## 3 Results and Discussion
### Color of Cold-Pressed Oils
The high values of R2 adj (0.9141 and 0.8324) and R2 cv (0.8499 and 0.8629) in Models 1 and 2, as well as the PRESS/TSS ratios close to zero (0.1501 and 0.3071) indicate a high predictive capability of the models. In contrast, Model 3 shows slightly weaker statistical parameters (R2 adj = 0.6679, R2 cv = 0.3965, PRESS/TESS = 0.6035), suggesting a lower predictive capability for this model. For predicting pressing efficiency, further improvements can be made by increasing the number of variables or samples. The obtained VIF values (1.1905) suggest that multicollinearity among the variables is negligible. Relatively high F-values (54.20, 25.83, and 11.06) confirm a good fit of the data to the models. Similar results were reported by [PERSON] et al. [8]. Using artificial neural networks, they modeled the extraction oil yield of 20 sunflower hybrids based on the initial seed characteristics, including hull content. They obtained a high-quality model with the following validation parameters: R2 = 0.975; root mean square error = 3.436; mean bias error = \(-\)0.266; mean percentage error = 6.827; sum of squared error = 234.673; average absolute relative deviation = 43.399. For the obtained models, the dependence between experimental and predicted values was determined and is shown in Figure 2.  
Ideally, the dependence should coincide with the equation y = x with a determination coefficient of 1, while residuals (differences between experimental and predicted values) should evenly scatter around the y = 0 axis [65]. The dependencies between experimental and predicted values for the models are very close to theoretical values; the slopes are equal to one, while the intercepts are near zero. The residuals are evenly distributed around the y = 0 axis. \begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Model 1** & **Model 2** & **Model 3** \\ \hline R & 0.9650 & 0.9305 & 0.8569 \\ R2 adj & 0.9313 & 0.8659 & 0.7344 \\ R2 adj & 0.9141 & 0.8324 & 0.6679 \\ R2 cv & 0.8499 & 0.6929 & 0.3965 \\ Fv & 54.20 & 25.83 & 11.06 \\ P & 0.0000 & 0.0003 & 0.0050 \\ SD & 0.2074 & 3.7924 & 6.9364 \\ CV\% & 3.2200 & 16.2000 & 10.5900 \\ PRESS & 0.7514 & 263.4902 & 874.4389 \\ TSS & 5.0060 & 858.1150 & 1448.9430 \\ PRESS/TSS & 0.1501 & 0.3071 & 0.6035 \\ VIF (X\({}_{1}\)) & 1.1905 & 1.1905 & 1.1905 \\ VIF (X\({}_{2}\)) & 1.1905 & 1.1905 & 1.1905 \\ \hline \end{tabular}
\end{table} 
Table 4: Validation parameters of the obtained MLR models. Figure 2: Validation of the obtained MLR models: Dependence between experimental and model-predicted values (**a**,**c**,**e**) and residual distribution around the y = 0 axis (**b**,**d**,**f**). In (**a**,**c**,**e**), the experimental and model-predicted values are represented by points, their dependence is shown with a solid line, while the dashed lines indicate the interval within which the values are expected to fall with 95% confidence. The findings of this study also have significant industrial implications for large-scale mechanical oil extraction processes but with adjustments of the models to the production conditions. The optimization of hull and impurity content in sunflower seeds can directly impact the efficiency of cold pressing, which is a critical factor for oil manufacturers seeking to maximize yield while minimizing waste. The hull and impurity content also affects the chemical quality and color of the oil [31], which is crucial for consumer acceptance. The higher nutritional quality and consumer preference for cold-pressed oils allow for premium market pricing, which economically justifies research aimed at optimizing the hull and impurity content in sunflower seeds.