AI-Driven Non-Thermal Processing for Nutritious and Sustainable Food Innovation

Growing consumer expectations for nutritious, clean-label, and minimally processed foods are driving a global shift toward sustainable production strategies. Conventional thermal treatments, while effective for safety and shelf-life extension, can negatively impact sensory attributes, nutritional value, and bioactive compound stability. As a result, non-thermal technologies - such as ultrasound, cold plasma, pulsed electric fields, and high-pressure processing -are gaining prominence for their ability to preserve quality while reducing energy consumption and processing intensity. Meanwhile, the rapid advancement of artificial intelligence (AI), machine learning, and data-driven decision-making has opened new opportunities for modeling, optimization, and automation in food processing. The convergence of these fields offers a unique pathway to design eco-efficient processes, minimize resource use, and support resilient and sustainable food systems.

This Research Topic aims to address current challenges in food processing by integrating AI-based tools with innovative non-thermal technologies. Despite significant progress, there is still a need for predictive solutions that can maximize nutritional retention, enhance product functionality, reduce processing losses, and minimize environmental impact. AI presents a transformative opportunity to improve process control, predict quality outcomes, and tailor treatments to specific food matrices. By combining real-time monitoring, intelligent modeling, and automated system design, data-driven approaches can accelerate the transition toward cleaner and more sustainable processing chains. This Research Topic seeks contributions that advance greener technologies, develop intelligent workflows, and explore techno-economic or life-cycle benefits. Ultimately, the goal is to support a more efficient, circular, and climate-smart food system enabled by AI-guided non-thermal processing.

We welcome Original Research, Reviews, and Perspectives that explore innovative applications of AI and machine learning in combination with non-thermal food technologies. Relevant themes include, but are not limited to:

– AI-based optimization and predictive modeling in non-thermal processing (e.g., RSM, ANN, ANFIS, SVR, MLP)

– Modeling and improving bioactive retention, antioxidant stability, shelf-life, and sensory attributes

– Design of nutrient-dense, clean-label, and functional food products

– Techno-economic assessments or life-cycle analysis of non-thermal technologies

– AI-driven, sensor-integrated systems, real-time monitoring, and automated process control

– Intelligent processing approaches for personalized and sustainable nutrition

Submissions contributing to climate-smart practices, resource efficiency, and digital transformation in food manufacturing are encouraged. Through this collection, we aim to foster innovative research that accelerates the development of resilient, data-empowered, and eco-efficient food systems.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial intelligence, Non-thermal food processing, Ultrasound, Sustainable food systems, Predictive modeling, Life cycle assessment, Clean-label products, High-pressure processing (HPP)

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.