Opportunities of digital twin in controlling and monitoring microbial processes in food fermentation

Abstract

Digital twin (DT) technology is emerging as a promising approach for predictive analysis of real-world processes on the computer screen and transforming the optimization process. In food fermentation industry, feedstock variability, environmental deviations, as well as microbial dynamics together make it a very rigorous, tedious, and time-consuming process. This is where digital twin technology can change the fate of the industry. In this review paper, the theoretical approach along with its components and technological enablers such as soft sensors, cloud-integration platforms for data analysis and storage, process analytical tools, machine learning, artificial intelligence, Internet of Things, etc. of DT are discussed, which combine together to form the digital twin system. This study emphasizes the opportunities that DTs provide to the food fermentation industries, ranging from real-time monitoring and control of the microbial processes, adaptive control, contamination prediction, evaluating metabolites and by-products concentration to the simulation of the physical fermenter. While the technology provides promising results at the bench-scale, several limitations persist, which have been critically examined, such as biological variability, lack of standardized frameworks and common data platforms for diverse datasets. It also highlights the future prospects of the digital twins in the food fermentation industry by inducing modifications in the system and feeding pilot-scale data, which could pave the way for their incorporation, developing a sustainable and cost-effective technology.

Keywords

Smart fermentation system / Internet of Things (IoT) / Industry 4.0 / Microbial process optimization / Modelling and prediction / Real-time data synchronization / Cloud-based bioprocessing

References

[1] Abdurrahman EEM, Ferrari G. Digital Twin applications in the food industry: A review. Front Sustainable Food Syst, 2025, 9: 1538375.

[2] Adeleke I, Adebo OA, Nwulu N. Leveraging IoT and machine learning for smart fermentation of amasi: A predictive framework for acidity control. J Agric Food Res, 2025, 24: 102409.

[3] Adeleke I, Nwulu N, Adebo OA. Internet of Things (IoT) in the food fermentation process: A bibliometric review. J Food Process Eng, 2023, 46(5): e14321.

[4] Albino M, Gargalo CL, Nadal-Rey G, Albæk MO, Krühne U, Gernaey KV. Hybrid modeling for on-line fermentation optimization and scale-up: A review. Processes, 2024, 12(8): 1635.

[5] Appl C, Moser A, Baganz F, Hass VC. Digital twins for bioprocess control strategy development and realisation. In: Herwig C, Pörtner R, Möller J, editors. Digital Twins, vol. 177. Springer International Publishing; 2020. pp. 63–94. https://doi.org/10.1007/10_2020_151.

[6] Beri R, Sachdeva P. (2025). Integrating edge-cloud computing and IoT for Real-time food quality assessment. In T. Senjyu, C. So-In, & A. Joshi, editors, Smart Trends in Computing and Communications (Vol. 1469, pp. 69–78). Springer; Singapore. https://doi.org/10.1007/978-981-96-7807-5_7

[7] Bhattacharya R, Arora S, Ghosh S. Bioprocess optimization for food-grade cellulolytic enzyme production from sorghum waste in a novel solid-state fermentation bioreactor for enhanced apple juice clarification. J Environ Manage, 2024, 358: 120781.

[8] Buonocore D, Ciavolino G, Caro DD, Liguori C. An IoT-based beer fermentation monitoring system. 2021 IEEE international workshop on metrology for agriculture and forestry (MetroAgriFor). 2021. 263–267. https://doi.org/10.1109/MetroAgriFor52389.2021.9628536

[9] Chen Y, Yang O, Sampat C, Bhalode P, Ramachandran R, Ierapetritou M. Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review. Processes, 2020, 8(9): 1088.

[10] Cheng X, Yu Z, Wang G, Jiang Q, Cao Z. Semi-supervised soft sensor method for fermentation processes based on physical monotonicity and variational autoencoders. Eng Appl Artif Intell, 2024, 137: 109065.

