## 3 Results
### Sensory Attributes  
After verifying the equality of error variances [Smell preference: F(5,354) = 3.941, \(p\) = 0.002; Colour preference: F(5,354) = 1.215, \(p\) = 0.302; Texture preference: F(5,354) = 1.771, \(p\) = 0.118; Fatitness preference: F(5,354) = 4.606, \(p\) = 0.001; Taste preference: F(5,354) = 6.912, \(p\) = 0.001; Overall characteristics: F(5,354) = 2.212, \(p\) = 0.053] and the normality of residuals [Smell preference: W(360) = 0.980, \(p\) < 0.001; Colour preference: W(360) = 0.988, \(p\) = 0.004; Texture preference: W(360) = 0.983, \(p\) < 0.001; Fatitness preference: W(360) = 0.973, \(p\) < 0.001; Taste preference: W(360) = 0.977, \(p\) < 0.001; Overall characteristics: W(360) = 0.914, \(p\) < 0.001], a multivariate analysis of variance (MANOVA) was performed. It is important to note that for fatiness preference, taste preference, and overall characteristics, the assumptions for the MANOVA were not fully met. However, due to the robustness of the MANOVA under such conditions and in the interest of maintaining the completeness of the analysis, the test was conducted. In the case of untrained panellists and preference testing, this approach is considered acceptable. The unexplained variance associated with meat type (and thus fat content) was found to be significant [Wilks' lambda = 0.617, F(6,349) = 36.097, \(p\) < 0.001], as was the variance for treatment [Wilks' lambda = 0.579, F(12,698) = 18.268, \(p\) < 0.001]. However, values above 0.5 were not particularly indicative of strong effects. The interaction between these two factors [Wilks' lambda = 0.608, F(12,698) = 16.404, \(p\) < 0.001] yielded results similar to those of the individual factors. This suggests that neither the fat content of the meat nor the type of treatment significantly influenced the panel's preferences for the samples. This can be explained by the fact that some consumers preferred drier meat, while others favoured fattier meat. Similarly, some consumers preferred darker-coloured meat, while others preferred lighter-coloured meat. The results of sensory analysis can be seen in Figures 12-17 and Table 4.  
Figure 12: Smell preference [-] results of sample groups made from different meat parts with different fat contents (tenderloin—dry; collar—fatty) with different treatments (control without crust, crust creating before cooking, crust creating after cooking). Figure 14: Texture preference [-] results of sample groups made from different meat parts with different fat contents (tenderloin—dry; collar—fatty) with different treatments (control without crust, crust creating before cooking, crust creating after cooking). Figure 13: Colour preference [-] results of sample groups made from different meat parts with different fat contents (tenderloin—dry; collar—fatty) with different treatments (control without crust, crust creating before cooking, crust creating after cooking). Figure 16: Taste preference [-] results of sample groups made from different meat parts with different fat contents (tenderloin—dry; collar—fatty) with different treatments (control without crust, crust creating before cooking, crust creating after cooking). Figure 15: Fattiness preference [-] results of sample groups made from different meat parts with different fat contents (tenderloin—dry; collar—fatty) with different treatments (control without crust, crust creating before cooking, crust creating after cooking). Unfortunately, it was not possible to isolate distinct consumer profiles using k-means cluster analysis, as consumer preferences were not consistent across different ments or treatments. Interestingly, although the smell of the meat theoretically should not have been influenced by the experimental factors, given that no spices were added and all the meat was allowed to cool to the same temperature before tasting, preconceptions about the different colours led most consumers to expect differences in the smell of the samples.