Artificial intelligence and fermentation: applications, publications and trend analysis

Hugo César Enríquez García; Fernando de Jesús Salcedo Medina; Juan Carlos Mateo Díaz

doi:10.37636/recit.v8n4e423

Authors

Hugo César Enríquez García Department of industrial biotechnology. Research Center and Assistance in Technology and Design of Jalisco´s State (CIATEJ). 800 normalistas avenue, Guadalajara, Jalisco, Zip code 44270, Mexico. https://orcid.org/0000-0003-1678-4850
Fernando de Jesús Salcedo Medina University of Guadalajara, Tonalá University Center (CUTONALA), Nuevo Periférico Avenue No. 555, Ejido San José Tateposco, Zip code. 45425, Tonalá, Jalisco, Mexico. https://orcid.org/0009-0005-9208-4313
Juan Carlos Mateos Díaz Department of industrial biotechnology. Research Center and Assistance in Technology and Design of Jalisco´s State (CIATEJ). 800 normalistas avenue, Guadalajara, Jalisco, Zip code 44270, Mexico. https://orcid.org/0000-0002-6723-6654

DOI:

https://doi.org/10.37636/recit.v8n4e423

Keywords:

Artificial intelligence (AI), Fermentation, Deep learning, Machine learning, Precision fermentation, Forecast

Abstract

Artificial intelligence (AI) is a transformative force across diverse industrial sectors, and fermentation processes are increasingly being optimized through its application in food, pharmaceutical, chemical, and biofuel production. This research aims to conduct a forecasting and publication analysis to elucidate the synergistic relationship between AI and fermentation within the broader context of this knowledge intersection. A comprehensive publication analysis was performed using the Web of Science (WoS) and Scopus databases to characterize the most significant authorship, geographic distribution, and major institutional affiliations, as well as to quantify the research output associated with the intersection of AI and fermentation. In addition, time series forecasting was performed, using triple exponential smoothing (TES), to predict publication trends up to 2030. Furthermore, a comprehensive literature review of diverse recent applications within AI and fermentation was conducted. This study contributes by elucidating global trends in the application of and high-impact research that characterize this specific knowledge intersection. Our findings indicate a substantial and sustained growth trajectory in research output and citation impact related to the convergence of AI and fermentation. This trend is projected to persist until 2030, representing a 48% projected growth from 2024–2030. China and India emerged as leading contributors and financiers in this field. The “Biotechnology & Applied Microbiology” category constitutes approximately one-third of the published articles in the WoS database, while “Chemical Engineering,” “Biochemistry,” and “Engineering” account for the greatest quantity of published articles in the Scopus database.

Downloads

Download data is not yet available.

References

[1] A. Florea, A. Sipos, and M. C. Stoisor, “Applying AI Tools for Modeling, Predicting, and Managing the White Wine Fermentation Process,” Fermentation, vol. 8, no. 4, p. 137, 2022. doi: 10.3390/fermentation8040137.

[2] K. Itto-Nakama et al., “AI-based forecasting of ethanol fermentation using yeast morphological data,” Bioscience, Biotechnology, and Biochemistry, vol. 86, no. 1, pp. 125–134, 2022. doi: 10.1093/bbb/zbab188.

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

[4] A. K. Pandey et al., “Machine learning in fermentative biohydrogen production: advantages, challenges, and applications,” Bioresource technology, vol. 370, p. 128502, 2023. doi: 10.1016/j.biortech.2022.128502.

[5] L. Mazzeo and V. Piemonte, “Fermentation and biochemical engineering: principles and applications,” in Studies in Surface Science and Catalysis, vol. 179, Elsevier, 2020, pp. 261–285. doi: 10.1016/b978-0-444-64337-7.00015-x.

[6] T. Keshavarz, “Fermentation- Industrial Control of Fermentation Conditions,” in Encyclopedia of Food Microbiology (Second Edition), Academic Press, 2014, pp. 762–768. doi: 10.1016/B978-0-12-384730-0.00108-7.

[7] J. L. Legras, D. Merdinoglu, J. M. Cornuet, and F. Karst, “Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history,” Molecular ecology, vol. 16, no. 10, pp. 2091–2102, 2007. doi: 10.1111/j.1365-294X.2007.03266.x.

