## 4 Results
### Analysis of AI-Generated Image Attractiveness Compared to Photographs
In contrast, the least preferred AI-generated images were those of beef tartare (Figure 17), dry salami (Figure 13), prosciutto (Figure 6), and steak (Figure 12). These same images had also been rated as less professionally adequate by food technology experts. The main reason for their low evaluations was the unnatural coloration in the AI-generated versions, which discouraged many respondents. This clearly indicates that color accuracy and realism are crucial for the perceived quality of AI-generated images, particularly for meat products, where hue and saturation significantly impact impressions of freshness and quality. To assess whether the observed preferences for AI-generated images were statistically significant, we conducted chi-square tests for each product. The results confirm that for 14 out of 15 items, the preference distributions were statistically significant at the \(p<0.05\) level. For example, preferences for AI-generated images were particularly strong for burger and  
Figure 6:3 Attractiveness comparison between AI-generated images and photographs (n = 366). roasted chicken, as well as Carnional sausage. The only product for which the difference was not statistically significant was steak (\(p\) = 0.12). This suggests that, while average preference leaned toward AI imagery, the strength of this preference varied by product. One of the most notable findings of this study is that the use of reference photographs during the AI image generation process significantly enhanced the visual quality and appeal of the resulting images. Specifically, the AI-generated images of the burger and roasted chicken--both created with the help of photographic samples--achieved the highest preference ratings among all image pairs, each selected by 92.9% of respondents. This strongly suggests that combining photographic inputs with textual prompts enables GenAI systems to better replicate real-world visual features such as texture, structure, and color accuracy. This 'hybrid' creation process offers a practical solution for food marketers and content creators, balancing the visual appeal of AI-generated images with the credibility and recognizability of real products. To facilitate implementation, we propose a structured five-step workflow:  
1. Capture or identify a reference photograph that illustrates the desired visual attributes;
2. Describe key features and stylistic targets;
3. Prompt the AI using both the image and detailed textual input;
4. Validate the output against a visual checklist (e.g., color fidelity, shape realism, product recognizability);
5. Refine results through human-in-the-loop feedback or light editing. This reference-supported strategy offers a path forward for professionals seeking efficiency without sacrificing accuracy, and should be considered a central contribution to this study. Overall, it can be concluded that Adobe Firefly 3 achieves highly variable outcomes. For meat products where details such as color and cross-section composition are crucial, the results are less satisfactory. By contrast, items that are more visually recognizable and allow greater aesthetic flexibility, such as burgers or spaghetti, achieve much higher preference rates.