AlertGen: Advanced Allergen Detection for Labelled and Non-labelled Foods

Abstract

In this study, artificial intelligence–driven analysis and optical character recognition (OCR) technology were utilized to enhance food safety by detecting allergens in both labelled and unlabelled foods through the development of the AlertGen mobile application. Individuals with food allergies play a crucial role in identifying the challenges associated with allergen detection, particularly in recognizing hidden allergens, interpreting unclear or misleading ingredient labels, finding safe food alternatives, and addressing the absence of allergen information in non-labelled foods. Their experiences and insights contributed significantly to understanding real-world difficulties in food allergen awareness and management. The researchers based the project design on a descriptive and developmental research method to better understand the existing challenges in allergen detection and to support the systematic development of the proposed application. For the design and development of the system, the SCRUM framework was adopted to ensure iterative improvement and alignment with user requirements. The study was conducted in the province of Pangasinan, and the participants were composed of individuals with food allergies selected through purposive sampling. Their feedback, together with data gathered from related studies and system usability evaluation using the System Usability Scale (SUS), proved essential in the successful design, development, and usability assessment of the AlertGen application.