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Celiac disease and gluten sensitivity affect a significant portion of the population and require adherence to a gluten-free diet. Dining in social settings, such as family events, workplace gatherings, or restaurants, makes it difficult to ensure that certain foods are gluten-free. Despite the availability of portable gluten testing devices, these instruments have high costs, disposable capsules, depend on user preparation and technique, and cannot analyze an entire meal or detect gluten levels below the legal thresholds, potentially leading to inaccurate results. In this study, we propose RGB (Recognition of Gluten in Bread), a novel deep learning-based method for automatically detecting gluten in bread images. RGB is a decision-support tool to help individuals with celiac disease make informed dietary choices. To develop this method, we curated and annotated three unique datasets of bread images collected from Pinterest, Instagram, and a custom dataset containing information about flour types. Fine-tuning pre-trained convolutional neural networks (CNNs) on the Pinterest dataset, our best-performing model, ResNet50V2, achieved 77% accuracy and recall. Transfer learning was subsequently applied to adapt the model to the Instagram dataset, resulting in 78% accuracy and 77% recall. Finally, further fine-tuning the model on a significantly different dataset, the custom bread dataset, significantly improved the performance, achieving an accuracy of 86%, precision of 87%, recall of 86%, and F1-score of 86%. Our analysis further revealed that the model performed better on gluten-free flours, achieving higher accuracy scores for these types. This study demonstrates the feasibility of image-based gluten detection in bread and highlights its potential to provide a cost-effective non-invasive alternative to traditional testing methods by allowing individuals with celiac disease to receive immediate feedback on potential gluten content in their meals through simple food photography. Full article