Food Additive Lens: An on-device AI application for real-time science-based consumer education on food additives using retrieval-augmented generation

Abstract

Consumer concerns about food additives have intensified amid widespread misinformation, with the 2024 IFIC survey revealing that 35% of consumers actively avoid artificial ingredients despite authoritative safety data existing in FDA and USDA databases. This paper presents Food Additive Lens (FAL), an iOS application that bridges the gap between scientific knowledge and consumer understanding through on-device artificial intelligence. FAL implements a three-agent architecture comprising: (1) a food category classifier achieving 87.2% top-3 accuracy across 257 categories, (2) a hybrid additive identifier combining database lookup with AI extraction (F1-score: 0.757), and (3) an explanation generator producing contextualized, consumer-friendly descriptions. The system deploys Meta's Llama 3.2 3B model quantized to 1.8GB through 4-bit compression, achieving 13-30 tokens/second generation speed while operating entirely offline. Integration of FDA's Substances Added to Food Inventory (3,971 substances) and USDA's Global Branded Food Products Database enables comprehensive coverage with direct Code of Federal Regulations links for professional users. The Retrieval-Augmented Generation workflow grounds AI responses in authoritative sources, reducing hallucination while maintaining accessibility. Performance evaluation on iPhone 14 and MacBook Air M1 demonstrated stable memory usage (peak 2.36GB) with complete offline functionality, ensuring user privacy. The application transforms complex ingredient lists into accessible information through camera-based OCR scanning, progressive disclosure interfaces, and context-aware explanations tailored to specific food products. This work demonstrates the feasibility of deploying sophisticated AI for science communication on consumer devices, offering a scalable model for combating food-related misinformation while preserving privacy and accessibility.

Supplementary files

Article information

Article type
Paper
Submitted
03 Oct 2025
Accepted
06 Feb 2026
First published
18 Feb 2026
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2025, Accepted Manuscript

Food Additive Lens: An on-device AI application for real-time science-based consumer education on food additives using retrieval-augmented generation

Y. FENG, Y. Wang, X. Wang, B. Zhao, J. Bi, S. Han and Y. Luo, Digital Discovery, 2025, Accepted Manuscript , DOI: 10.1039/D5DD00444F

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