Food science encompasses complex interactions among chemical composition, processing, biology, human perception, regulation, and sustainability. These characteristics make it a promising yet challenging domain for artificial intelligence. Recent advances have shown that AI can contribute to personalized nutrition, food safety monitoring, flavor prediction, and sustainability assessment^[1][2][3]. At the same time, these applications often address narrowly defined problems and rely on task-specific datasets or models, limiting their transferability across different food systems and contexts. The multidisciplinary nature of food science means that progress in one area does not automatically translate to others, highlighting the need for more coordinated and principled approaches. To move from isolated demonstrations toward sustained impact, a set of coordinated initiatives can help structure future AI-driven research and innovation in food science.

Initiative 1: Integrating Domain Knowledge into AI Models

A central initiative is the deep integration of food science domain knowledge into AI model design. Unlike applications focused solely on molecules or biological systems, food-related problems require consideration of food matrices, processing-induced transformations, regulatory constraints, and consumer behavior. Embedding chemical mechanisms, processing knowledge, and contextual constraints into AI models enables more realistic predictions, such as capturing synergistic effects among food components or anticipating changes during food processing^[4]. Domain-aware models also support applications in sustainability, where early-stage design decisions require integrating formulation, processing, and life-cycle considerations.

Initiative 2: Promoting Transparency, Reusability, and Just Evaluation

Transparency and reusability represent a second key initiative for advancing AI in food science. Many existing AI models remain difficult to reproduce due to limited access to code, data, or detailed methodological descriptions^[5]. Encouraging open science practices—such as sharing datasets, model architectures, and evaluation protocols—can improve trust and accelerate collective progress. Equally important is the adoption of just and rigorous evaluation practices, including benchmarking against prior methods and assessing model performance across diverse datasets. Such practices help distinguish genuine methodological advances from incremental variations and support fair comparison across studies.

Initiative 3: Establishing Benchmarking and Practical Validation

Benchmarking and validation form a third initiative that bridges methodological development and real-world relevance. In other scientific domains, community-driven benchmarking efforts have played a decisive role in advancing AI capabilities^[6]. Comparable initiatives in food science could provide shared reference datasets and evaluation tasks for applications such as flavor prediction, ingredient discovery, or food safety risk assessment. Beyond computational benchmarks, practical validation through laboratory experiments, pilot-scale studies, or industrial case examples remains essential. These validation efforts help assess robustness, identify limitations, and demonstrate applicability under realistic conditions.

Initiative 4: Developing Robust Data Standards and Infrastructure

Data availability and interoperability underpin all AI applications. A fourth initiative therefore focuses on building robust data standards and infrastructure for food science. Although large volumes of food-related data have been generated by researchers, regulators, and industry, much of this information remains fragmented, inaccessible, or poorly standardized^[7]. Establishing common data formats, metadata schemas, and ontologies can enable interoperability across datasets and facilitate AI model training. Robust data infrastructure also supports the development of domain-specific datasets needed for tasks such as predicting food processing reactions, assessing toxicity, or fine-tuning large language models with food-relevant knowledge.

Initiative 5: Encouraging Collaborative and Inclusive AI Ecosystems

The fifth initiative emphasizes collaboration across disciplines, sectors, and regions. Advancing AI in food science requires close interaction among food scientists, data scientists, experimentalists, regulators, and industry stakeholders. Collaborative frameworks can help align research priorities with practical needs, support experimental validation, and ensure responsible deployment. Inclusivity is equally important, as food systems vary widely across cultural, geographic, and socio-economic contexts. AI models trained on geographically or culturally narrow datasets may fail to generalize globally. Expanding data coverage and incorporating regional knowledge can help ensure that AI-driven solutions are relevant, equitable, and applicable across diverse food systems.

Outlook

Together, these five initiatives provide a strategic roadmap for advancing artificial intelligence in food science. By integrating domain knowledge, promoting transparency and benchmarking, strengthening data infrastructure, and fostering collaboration, AI can evolve from a collection of isolated tools into a robust methodological framework. Aligning future research with these initiatives may help unlock AI’s full potential to generate scientific insight, support sustainable innovation, and address pressing challenges in global food systems.

This entry is adapted from: 10.1016/j.tifs.2025.105272