[8] S. A. Siddiqui et al., “An overview of fermentation in the food industry-looking back from a new perspective,” Bioresour. Bioprocess., vol. 10, no. 1, p. 85, 2023. doi: 10.1186/s40643-023-00702-y.

[9] J. P. Tamang and N. Thapa, “Beneficial Microbiota in Ethnic Fermented Foods and Beverages”, in Good Microbes in Medicine, Food Production, Biotechnology, Bioremediation, and Agriculture, F. J. de Bruijn, H. Smidt, L. S. Cocolin, M. Sauer, D. Dowling, and L. Thomashow, Eds. Wiley, 2022, pp. 130–148. DOI: 10.1002/9781119762621.ch11.

[10] T. A. Ewing et al., “Fermentation for the production of biobased chemicals in a circular economy: a perspective for the period 2022–2050,” Green Chemistry, vol. 24, no. 17, pp. 6373–6405, 2022. doi: 10.1039/d1gc04758b.

[11] N. Fackler et al., “Stepping on the gas to a circular economy: accelerating development of carbon-negative chemical production from gas fermentation,” Annu. Rev. Chem. Biomol. Eng., vol. 12, no. 1, pp. 439–470, 2021. doi: 10.1146/annurev-chembioeng-120120-021122.

[12] FAO, “Cell-based food and precision fermentation,” 2024. Accessed: July 2024. [Online]. Available: https://www.fao.org/food-safety/scientific-advice/asuntos-transversales-y-emergentes/cell-based-food/es/

[13] L. Zhang, J. Ling, and M. Lin, “Artificial intelligence in renewable energy: A comprehensive bibliometric analysis,” Energy Reports, vol. 8, pp. 14072–14088, 2022. doi: 10.1016/j.egyr.2022.10.347.

[14] L. Espina-Romero et al., “Which Industrial Sectors Are Affected by Artificial Intelligence? A Bibliometric Analysis of Trends and Perspectives,” Sustainability, vol. 15, no. 16, Art. no. 16, 2023. doi: 10.3390/su151612176.

[15] J. A. Nichols, H. W. Herbert Chan, and M. A. Baker, “Machine learning: applications of artificial intelligence to imaging and diagnosis,” Biophysical reviews, vol. 11, pp. 111–118, 2019. doi: 10.1007/s12551-018-0449-9.

[16] B. Mahesh, “Machine learning algorithms-a review,” Int. J. Sci. Res. (IJSR), vol. 9, no. 1, pp. 381–386, 2020. doi: 10.21275/ART20203995.

[17] L. Ma, M. Earles, N. Wisuthiphaet, J. Yi, and N. Nitin, “Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging,” Applied and Environmental Microbiology, 2023. doi: 10.1128/aem.01828-22.

[18] L. Munyanyi, “The Integration of Artificial Intelligence in Optimizing Food Supply Chain Management: Opportunities, Challenges, and Implications,” presented at the International Business Conference, 2024. [Online]. Available: at:https://internationalbusinessconference.com/wp-content/uploads/2024/10/CP177-Munyanyi-Integration-of-Artifical-Intelligence-final-corrected.pdf](https://internationalbusinessconference.com/wp-content/uploads/2024/10/CP177-Munyanyi-Integration-of-Artifical-Intelligence-final-corrected.pdf.

[19] A. Shrestha and A. Mahmood, “Review of deep learning algorithms and architectures,” IEEE Access, vol. 7, pp. 53040–53065, 2019. doi: 10.1109/ACCESS.2019.2912200.

[20] S. Nevo et al., “ML for flood forecasting at scale,” arXiv preprint arXiv:1901.09583, 2019. doi: 10.48550/arXiv.1901.09583.

