Artificial intelligence for food innovation

Machine Learning


  • Lappé, F. M. Diet for a Small Planet (Ballantine Books, 1971).

  • Clark, M. A. et al. Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science 370, 705–708 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Xu, X. et al. Global greenhouse gas emissions from animal-based foods are twice those of plant-based foods. Nat. Food 2, 724–732 (2021). This study shows that animal-based foods produce roughly twice the greenhouse gases of plant-based foods. It underscores the environmental benefits of plant and microbial proteins central to sustainable food design.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Keesing, F. Diet for a small footprint. Proc. Natl Acad. Sci. USA 119, e2204241119 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Friedrich, B. Transforming a 12,000-year-old technology. Nat. Food 3, 807–808 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Barabasi, A. L. et al. The unmapped chemical complexity of our diet. Nat. Food 1, 33–37 (2020). This perspective reveals the vast and largely unexplored chemical diversity in everyday foods. It highlights the need for computational and AI-driven mapping of food molecules to understand nutrition, flavour and health interactions.

    Article 

    Google Scholar 

  • Zeni, C. et al. A generative model for inorganic materials design. Nature 639, 624–632 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Al-Sarayreh, M. et al. Inverse design and AI/deep generative networks in food design: a comprehensive review. Trends Food Sci. Technol. 138, 215–228 (2023).

    Article 
    CAS 

    Google Scholar 

  • Datta, A. et al. Computer-aided food engineering. Nat. Food 3, 894–904 (2022). This review outlines how computation, modelling and automation can transform food engineering. It establishes a framework for integrating AI, digital twins and process optimization into food design and manufacturing.

    Article 
    PubMed 

    Google Scholar 

  • King, A. Four ways to power-up AI for drug discovery. Nature https://doi.org/10.1038/d41586-025-00602-5 (2025).

  • Kuhl, E. AI for food: accelerating and democratizing discovery and innovation. npj Sci. Food 9, 82 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Turing, A. M. Computing machinery and intelligence. Mind 59, 433–460 (1950).

    Article 
    MathSciNet 

    Google Scholar 

  • Rumelhart, D. E. et al. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).

    Article 
    ADS 

    Google Scholar 

  • Krizhevsky, A. et al. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).

    Article 

    Google Scholar 

  • Goodfellow, I. et al. Generative adversarial networks. Adv. Neural Inf. Process. Syst. 27, 2672–2680 (2014). This work presents the GAN framework for generating realistic synthetic data. It underpins AI applications in food imaging, formulation design and sensory data augmentation.

    Google Scholar 

  • Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017). This landmark paper introduces the transformer architecture of today’s large language models. It enables multimodal AI systems for recipe generation, ingredient substitution and scientific discovery.

    Google Scholar 

  • International Human Genome Sequencing Consortium Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    Article 
    ADS 

    Google Scholar 

  • Gilmer, J. et al. Neural message passing for quantum chemistry. Proc. Int. Conf. Mach. Learn. 34, 1263–1272 (2017). This paper introduces message-passing neural networks for predicting molecular properties from graph structures. It lays the foundation for AI models in food chemistry and ingredient design.

    Google Scholar 

  • Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Gordon, E. B. et al. Biomaterials in cellular agriculture and plant-based foods for the future. Nat. Rev. Mater. 10, 500–518 (2025).

    Article 

    Google Scholar 

  • Schreurs, M. et al. Predicting and improving complex beer flavor through machine learning. Nat. Commun. 15, 2368 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lee, B.-K. et al. A principal odor map unifies diverse tasks in olfactory perception. Science 381, 999–1006 (2023). This work creates a continuous map linking molecular structure to human odour perception. It enables AI models to predict and generate new flavour and aroma profiles for food design.

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). This breakthrough model achieves near-experimental accuracy in protein structure prediction. It inspires the design of functional food proteins and enzymes for alternative protein production.

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Butler, K. T. et al. Machine learning for molecular and materials science. Nature 559, 547–555 (2018). This review outlines how machine learning accelerates discovery in chemistry and materials science. It provides key methodologies directly transferable to AI-based formulation and texture engineering in food.

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Graham, A. E. & Ledesma-Amaro, R. The microbial food revolution. Nat. Commun. 14, 2231 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lurie-Luke, E. Alternative protein sources: science-powered startups to fuel food innovation. Nat. Commun. 15, 4425 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kyriakopoulou, K. et al. in Sustainable Meat Production and Processing (ed. Galanakis, C. M.) 103–126 (Academic Press, 2019).

  • Fasolin, L. H. et al. Emergent food proteins—towards sustainability, health and innovation. Foods 10, 600 (2021).