[11] Choi J-Y, Lee M, Kim M, Lee M-A, Min SG, Chung YB, Yang J-H, Park SH. Chemometrics for estimating the fermentation and quality properties of kimchi based on hyperspectral image analysis. Food Res Int, 2024, 198: 115307.

[12] Dakhia Z, Russo M, Merenda M. AI-enabled IoT for food computing: Challenges, opportunities, and future directions. Sensors, 2025, 25(7): 2147.

[13] Dazzarola C, Tighe R, Pérez-Correa JR, Saa PA. Toward a digital twin for beer quality control: Development of a digital model integrating industrial process data and model-based fermentation descriptors. J Food Eng, 2026, 403: 112726.

[14] Dhiman S, Kaur S, Thakur B, Singh P, Tripathi M. Nutritional enhancement of plant-based fermented foods: Microbial innovations for a sustainable future. Fermentation, 2025, 11(6): 346.

[15] Feng Y, Tian X, Chen Y, Wang Z, Xia J, Qian J, Zhuang Y, Chu J. Real-time and on-line monitoring of ethanol fermentation process by viable cell sensor and electronic nose. Bioresources Bioprocess, 2021, 8(1): 37.

[16] Ferrocino I, Rantsiou K, McClure R, Kostic T, De Souza RSC, Lange L, FitzGerald J, Kriaa A, Cotter P, Maguin E, Schelkle B, Schloter M, Berg G, Sessitsch A, Cocolin L. The need for an integrated multi-OMICs approach in microbiome science in the food system. Compr Rev Food Sci Food Saf, 2023, 22(2): 1082-103.

[17] Food fermentations. NIR spectroscopy as a tool for process analytical technology. In Advances in Food and Nutrition Research. Elsevier; 2025. Vol. 115, pp. 391–430. https://doi.org/10.1016/bs.afnr.2025.06.002

[18] Fuller A, Fan Z, Day C, Barlow C. Digital Twin: Enabling technologies, challenges and open research. IEEE Access, 2020, 8: 108952-71.

[19] Genzardi D, Caruso I, Poeta E, Sberveglieri V, Núñez Carmona E. Nano-tailored triple gas sensor for real-time monitoring of dough preparation in kitchen machines. Sensors, 2025, 25(9): 2951.

[20] Glaessgen, E., & Stargel, D. The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference20th AIAA/ASME/AHS Adaptive Structures Conference14th AIAA. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference < BR>20th AIAA/ASME/AHS Adaptive Structures Conference < BR>14th AIAA, Honolulu, Hawaii; 2012. https://doi.org/10.2514/6.2012-1818

[21] Goffi P-E, Tremblay R, Oakes B. Engineering a Digital Twin for the monitoring and control of beer fermentation sampling (version 1). arXiv. 2025. https://doi.org/10.48550/ARXIV.2508.18452.

[22] Goldrick S, Duran-Villalobos CA, Jankauskas K, Lovett D, Farid SS, Lennox B. Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process. Comput Chem Eng, 2019, 130: 106471.

[23] Gong P, Shi R, Tang J, Wang J, Luo Q, Zhang J, Ruan X, Wang C, Chen W. Effect of exogenous and endogenous ectoine on monascus development, metabolism, and pigment stability. Foods, 2023, 12(17): 3217.

[24] Gottschald M. Advancing food safety through digital traceability, interoperability, harmonized data and collaborative partnerships. J Consumer Prot Food Saf, 2024, 19(3): 257-8.

[25] Grieves M, Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In Kahlen F-J, Flumerfelt S, Alves A, editors, Transdisciplinary Perspectives on Complex Systems. Springer International Publishing; 2017. pp. 85–113. https://doi.org/10.1007/978-3-319-38756-7_4

[26] Hassan M, Montague G, Iqbal MZ, Fahey J. Virtual reality-based bioreactor digital twin for operator training. Digit Chem Eng, 2024, 11: 100147.

