## 3 Results and Discussion
### Color of Cold-Pressed Oils  
Color is one of the key factors influencing food acceptability. Moreover, it can be used to assess the composition and chemical changes in food products, serving as an important quality indicator [63]. Typically, color intensity is quantified using units proposed by the Commission Internationale de l'Eclairage (CIE). Colors are defined by their tristimulus values (X, Y, and Z), which can be used to calculate corresponding derived values. In this study, the color of the examined oil samples was significantly influenced by the hull and impurity content, as oil extraction during pressing incorporated some of these substances. The highest brightness (Y) values (4.01 \(\pm\) 0.09% and 4.02 \(\pm\) 0.16%) were observed in samples with the lowest hull and impurity content (sample 10: 0% hulls, 0% impurities; sample 9: 10% hulls, 0% impurities, respectively), as shown in Table 3. Significantly lower values were recorded for other samples, with the lowest brightness confirmed in the sample with the highest total hull and impurity content (sample 11). The dominant wavelength (\(\lambda\)) ranged within a narrow interval of 566 \(\pm\) 1 nm to 571 \(\pm\) 1 nm. The lowest color purity (P) was observed in the sample without hulls and impurities (sample 10), measuring 12.60 \(\pm\) 1.97%, while the highest value (24.73 \(\pm\) 0.20%) was found in sample 5 (20% hull, 5% impurities). The color parameters of the control sample fell within the examined range. Similar brightness (Y) values were reported for olive oils, ranging between 3.19 \(\pm\) 0.06% and 4.14 \(\pm\) 0.00%. However, the dominant wavelength (\(\lambda\)) of olive oil was significantly lower compared to sunflower oil, with values between 22.47 \(\pm\) 0.00 nm and 24.13 \(\pm\) 0.00 nm [64]. In most edible vegetable oils, carotenoids are the primary color-bearing compounds. These pigments exhibit maximum absorption at wavelengths between 440 and 460 nm, making transparency measurements within this range a standard practice. The highest transparency value among the examined samples was observed in the sample without hulls and impurities (sample 10), measuring 71.96 \(\pm\) 0.14%. This value is close to the transparency level of refined oils, which should exceed 80% [42], indicating an exceptionally light-colored oil. As expected, the lowest transparency (32.00 \(\pm\) 0.05%) was recorded in the sample with the highest hull and impurity content (sample 11). The transparency of the control sample was measured at 35.11 \(\pm\) 0.26%. Prediction of Cake Composition and Pressing Efficiency (Oil Yield) Using Multiple Linear Regression (MLR)  
Using multiple linear regression (MLR), a mathematical method that accounts for multiple independent variables influencing one dependent variable. Models were developed to predict moisture (Mc) and content of oil (Oc) remaining in the cake and pressing efficiency--oil yield (Ep) based on the hull (h) and impurity (i) content in the seeds. Experimentaldata and calculated data (Table 3) were used to form MLR models for predicting selected dependent variables (Equations (5)-(7)). \[\textbf{Model 1}\ \text{Mc}=0.087\ (\pm 0.010)\ \times\ \text{h}(\%)+0.039\ (\pm 0.020)\ \times\ \text{i}(\%)+5.382\ (\pm 0.127) \tag{5}\] \[\textbf{Model 2}\ \text{Oc}=-1.073\ (\pm 0.185)\ \times\ \text{h}(\%)-0.581\ (\pm 0.370)\ \times\ \text{i}(\%)+37.050\ (\pm 2.327)\] (6) \[\textbf{Model 3}\ \text{Ep}=1.361\ (\pm 0.338)\ \times\ \text{h}(\%)+0.422\ (\pm 0.677)\ \times\ \text{i}(\%)+49.779\ (\pm 4.257) \tag{7}\]  
Validation parameters of the obtained MLR models are presented in Table 4.  
Based on the values of R and R2, the proposed MLR models describe strong to very strong correlations between variables, and the prediction of moisture and oil content in the cake, as well as pressing yield, can be achieved with acceptable prediction error.