    Google Scholar 

  • Day, L. Proteins from land plants—potential resources for human nutrition and food security. Trends Food Sci. Technol. 32, 25–42 (2013).

    Article 
    CAS 

    Google Scholar 

  • Liu, M.-Q. et al. Digging natural emulsifiers based on machine learning and exploration their performance for stabilizing dairy products. Preprint at SSRN https://ssrn.com/abstract=4994781 (2025).

  • Siejak, P. et al. The prediction of pectin viscosity using machine learning based on physical characteristics—case study: aglupectin HS-MR. Sustainability 16, 5877 (2024).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Lie-Piang, A. et al. Quantifying techno-functional properties of ingredients from multiple crops using machine learning. Curr. Res. Food Sci. 7, 100601 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Yilmaz, M. T. et al. Explainable AI-driven evaluation of plant protein rheology using tree-based and Gaussian process machine learning models. Ain Shams Eng. J. 16, 103565 (2025).

    Article 

    Google Scholar 

  • Kraessig, P. M. et al. Sensory-biased autoencoder enables prediction of texture perception from food rheology. Food Res. Int. 205, 116007 (2025).

    Article 
    PubMed 

    Google Scholar 

  • Meng, Y. et al. Advancing salt reduction technologies: AI-assisted structural design of starch-based emulsion gel systems for next-generation low sodium food formulations. Trends Food Sci. Technol. 264, 105234 (2025).

    Article 

    Google Scholar 

  • NotCo. Latent space method of generating food formulas. US patent 10,915,818 (2021).

  • NotCo. Neural network method of generating food formulas. US patent 10,957,424 (2021).

  • NotCo. Systems and methods to mimic target food items using artificial intelligence. US patent 11,164,478 (2021).

  • Martin, J. et al. Recipe1M+: a dataset for learning cross-modal embeddings for cooking recipes and food images. IEEE Trans. Pattern Anal. Mach. Intell. 43 187–203 (2021).

  • Park, D. et al. FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings. Sci. Rep. 11, 931 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Garg, N. et al. FlavorDB: a database of flavor molecules. Nucl. Acids Res. 46, D1210–D1216 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ahn, Y.-Y. et al. Flavor network and the principles of food pairing. Sci. Rep. 1, 196 (2011).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tac, V., Gardner, C. & Kuhl, E. Generative artificial intelligence creates delicious, sustainable, and nutritious burgers. Preprint at https://arxiv.org/abs/2602.03092 (2026). This study uses generative AI to create burgers that outperformed the classic Big Mac in a blinded restaurant survey.

  • van den Bedem, S. D. et al. Open-source benchmarking of plant-based and animal meats. Foods 15, 2112 (2026).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Eastham, J. L. & Leman, A. R. Precision fermentation for food proteins: ingredient innovations, bioprocess considerations, and outlook—a mini-review. Curr. Opin. Food Sci. 58, 101194 (2024).

    Article 
    CAS 

    Google Scholar 

  • Feng, X. et al. Enhanced lipid production by Chlorella pyrenoidosa through magnetic field pretreatment of wastewater and treatment of microalgae-wastewater culture solution: magnetic field treatment modes and conditions. Bioresour. Technol. 206, 123102 (2020).

    Article 

    Google Scholar 

  • Sharma, D. et al. Response surface methodology and artificial neural network modelling for enhancing maturity parameters during vermicomposting of floral waste. Bioresour. Technol. 324, 124672 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Liao, X. et al. Artificial Intelligence: a solution to involution of design–build–test–learn cycle. Curr. Opin. Biotechnol. 75, 102712 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Liu, L. et al. Modeling and optimization of microbial hyaluronic acid production by Streptococcus zooepidemicus using radial basis function neural network coupling quantum-behaved particle swarm optimization algorithm. Biotechnol. Prog. 25, 1819–1825 (2009).

    Article 
    PubMed 

    Google Scholar 

  • Jia, R. et al. Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors. Bioresour. Technol. 363, 127908 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Cheng, Y. et al. Artificial intelligence technologies in bioprocess: opportunities and challenges. Bioresour. Technol. 369, 128451 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Treloar, N. J. et al. Deep reinforcement learning for the control of microbial co-cultures in bioreactors. PLoS Comput. Biol. 16, e1007783 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Singhal, A. et al. Pretreatment of Leucaena leucocephala wood by acidified glycerol: optimization, severity index and correlation analysis. Bioresour. Technol. 265, 214–223 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Austerjost, J. et al. A machine vision approach for bioreactor foam sensing. SLAS Technol. 26, 408–414 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Butean, A. et al. A review of artificial intelligence applications for biorefineries and bioprocessing: from data-driven processes to optimization strategies and real-time control. Processes 13, 2544 (2025).