[27] Hassoun A, Jagtap S, Trollman H, Garcia-Garcia G, Duong LNK, Saxena P, Bouzembrak Y, Treiblmaier H, Para-López C, Carmona-Torres C, Dev K, Mhlanga D, Aït-Kaddour A. From food industry 4.0 to food industry 5.0: Identifying technological enablers and potential future applications in the food sector. Compr Rev Food Sci Food Saf, 2024, 23(6): e370040.

[28] Helmy M, Smith D, Selvarajoo K. Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering. Metabolic Eng Commun, 2020, 11: e00149.

[29] Henrichs E, Noack T, Pinzon Piedrahita AM, Salem MA, Stolz J, Krupitzer C. Can a byte improve our bite? An analysis of Digital Twins in the food industry. Sensors, 2021, 22(1): 115.

[30] Hermann L, Kremling A. A hybrid soft sensor approach combining partial least-squares regression and an unscented Kalman filter for state estimation in bioprocesses. Bioengineering, 2025, 12(6): 654.

[31] Hsueh K-L, Lin T-Y, Lee M-T, Hsiao Y-Y, Gu Y. Design of experiments for modeling of fermentation process characterization in biological drug production. Processes, 2022, 10(2): 237.

[32] INRAE. FermenTwin: Digital twin for food microbiota prediction in vegetable fermentation. 2024. https://fme.micalis.fr/projects/fermentwin/

[33] Isoko K, Cordiner L, Kis J, Moghadam Z. Bioprocessing 4.0: A pragmatic review and future perspectives. Digit Discov, 2024, 3(9): 1662-81.

[34] Jamshidi MB, Hoang DT, Nguyen DN. CNN-FL for biotechnology industry empowered by internet-of-bionano things and Digital Twins. IEEE Internet Things Mag, 2024, 7(5): 54-63.

[35] Jiang H, Deng J, Chen Q. Monitoring of simultaneous saccharification and fermentation of ethanol by multi-source data deep fusion strategy based on near-infrared spectra and electronic nose signals. Eng Appl Artif Intell, 2024, 127: 107299.

[36] Jones D, Snider C, Nassehi A, Yon J, Hicks B. Characterising the Digital Twin: A systematic literature review. CIRP J Manufact Sci Technol, 2020, 29: 36-52.

[37] Joshi M, Meena KK, Kumar A, Meena S. Optimization of a novel probiotic-fermented pearl millet-based strained yoghurt-like functional dessert: Physicochemical, microbial and sensory characterization. Sustainable Food Technol, 2025, 3(4): 1074-85.

[38] Kayitesi E, Onojakpor O, Moyo SM. Highlighting the impact of lactic-acid-bacteria-derived flavours or aromas on sensory perception of African fermented cereals. Fermentation, 2023, 9(2): 111.

[39] Keskin E, Yıldırım IO. Cultivating precision: Comparative analysis of sensor-based yogurt fermentation monitoring techniques (version 1). arXiv. 2025. https://doi.org/10.48550/ARXIV.2501.08781.

[40] Kontogiannis S, Tsoumani M, Kokkonis G, Pikridas C, Kotseridis Y. Proposed SmartBarrel System for monitoring and assessment of wine fermentation processes using IoT nose and tongue devices. Sensors, 2025, 25(13): 3877.

[41] Krupitzer C, Noack T, Borsum C. Digital food twins combining data science and food science: System model, applications, and challenges. Processes, 2022, 10(9): 1781.

[42] Kwon HJ, Shiu JH, Yamakawa CK, Rivera EC. Enhancing fermentation process monitoring through data-driven modeling and synthetic time series generation. Bioengineering, 2024, 11(8): 803.

[43] Lecomte M, Cao W, Aubert J, Sherman DJ, Falentin H, Frioux C, Labarthe S. A digital twin of bacterial metabolism during cheese production. Bioinformatics, 2023.

[44] Lee A, Manrique Negrin DA, Cleophas L. Evaluating open-source tools for heterogeneous model-based digital twin development: A microbrewery case study. 2024 Annual Modeling and Simulation Conference (ANNSIM). 2024a. 1–13. https://doi.org/10.23919/ANNSIM61499.2024.10732658

[45] Lee C-H, Ahn J, Son H-S. Ethnic fermented foods of the world: An overview. J Ethnic Foods, 2024, 11(1): 39.

