07 & 99.85 & 99.96 & 99.98 & 99.99 \\ SNV & 50.81 & 87.85 & 96.66 & 98.31 & 99.47 \\ SNV + detrending & 58.77 & 90.52 & 97.43 & 98.93 & 99.63 \\ SNV + detrending + 1 st & 78.51 & 91.07 & 98.10 & 99.08 & 99.76 \\ derivative 19 smoothing points & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The behavior of cumulative explained variance as a function of pre-treatment type. Figure 4: _Cont._Figure 4: Pretreatment of spectra for each sample. (**a**) Raw data, (**b**) SNV, (**c**) SNV + detrending (2-order), and (**d**) SNV + detrending (2-order) + Savitzky–Golay first-derivative with 19 smoothing points. The best pre-treatment method for explaining the variances and differences among the flour products was SNV + detrending + 1 st derivative with 19 smoothing points, as indicated in Figure 4d. This figure shows the score plot of the five samples, in which soybean is situated on the right side of the central axis, while the other products are near the central axis and the left side. Meanwhile, PC-1 depicts changes at 4644 cm\({}^{-1}\) due to the increase in C-O bonds in the soybean spectrum, and this result is associated with protein content (see Figure 5) [51; 52]. To emphasize, variables with strong positive or negative loading plot values are more representative of the associated principal component; conversely, principal components that have little influence are those that have a loading plot value around 0 [53]. This would indicate that the main source of variation for PC-1 in Figure 4d is the C-O vibrational mode. Importantly, Figure 4d shows a difference between the elements of each cluster for H2-H5 as well as a slight variation for individual samples of H1. This could suggest some heterogeneity within the same cluster for H2-H5. From what has been discussed above, it can be concluded that soybeans are different from the rest. The advantage of creating a PCA classification model with different groups of samples separated with few PCs in a score plot is that one can explain the differences between samples through the variables of the different modes of vibration (wavenumber). In reference to the data obtained in Table 1, we can see that the cumulative explained variance changes according to the pre-treatment applied. For example, in the original data, the first three PCs explain almost 100% of the variation present in the samples, but in the score plot of Figure 4a, there is an overlap in the samples that is difficult to explain by means of any difference between them.