    Article 

    Google Scholar 

  • Alejo, L. et al. Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques. Environ. Sci. Pollut. Res. Int. 25, 21149–21163 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Akinade, O. O. & Oyedele, L. O. Integrating construction supply chains within a circular economy: an ANFIS-based waste analytics system (A-WAS). J. Clean. Prod. 229, 863–873 (2019).

    Article 

    Google Scholar 

  • Peng, J. et al. Time-dependent fermentation control strategies for enhancing synthesis of marine bacteriocin 1701 using artificial neural network and genetic algorithm. Bioresour. Technol. 138, 345–352 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Sharma, D. & Singh, K. AI-enhanced bioprocess technologies: machine learning implementations from upstream to downstream operations. World J. Microbiol. Biotechnol. 41, 278 (2025).

    Article 
    PubMed 

    Google Scholar 

  • Holzinger, A. et al. AI for life: trends in artificial intelligence for biotechnology. New Biotechnol. 74, 16–24 (2023).

    Article 
    CAS 

    Google Scholar 

  • Ghafarollahi, A. & Buehler, M. J. ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning. Digit. Discov. 3, 1389–1409 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • St. Pierre, S. R. et al. Discovering the mechanics of artificial and real meat. Comput. Methods Appl. Mech. Eng. 415, 116236 (2023).

    Article 
    MathSciNet 

    Google Scholar 

  • St. Pierre, S. R. et al. The mechanical and sensory signature of plant-based and animal meat. npj Sci. Food 8, 94 (2024). This study quantitatively links mechanical texture measurements with human sensory perception in plant-based and animal meats. It provides critical benchmark data for AI models to predict texture and mouthfeel from physical properties.

    Article 

    Google Scholar 

  • Sanahuja, M. et al. Classification of puffed snacks freshness based on crispiness-related mechanical and acoustical properties. J. Food Eng. 226, 53–64 (2018).

    Article 

    Google Scholar 

  • Dahl, J. F. et al. Predicting rheological parameters of food biopolymer mixtures using machine learning. Food Hydrocoll. 160, 110786 (2025).

    Article 
    CAS 

    Google Scholar 

  • Zhang, Y. et al. Construction of an intelligent recognition system for the cooking doneness of deep-fried golden pompano (Trachinotus ovatus) based on deep learning. Food Biosci. 68, 106644 (2025).

    Article 
    CAS 

    Google Scholar 

  • Saalbrink, J. et al. Quantifying microscopic droplets in colloidal systems through machine learning-based image analysis. Food Hydrocoll. 166, 111301 (2025).

    Article 
    CAS 

    Google Scholar 

  • Dunne, R. A. et al. Texture profile analysis and rheology of plant-based and animal meat. Food Res. Int. 205, 115876 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Munekata, P. E. S. et al. Applications of electronic nose, electronic eye and electronic tongue in quality, safety and shelf life of meat and meat products: a review. Sensors 23, 672 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Karakaya, D. et al. Electronic nose and its applications: a survey. Int. J. Autom. Comput. 17, 179–209 (2019).

    Article 

    Google Scholar 

  • Bengio, Y. et al. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2012).

    Article 
    ADS 

    Google Scholar 

  • Tom, G. et al. From molecules to mixtures: learning representations of olfactory mixture similarity using inductive biases. Preprint at https://arxiv.org/abs/2501.16271 (2025).

  • Istif, E. et al. Miniaturized wireless sensor enables real-time monitoring of food spoilage. Nat. Food 4, 427–436 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Watson, N. J. et al. Intelligent sensors for sustainable food and drink manufacturing. Sustain. Food Syst. 5, 642786 (2021).

    Article 

    Google Scholar 

  • Rombach, R. et al. High-resolution image synthesis with latent diffusion models. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 10684–10695 (IEEE, 2022).

  • Dinali, M. et al. Fibrous structure in plant-based meat: high-moisture extrusion factors and sensory attributes in production and storage. Food Rev. Int. 40, 2940–2968 (2024).

    Article 

    Google Scholar 

  • Jiang, Y. et al. Plant-based protein extrusion optimization: comparison between machine learning and conventional experimental design. Curr. Res. Food Sci. 11, 101157 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Abdurrahman, E. E. M. et al. Digital twin applications in the food industry: a review. Front. Sustain. Food Syst. 9, 1538375 (2025).