[46] Lee J, Jeong J, Kim S. Pressure-guided LSTM modeling for fermentation quantification prediction. Sensors, 2025, 25(17): 5251.

[47] Liu G-P. Control strategies for Digital Twin systems. IEEE/CAA J Autom Sin, 2024, 11(1): 170-80.

[48] Meena KK, Taneja NK, Jain D, et al.. Spontaneously fermented cereal based products: An ancient health promoting formulae for consumption of probiotic lactic acid bacteria. Biointerface Res Appl Chem, 2023, 13(5): 465.

[49] Mendes I, Monteiro J, Barata J. A heritage Digital Twin for Serra Da Estrela Cheese production. 32nd International Conference on Information Systems Development, Gdańsk, Poland; 2024. https://doi.org/10.62036/ISD.2024.3

[50] Moser A, Appl C, Pörtner R, Baganz F, Hass VC. A new concept for the rapid development of Digital Twin core models for bioprocesses in various reactor designs. Fermentation, 2024, 10(9): 463.

[51] Mujahid M. Food fermentation: Traditional practices and modern applications in food industry. Int J Food Ferment Technol, 2024, 14(1): 239-73.

[52] Muncan J, Tei K, Tsenkova R. Real-time monitoring of yogurt fermentation process by aquaphotomics near-infrared spectroscopy. Sensors, 2020, 21(1): 177.

[53] Negri E, Fumagalli L, Macchi M. A review of the roles of Digital Twin in CPS-based production systems. Procedia Manuf, 2017, 11: 939-48.

[54] Neubauer P, Anane E, Junne S, Cruz Bournazou MN. Potential of integrating model-based design of experiments approaches and process analytical technologies for bioprocess scale-down. In: Herwig C, Pörtner R, Möller J, editors. Digital Twins. Volume 177. Springer International Publishing; 2020. pp. 1–28. https://doi.org/10.1007/10_2020_154.

[55] Newrzella SR, Franklin DW, Haider S. 5-dimension cross-industry Digital Twin applications model and analysis of Digital Twin classification terms and models. IEEE Access, 2021, 9: 131306-21.

[56] Nguyen XDJ, Liu YA, McDowell CC, Dooley L. Methodology for contamination detection and reduction in fermentation processes using machine learning. Bioprocess Biosyst Eng, 2025, 48(9): 1547-63.

[57] Ortiz JS, Quishpe EK, Sailema GX, Guamán NS. Digital Twin-based active learning for industrial process control and supervision in industry 4.0. Sensors, 2025, 25(7): 2076.

[58] Panunzi A, Moroni M, Mazzelli A, Bravi M. Industrial case-study-based computational fluid dynamic (CFD) modeling of stirred and aerated bioreactors. ACS Omega, 2022, 7(29): 25152-63.

[59] Perno M, Hvam L, Haug A. Uses and challenges of digital twins-based augmented reality in operator training and data visualization in process manufacturing lines. Flex Serv Manuf J, 2025.

[60] Peterson L, Gosea IV, Benner P, Sundmacher K. Digital twins in process engineering: An overview on computational and numerical methods. Comput Chem Eng, 2025, 193: 108917.

[61] Priyadharshini D, Muthuvel I, Saraswathy S, Kavitha PS, Jegadeeswari V. Precision to plate: AI-driven innovations in fermentation and hyper-personalized diets. Front Nutr, 2025, 12: 1659511.

[62] Ray RC, Paramithiotis S, Thekkangil A, Nethravathy V, Rai AK, Martin JGP. Food fermentation and its relevance in the human history. Trending Topics on Fermented Foods, 2024. Cham, Springer: 1-57.

[63] Riezzo L, Kay H, Feng Y, Jing K, Zhang D. Accelerating bioprocess digital twin development by integrating hybrid modelling with transfer learning. Chem Eng J, 2025, 511: 162018.