[21] S. A. Alsheibani, Y. Cheung, C. Messom, and M. Ahosni, “Winning AI Strategy: Six-Steps to Create Value from Artificial Intelligence,” in AMCIS 2020 Proceedings, 2020, pp. 1–10. Accessed: [Online]. Available: https://core.ac.uk/download/pdf/326836031.pdf

[22] S. Alsheibani, C. Messom, and Y. Cheung, “Re-thinking the competitive landscape of artificial intelligence,” 2020. Accessed: [Online]. Available: https://scholarspace.manoa.hawaii.edu/items/a421ac9a-765f-457d-98af-f70eb1767810

[23] C. Xiao and J. Sun, “Deep Neural Networks (DNN),” in Introduction to Deep Learning for Healthcare, Cham: Springer International Publishing, 2021, pp. 41–61. Accessed: [Online]. Available: https://play.google.com/store/books/details?id=0D9OEAAAQBAJ&source=gbs_api.

[24] A. Masood and K. Ahmad, “A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance,” J. Clean. Prod., vol. 322, p. 129072, 2021. doi: 10.1016/j.jclepro.2021.129072.

[25] J. Xia, J. Liu, and Y. Zhuang, “Opportunities and challenges for fermentation optimization and scale-up technology in the artificial intelligence era,” Sheng wu Gong Cheng xue bao= Chinese Journal of Biotechnology, vol. 38, no. 11, pp. 4180–4199, 2022.

[26] T. Vinestock, M. Short, K. Ward, and M. Guo, “Computer-aided chemical engineering research advances in precision fermentation,” Current Opinion in Food Science, vol. 58, Art. no. 101196, Jul. 2024. DOI: 10.1016/j.cofs.2024.101196.

[27] A. Amore and S. Philip, “Artificial intelligence in food biotechnology: trends and perspectives,” Front. Ind. Microbiol., vol. 1, p. 1255505, 2023. doi: 10.3389/finmi.2023.1255505.

[28] R. Nian, J. Liu, and B. Huang, “A review on reinforcement learning: Introduction and applications in industrial process control,” Computers and Chemical Engineering, vol. 139, Art. no. 106886, Aug. 2020. DOI: 10.1016/j.compchemeng.2020.106886.

[29] C. S. Yee et al., “Smart Fermentation Technologies: Microbial Process Control in Traditional Fermented Foods,” Fermentation, vol. 11, no. 6, Art. no. 323, Jun. 2025. DOI: 10.3390/fermentation11060323.

[30] R. Asar et al., “Understanding the Functionality of Probiotics on the Edge of Artificial Intelligence (AI) Era,” Fermentation, vol. 11, no. 5, p. 259, 2025. doi: 10.3390/fermentation11050259.

[31] P. Nettesheim, P. Burggräf, and F. Steinberg, “Applications of machine learning in the brewing process: a systematic review,” Discover Artificial Intelligence, vol. 4, no. 1, p. 80, 2024. doi: 10.1007/s44163-024-00177-6.

[32] N. E. A. El-Naggar, R. A. Hamouda, and N. Elshafey, “Artificial intelligence-based optimization for extracellular L-glutaminase free L-asparaginase production by Streptomyces violaceoruber under solid state fermentation conditions,” Scientific Reports, vol. 14, no. 1, p. 29625, 2024. doi: 10.1038/s41598-024-77867-9.

[33] F. Villarreal, Introducción a los Modelos de Pronósticos. Univ. Nac. del Sur, 2016, pp. 1–121.URL: https://www.matematica.uns.edu.ar/uma2016/material/Introduccion_a_los_Modelos_de_Pronosticos.pdf

[34] N. Van Eck and L. Waltman, “VOS viewer manual,” 2022. Accessed: [Online]. URL: https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.18.pdf

[35] A. A. Pereira et al., “Precision fermentation in the realm of microbial protein production: State-of-the-art and future insights,” Food Research International, vol. 115527, 2024. doi: 10.1016/j.foodres.2024.115527.

[36] Grand View Report, “Precision Fermentation Market Size | Industry Report 2025-2030,” 2024. Accessed: [Online]. Available: https://www.grandviewresearch.com/industry-analysis/precision-fermentation-market-report

[37] S. Wainaina and M. J. Taherzadeh, “Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review,” Bioresource Technology, vol. 369, p. 128421, Feb. 2023. https://doi.org/10.1016/j.biortech.2022.12842.