Revista de Ciencias Tecnológicas (RECIT). Volumen 3 (1): 10-22
Revista de Ciencias Tecnológicas (RECIT). Universidad Autónoma de Baja California ISSN 2594-1925
Volumen 8 (4): e423. Octubre-Diciembre, 2025. hps://doi.org/10.37636/recit.v8n4e423
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Review article
Artificial intelligence and fermentation: applications,
publications and trend analysis
Inteligencia artificial y fermentación: aplicaciones, artículos de
investigación y análisis de tendencias
Hugo César Enríquez García1* , Fernando de Jesús Salcedo Medina2, Juan Carlos Mateos Díaz1
1Department 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.
2University of Guadalajara, Tonalá University Center (CUTONALA), Nuevo Periférico Avenue No. 555, Ejido San José
Tateposco, Zip code. 45425, Tonalá, Jalisco, Mexico.
Corresponding author: 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. E-mail:
huenriquez_pos@ciatej.edu.mx. ORCID: 0000-0003-1678-4850.
Received: August 13, 2025 Accepted: October 10, 2025 Published: November 3, 2025
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 https://recit.uabc.mx/index.php/revista/article/view/423publication 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.
Keywords: Artificial Intelligence; Fermentation; Deep learning; Machine learning; Precision fermentation; Forecast.
Resumen. - La inteligencia artificial (IA) es una fuerza transformadora en diversos sectores industriales, y los procesos de
fermentación están siendo cada vez más optimizados mediante su aplicación en la producción de alimentos, productos
farmacéuticos, químicos y biocombustibles. Esta investigación tiene como objetivo realizar un análisis de publicaciones y
proyecciones para esclarecer la relación sinérgica entre la IA y la fermentación dentro del contexto más amplio de esta
intersección del conocimiento. Se llevó a cabo un análisis exhaustivo de publicaciones utilizando las bases de datos Web of
Science (WoS) y Scopus, con el fin de caracterizar las autorías más relevantes, la distribución geográfica y las principales
afiliaciones institucionales, así como cuantificar la producción científica asociada a la intersección entre IA y fermentación.
Además, se realizó una previsión de series temporales utilizando el método de suavizamiento exponencial triple (TES), con el
fin de predecir las tendencias de publicaciones hasta el año 2030. También se llevó a cabo una revisión bibliográfica integral
sobre diversas aplicaciones recientes en el ámbito de la IA y la fermentación. Este estudio contribuye al esclarecer las
tendencias globales en la aplicación y en la investigación de alto impacto que caracterizan esta intersección específica del
conocimiento. Nuestros hallazgos indican una trayectoria de crecimiento sustancial y sostenido en la producción científica y
el impacto de citaciones relacionados con la convergencia entre la IA y la fermentación. Se proyecta que esta tendencia
continuará hasta 2030, lo que representa un crecimiento estimado del 48% entre 2024 y 2030. China e India se posicionan
como los principales contribuyentes y financiadores en este campo. La categoría “Biotecnología y Microbiología Aplicada”
constituye aproximadamente un tercio de los artículos publicados en la base de datos WoS, mientras que “Ingeniería Química”,
“Bioquímica” e “Ingeniería” representan la mayor cantidad de artículos publicados en la base de datos Scopus.
Palabras clave: Inteligencia artificial; Fermentación; Aprendizaje profundo; Aprendizaje automático; Fermentación en
precisión; Pronóstico.
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1. Introduction
Artificial intelligence (AI) is emerging as a
transformative force in both social and economic
landscapes. While the full extent of its impact on
the global landscape remains to be elucidated, its
adoption across diverse industries is accelerating.
AI offers significant advantages to industries,
including automation, predictive capabilities,
enhanced process control, and optimized
efficiency. Significantly, the field of academic
research has embraced AI as a tool to address
global challenges.
Conversely, fermentation, a well-established
technique in diverse industries, continues to
evolve. Its applications encompass a vast array of
products, and the fermentation processes
themselves are undergoing advancements toward
greater innovation and sophistication.
The use of AI has increasingly optimized
fermentation processes. This is exemplified by
successful implementations in white wine
fermentation [1]. Researchers introduced a
digitalization solution for white wine
fermentation, making the process compatible
with advanced control systems. The core method
uses a genetic algorithm-optimized and a neural
network to predict alcohol and substrate
concentrations based on initial fermentation
settings. Another example was seen in ethanol
fermentation [2], which was complex. Using cell
morphological data, AI models were developed
to forecast ethanol yields in yeast fermentation. A
neural network proved highly effective,
maintaining an R2 exceeding 0.9.
Furthermore, another application was in
Lactobacillus fermentation. Current manual
fermentation methods are plagued by uncertainty
and human error, often leading to losses of
millions of dollars. To mitigate this risk and
improve efficiency, this initiative proposes a
solution to digitalize the complex process by
integrating AI within the context of lactic acid
bacteria cultivation [3].
