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Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Abstract Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, pre…

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Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions

Artificial Intelligence (AI) is emerging as a key driver at the intersection of nutrition and food systems, offering scalable solutions for precision health, smart manufacturing, and sustainable development. This study aims to present a comprehensive review of AI-driven innovations that enable precision nutrition through real-time dietary recommendations, meal planning informed by individual biological markers ( e.g ., blood glucose or cholesterol levels), and adaptive feedback systems. It further examines the i…

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Machine Learning for Quality Control in the Food Industry: A Review

The increasing complexity of modern food production demands advanced solutions for quality control (QC), safety monitoring, and process optimization. This review systematically explores recent advancements in machine learning (ML) for QC across six domains: Food Quality Applications; Defect Detection and Visual Inspection Systems; Ingredient Optimization and Nutritional Assessment; Packaging-Sensors and Predictive QC; Supply Chain-Traceability and Transparency and Food Industry Efficiency; and Industry 4.0 Model…

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Applications of artificial intelligence (AI) in managing food quality and ensuring global food security

The food industry uses artificial intelligence (AI) to enhance food quality and security while proposing significant capital savings and resource optimization. Additionally, understanding machine learning (ML) techniques is essential for their effectiveness. Therefore, the gap lies in examining how industrial automation plays a crucial role in successfully implementing this new technology. To address this gap, this review explores AI’s potential to significantly enhance food safety by creating a more transparent…

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Explainable artificial intelligence techniques for interpretation of food models: a review

Abstract Artificial Intelligence (AI) has become essential for analyzing complex data and solving highly-challenging tasks. It is being applied across numerous disciplines beyond computer science, including Food Engineering, where there is a growing demand for accurate and reliable predictions to meet stringent food quality standards. However, this requires increasingly complex AI models, raising concerns. In response, eXplainable AI (XAI) has emerged to provide insights into AI decision-making, aiding model int…

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Platformed Foodways in Helsinki: Young Immigrant Men, AI Tools, and “Networked Eating”

In a digitally saturated Helsinki, everyday eating is increasingly routed through apps, chats, and platform encounters. This article examines how young immigrant men (aged 21–35) activate these encounters and move across them to shape their food practices and a sense of belonging. In qualitative interviews, participants narrated how they search and share recipes and foods (e.g., through WhatsApp, Telegram, or Instagram), adapt dishes to cultural or religious preferences, learn new techniques, and use delivery pl…

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