    Article 

    Google Scholar 

  • Top, J. et al. Cultivating FAIR principles for agri-food data. Comput. Electron. Agric. 196, 106909 (2022).

    Article 

    Google Scholar 

  • Bölücü, N. et al. An evaluation of large language models for supplementing a food extrusion dataset. Foods 14, 1355 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Watson, N. & Rafiq, S. NAPIC Partner Engagement Workshop Report (Univ. Leeds, 2025); https://doi.org/10.48785/100/320

  • Reducing the Price of Alternative Proteins (Good Food Institute, 2025); https://gfi.org/reducing-the-price-of-alternative-proteins

  • Mohbat, F. & Mohammed, J. Z. Llava-chef: a multi-modal generative model for food recipes. In Proc. 33rd ACM International Conference on Information and Knowledge Management (ACM, 2024).

  • Li, P. et al. Cheffusion: multimodal foundation model integrating recipe and food image generation. In Proc. 33rd ACM International Conference on Information and Knowledge Management (ACM, 2024).

  • Senath, T. et al. Large language models for ingredient substitution in food recipes using supervised fine-tuning and direct preference optimization. Preprint at https://arxiv.org/abs/2412.04922 (2024).

  • Tac, V. & Kuhl, E. Generative AI for material design: a mechanics perspective from burgers to matter. Comp. Meth. Appl. Mech. Eng. 61, 119171 (2026).

    Article 
    MathSciNet 

    Google Scholar 

  • Liu, G. et al. Retrieval augmented recipe generation. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (IEEE, 2025).

  • Thomas, A. T. et al. What can large language models do for sustainable food? Proc. 42nd Int. Conf. Mach. Learn 267, 59377–59433 (PMLR, 2025).

  • Yang, D. et al. Social skill training with large language models. Preprint at https://arxiv.org/abs/2404.04204 (2024).

  • Rosas, L. G. et al. The effectiveness of Recipe4Health: a quasi-experimental evaluation. Am. J. Prevent. Med. 68, 377–390 (2025).

    Article 

    Google Scholar 

  • Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Yuen, J. S. K. et al. Aggregating in vitro-grown adipocytes to produce macroscale cell-cultured fat tissue with tunable lipid compositions for food applications. eLife 12, e82120 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Vervenne, T. et al. Probing mycelium mechanics and taste: the moist and fibrous signature of fungi steak. Acta Biomat. 202, 341–351 (2025).

    Article 

    Google Scholar 

  • Rackauckas, C. et al. Universal differential equations for scientific machine learning. Preprint at https://arxiv.org/abs/2001.04385 (2020).

  • Raissi, M. et al. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019).

    Article 
    ADS 
    MathSciNet 

    Google Scholar 

  • Karniadakis, G. E. et al. Physics-informed machine learning. Nat. Rev. Phys. 3, 422–440 (2021).

    Article 

    Google Scholar 

  • Linka, K. & Kuhl, E. A new family of constitutive artificial neural networks towards automated model discovery. Comput. Methods Appl. Mech. Eng. 403, 115731 (2023).

    Article 
    MathSciNet 

    Google Scholar 

  • Brunton, S. L. et al. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl Acad. Sci. USA 113, 3932–3937 (2016).

    Article 
    ADS 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • St. Pierre, S. R. et al. Biaxial testing and sensory texture evaluation of plant-based and animal deli meat. Curr. Res. Food Sci. 10, 101080 (2025).

    Article 

    Google Scholar 

  • Ghafarollahi, A. & Buehler, M. J. Sparks: multi-agent artificial intelligence model discovers protein design principles. Preprint at https://arxiv.org/abs/2504.19017 (2025).

  • Roohani, Y. et al. BioDiscoveryAgent: an AI agent for designing genetic perturbation experiments. Preprint at https://arxiv.org/abs/2405.17631 (2024).

  • Swanson, K. et al. The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies. Nature 646, 716–723 (2025).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Buehler, M. J. Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning. Mach. Learn. Sci. Technol. 5, 035083 (2024).

    Article 
    ADS 

    Google Scholar 

  • Boiko, D. A. et al. Autonomous chemical research with large language models. Nature 624, 570–578 (2023). This paper demonstrates how large language models can autonomously plan, execute and interpret chemical experiments. It marks a major step towards self-driving discovery systems, directly inspiring similar AI-driven pipelines for food formulation and ingredient design.

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Peirlinck, M. et al. On automated model discovery and a universal material subroutine for hyperelastic materials. Comput. Methods Appl. Mech. Eng. 418, 116534 (2024).

    Article 
    MathSciNet 

    Google Scholar 



  • Source link