[64] Sakaguchi T, Irifune Y, Kamada R, Sakaguchi K. Bacterial systematic genetics and integrated multi-omics: Beyond static genomics toward predictive models. Int J Mol Sci, 2025, 26(19): 9326.

[65] Shariatifar M, Rizi MS, Sotudeh-Gharebagh R, Zarghami R, Mostoufi N. On digital twins in bioprocessing: Opportunities and limitations. Process Biochem, 2025, 156: 274-99.

[66] Sharma R, Garg P, Kumar P, Bhatia SK, Kulshrestha S. Microbial fermentation and its role in quality improvement of fermented foods. Fermentation, 2020, 6(4): 106.

[67] Shimomura Y, Nemoto Y, Kimita K. A method for analysing conceptual design process of product-service systems. CIRP Ann, 2015, 64(1): 145-8.

[68] Siddiqui SA, Erol Z, Rugji J, Taşçı F, Kahraman HA, Toppi V, Musa L, Di Giacinto G, Bahmid NA, Mehdizadeh M, Castro-Muñoz R. An overview of fermentation in the food industry: Looking back from a new perspective. Bioresources Bioprocess, 2023, 10(1): 85.

[69] Singh A, Kumar S. Exploring the functionality of microbes in fermented foods: Technological advancements and future directions. Fermentation, 2025, 11(6): 300.

[70] Sinner P, Daume S, Herwig C, Kager J. Usage of Digital Twins Along a Typical Process Development Cycle. In: Herwig C, Pörtner R, Möller J, editors. Digital Twins. Volume 176. Springer International Publishing; 2020. pp. 71–96. https://doi.org/10.1007/10_2020_149.

[71] Sivakumar M, Maranco M, Krishnaraj N, Reddy US. Data analytics and visualization in smart manufacturing using AI-based Digital Twins. In A. K. Tyagi, S. Tiwari, S. K. Arumugam, & A. K. Sharma, editors, Artificial Intelligence‐Enabled Digital Twin for Smart Manufacturing (1st ed., pp. 249–277). Wiley; 2024. https://doi.org/10.1002/9781394303601.ch12

[72] Sørensen HM, Cunningham D, Balakrishnan R, Maye S, MacLeod G, Brabazon D, Loscher C, Freeland B. Steps toward a digital twin for functional food production with increased health benefits. Curr Res Food Sci, 2023, 7: 100593.

[73] Steindl G, Kastner W. Semantic microservice framework for Digital Twins. Appl Sci, 2021, 11(12): 5633.

[74] Tancredi GP, Vignali G, Bottani E. Integration of Digital Twin, machine-learning and industry 4.0 tools for anomaly detection: An application to a food plant. Sensors, 2022, 22(11): 4143.

[75] Thelen A, Zhang X, Fink O, Lu Y, Ghosh S, Youn BD, Todd MD, Mahadevan S, Hu C, Hu Z. A comprehensive review of digital twin — part 1: Modeling and twinning enabling technologies. Struct Multidisciplinary Optim, 2022, 65(12): 354.

[76] The PAT pathway to scalable and sustainable precision fermentation, Hamilton company. (2024). https://processanalytics.hamiltoncompany.com/hubfs/00%20%20Process%20Analytics/Documents%20%28PA%29/PDFs/White-Papers/11100988500_Whitepaper_PAT_PrecisionFermentation_LR.pdf

[77] Tiso T, Demling P, Karmainski T, Oraby A, Eiken J, Liu L, Bongartz P, Wessling M, Desmond P, Schmitz S, Weiser S, Emde F, Czech H, Merz J, Zibek S, Blank LM, Regestein L. Foam control in biotechnological processes: Challenges and opportunities. Discover Chem Eng, 2024, 4(1): 2.

