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.

Are you interested in contributing a manuscript to this Research Topic?

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