Artificial Intelligence in Food Engineering for Food Safety, Quality Assurance, and Human Health

Vollständiger Abstract

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Artificial intelligence (AI) is increasingly reshaping food engineering by providing data-driven tools for safeguarding food safety, ensuring quality assurance, and improving human health outcomes. This review synthesizes current evidence on the application of machine learning, deep learning, computer vision, and the Internet of Things (IoT) across the food production continuum, from raw material inspection to consumer-facing nutrition guidance. We examine how convolutional neural networks, hyperspectral imaging, and sensor-fusion approaches enable rapid, non-destructive detection of contaminants and pathogens, and how predictive models support shelf-life estimation and quality grading. We further discuss AI-enabled traceability systems, including blockchain-integrated supply chains, and the growing role of AI in personalized nutrition and foodborne disease prevention. Finally, we identify key technical, regulatory, and economic barriers that currently limit large-scale adoption, and we outline priority research directions, including explainable AI, federated learning, and multi-omics data integration. Collectively, the evidence indicates that AI technologies offer substantial, measurable benefits for food safety and human health, but their full potential depends on resolving data standardization, model transparency, and cost-accessibility challenges, particularly for small and medium-sized food enterprises.