[78] T SK, Saranyadevi S, Abdullahi A, Navina B. Microbial fermentation profiling: Tools and techniques for food analysis. In T. Sarkar, editor, Novel Food Analysis. Springer, US; 2025. pp. 295–320. https://doi.org/10.1007/978-1-0716-4787-5_14

[79] Udugama IA, Lopez PC, Gargalo CL, Li X, Bayer C, Gernaey KV. Digital Twin in biomanufacturing: Challenges and opportunities towards its implementation. Syst Microbiol Biomanufacturing, 2021, 1(3): 257-74.

[80] Vera-Guerrero D, Mendoza LM, Lara GB, Vallejo DL, Valenzuela-Cobos A, Domínguez-Brito L, Fiallos-Cárdenas M. Sustainable kombucha production: A conceptual framework integrating life cycle assessment and circular economy principles. Trends Food Sci Technol, 2025, 159: 104996.

[81] Wang B, Liu J, Yu A, Wang H. Development and optimization of a novel soft sensor modeling method for fermentation process of pichia pastoris. Sensors, 2023, 23(13): 6014.

[82] Wang K, Zhao W, Lin L, Wang T, Wei P, Ledesma-Amaro R, Zhang A, Ji X. A robust soft sensor based on artificial neural network for monitoring microbial lipid fermentation processes using Yarrowia lipolytica. Biotechnol Bioeng, 2023, 120(4): 1015-25.

[83] Wang Y, Wang Y, Xu F. Artificial intelligence-driven fermentation optimization for α-amylase hyperproduction enabled by Raman monitoring and metabolic network analysis. Bioresour Technol, 2026, 439: 133287.

[84] West S, Stoll O, Meierhofer J, Züst S. Digital Twin providing new opportunities for value co-creation through supporting decision-making. Appl Sci, 2021, 11(9): 3750.

[85] Willa V, Nyambo DG, Sam A. Brix and alcohol content monitoring using wireless sensor network. Int J Adv Sci Res Eng, 2021.

[86] WINE-PRO Project. Digital twin for wine fermentation process optimisation. 2023. https://ss4af.com/en/highfive/projects/wine-pro-digital-twin-wine-fermentation-process-optimisation

[87] Wu J, Wu C-C, Liao C-S. Novel Lactobacillus Fermentation Prediction Using Deep Learning. 2021 7th International Conference on Applied System Innovation (ICASI). 2021. pp. 54–57. https://doi.org/10.1109/ICASI52993.2021.9568412

[88] Yan Y, Zou M, Tang C, Ao H, He L, Qiu S, Li C. The insights into sour flavor and organic acids in alcoholic beverages. Food Chem, 2024, 460: 140676.

[89] Yee CS, Zahia-Azizan NA, Abd Rahim MH, Mohd Zaini NA, Raja-Razali RB, Ushidee-Radzi MA, Ilham Z, Wan-Mohtar WAAQI. Smart fermentation technologies: Microbial process control in traditional fermented foods. Fermentation, 2025, 11(6): 323.

[90] Zhang Y, Zhou K, Chen X, Zhang H, Han J, Ning K. MicroProphet: A Digital Twin framework for predicting microbial community dynamics with personalized precision. Microbiology, 2025.

[91] Zhao B, Li X, Sun W, Qian J, Liu J, Gao M, Guan X, Ma Z, Li J. BioDT: An integrated Digital-Twin-based framework for intelligent biomanufacturing. Processes, 2023, 11(4): 1213.

[92] Zhao S, Adade SY-SS, Wang Z, Jiao T, Ouyang Q, Li H, Chen Q. Deep learning and feature reconstruction assisted vis-NIR calibration method for on-line monitoring of key growth indicators during kombucha production. Food Chem, 2025, 463: 141411.

[93] Zhao S, Jiao T, Adade SY-SS, Wang Z, Ouyang Q, Chen Q. Digital twin for predicting and controlling food fermentation: A case study of kombucha fermentation. J Food Eng, 2025, 393: 112467.

[94] Zhu X, Rehman KU, Wang B, Shahzad M. Modern soft-sensing modeling methods for fermentation processes. Sensors, 2020, 20(6): 1771.

RIGHTS & PERMISSIONS

Jiangnan University