1. Opportunities for AI in Food Science

Food systems are inherently rich and multidimensional, encompassing molecular composition, processing dynamics, sensory perception, biological responses, and environmental and societal contexts^[1]. These characteristics make food science a particularly promising domain for artificial intelligence. Recent studies have shown that AI can support tasks such as food property prediction, safety assessment, formulation design, and supply chain optimization^[2]. Beyond these established use cases, AI also offers opportunities to explore complex interactions that are difficult to isolate experimentally, such as synergistic effects among ingredients, dynamic changes during processing, and links between molecular composition and consumer perception. By enabling the integration of heterogeneous data across multiple scales, AI has the potential to reveal patterns and relationships that extend beyond traditional reductionist approaches. At the same time, many opportunities remain open for AI to contribute more deeply to scientific understanding and industrial innovation, especially by integrating diverse data sources and moving beyond narrowly defined tasks^[3][4].

2. Pillar I: Expanding and Enriching Datasets for Food AI

The availability of diverse, high-quality datasets represents a major opportunity for advancing AI in food science. Food-related data—covering chemical composition, processing reactions, sensory attributes, and biological effects—are increasingly being generated across academia and industry^[5]. Emerging tools now make it possible to organize and leverage these data more effectively.

Large language models (LLMs) provide new capabilities for extracting structured information from the growing body of scientific literature, patents, and regulatory documents^[6]. When combined with expert validation, LLM-assisted data curation can accelerate the construction of domain-specific datasets. In parallel, automated and high-throughput experimental platforms offer opportunities to systematically generate reproducible datasets across formulation and processing spaces^[7]. Additional approaches, including collaborative data-sharing initiatives, federated learning, and synthetic data generation, further expand the range of data available for AI-driven exploration.

3. Pillar II: Tailored Algorithms to Capture Food System Complexity

Food systems present distinctive modeling challenges and opportunities that can be addressed through tailored algorithmic approaches. Beyond conventional machine learning models, recent advances offer new ways to represent interactions among food components, processes, and sensory outcomes^[8].

Methods designed for imbalanced data, explainable AI techniques, and multimodal learning frameworks enable more robust and interpretable modeling of food-related phenomena^[9]. Physics-informed neural networks provide a pathway to integrate physicochemical principles directly into AI models, supporting predictions that are consistent with known mechanisms. Instruction-tuned large language models further expand the toolkit by enabling context-aware reasoning, natural-language interaction, and flexible problem formulation, opening new avenues for accessible and exploratory AI use in food science.

4. Pillar III: Unlocking New Application Scenarios and Impact

AI holds significant potential to unlock new application scenarios across food research and industry. In scientific research, AI can accelerate high-throughput discovery of functional compounds, bioactive molecules, and processing–property relationships^[10]. In industrial settings, AI-enabled computer vision, analytical data interpretation, and sensor-driven monitoring can enhance quality control and process optimization^[11]. At broader scales, AI-driven analysis of large, heterogeneous data sources offers opportunities to inform policy development, risk assessment, and strategic planning in food systems.

Many of these applications remain at an early stage, suggesting substantial room for innovation as AI methods mature and datasets expand.

5. A Forward-Looking Checklist for Food Scientists

To help realize these opportunities, a practical checklist proposed by Zhang, D.^[3] can guide food scientists in developing AI-enabled studies. Rather than serving as a rigid protocol, this checklist highlights key considerations across the lifecycle of AI research, from data creation to real-world deployment, and emphasizes alignment between scientific goals and methodological choices.

A natural starting point is the deliberate curation and expansion of high-quality datasets. For food scientists, this involves not only assembling sufficient data volume, but also ensuring that datasets reflect the complexity of real food systems. Integrating chemical, processing, sensory, and biological data can enable AI models to capture interactions that are otherwise difficult to study experimentally. As new tools such as LLM-assisted extraction and automated experimentation become more accessible, dataset development itself can evolve into an active research process that opens new scientific questions rather than merely supporting downstream modeling.

Once suitable data resources are established, attention can shift toward algorithmic design. Exploring domain-aware algorithms allows researchers to move beyond generic prediction tasks and instead address food-specific challenges, such as interacting ingredients, nonlinear processing effects, or sparse and imbalanced observations. At this stage, the checklist encourages thoughtful selection of models that balance predictive performance with interpretability, enabling AI outputs to contribute to mechanistic understanding rather than functioning as opaque black boxes.

Transparency and reproducibility form a critical bridge between model development and broader impact. Clearly documenting data sources, preprocessing steps, model architectures, and evaluation strategies helps ensure that AI studies can be interpreted, reproduced, and extended by others. In food science, where regulatory considerations and industrial adoption are often important, transparent modeling practices also support trust and facilitate dialogue between researchers, industry stakeholders, and policymakers.

Validation represents another key element of the checklist. While computational benchmarks provide useful initial insights, validating AI models in laboratory, pilot-scale, or real-world settings is often essential for assessing practical relevance. Experimental validation can reveal limitations that are not apparent from retrospective data alone and can guide iterative model refinement. In this sense, validation is not an endpoint but an opportunity to strengthen the connection between AI predictions and observable food system behavior.

Embedding AI models into scalable workflows further enhances their long-term value. Rather than remaining isolated analytical tools, AI systems can be integrated into experimental design, process control, quality monitoring, or decision-support frameworks. Such integration allows AI to actively shape how experiments are conducted and how processes are optimized, amplifying its impact across research and industrial contexts.

Finally, the checklist highlights the importance of adaptability. Food systems are dynamic, influenced by changing raw materials, processing technologies, environmental conditions, and consumer preferences. Designing AI systems that can learn from new data and evolve over time enables more resilient and future-ready applications. Adaptive learning frameworks and continuous data integration remain relatively underexplored in food science, representing a promising direction for sustained AI-driven innovation.

This entry is adapted from: 10.1016/j.foodchem.2025.147281