Finally, AI was implemented in fermentative
biohydrogen production. Biohydrogen
production from organic waste is an
environmentally sustainable process, yet its lack
of predictability due to biological complexity
currently obstructs industrial scale-up.
Researchers utilized Machine Learning (ML) as
a technological solution to enhance the
predictability and reliability of this technology
[4].
Motivated by this growing trend of research, our
objective is to investigate via a publication
analysis the evolutionary trajectory of the
synergy between fermentation processes and
artificial intelligence techniques within the
period from 2000 to mid-2024. Furthermore, we
aim to generate extrapolations using select
metrics to provide a preliminary forecast for the
year 2030. This inquiry is motivated by the
expanding scope of applications in both
fermentation and artificial intelligence.
Therefore, our research questions are:
• How much has research on the use of AI in
fermentation grown?
• How has this intersection (fermentation and AI)
evolved in terms of publications—citations,
authors, institutions/countries, and knowledge
categories?
• What will be the document output forecast for
2030?
Given the escalating interest in AI-related fields,
a substantial growth trend in the upcoming years
is anticipated.
Moreover, this research addresses a critical gap
in the existing literature by providing the first
systematic analysis of AI's potential within
fermentation processes. By examining diverse
applications and current publication trends, this
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work serves as an essential resource, encouraging
researchers in fields like biochemistry,
computational sciences, and biotechnology to
pursue new developments, patents, and
innovations in these converging topics.
This is our proposed research structure:
• Literature Review: A comprehensive review of
the existing literature relevant to the research
topic will be conducted.
• Methodology: The methodology will explain
the bibliometric analysis and forecast
application.
• Analysis of Results: A thorough analysis of the
outcomes obtained from both the publication
analysis and forecast application will be
undertaken.
• Conclusions: The research will culminate in
well-supported conclusions based on our
analyses.
2. Background, state of the art: fermentation
& artificial intelligence.
2.1 Fermentation techniques in diverse
industries.
Fermentation processes boast a remarkably
diverse array of industrial applications,
solidifying their position as a cornerstone
technology for advancing human well-being.
Fermentation is likewise considered a large-scale
cultivation process employing microorganisms
or their enzymatic machinery (particularly
bacteria, yeasts, molds, or fungi) to drive a
targeted bioconversion, resulting in the
production of a specific product [5]. Further,
industrialized fermentation implies rapid and
efficient production, maximizing yield and
consistency while minimizing complexity and
cost [6].
In food processes, traditional fermentation
represents one of the earliest documented food
processing techniques, with independent
emergence across various cultures dating back to
approximately 7000 BC [7]. This technique has
yielded novel food products and flavors,
including alcoholic beverages, bread, and
enhanced preservation methods. Fermented
foods harbor probiotic microorganisms,
conferring digestive and nutrient absorption
benefits on human health, preventing diseases
such as various pathologies—including type 2
diabetes mellitus and allergic reactions [8].
Moreover, fermented foods have been associated
with a wide range of other health advantages,
including reduced cholesterol concentrations,
enhanced immune function, protection against
infectious diseases, cancer, osteoporosis, obesity,
allergic reactions, and atherosclerosis [9].
Similarly, fermentation processes generate a
diverse array of bioderived chemical
intermediates for large-scale industrial
applications. These include commercially
relevant examples such as ethanol, n-butanol,
lactic acid, citric acid, and β-farnesene [10].
Additionally, fermentation holds promise for
producing diverse bio-based polymers and
lubricants.
Furthermore, the production of amino acids from
biomass-derived feedstocks has recently
achieved commercial viability or
pilot/demonstration scale feasibility. Notably,
these processes have primarily utilized first-
generation biomass feedstocks [10].
Fermentation has another valuable application to
mitigate climate change, namely gas
fermentation, which utilizes carbon-fixing
microorganisms and offers a recently
commercialized, economically feasible source of
clean energy [11].
A growing area of interest in the food
fermentation industry is "precision
fermentation," that involves cell-based food
production. This innovative method involves
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cultivating cells from animals, plants, or
microorganisms to create sustainable and cost-
efficient food. The products generated through
this process are typically similar in structure and
function to traditional animal-based foods,
including meat, plant-based meat, poultry,
seafood, dairy, and eggs [12].
2.2 Artificial Intelligence tech in diverse
industries
Alternatively, the field of AI encompasses a vast
spectrum of advanced technologies dedicated to
the exploration and implementation of machine
intelligence. Recent years have witnessed a surge
in activity within core computing subfields
relevant to AI, including multimedia, distributed
AI and multi-agent systems in open
environments, and knowledge mining [13].
AI improves the efficiency and automation in
diverse industries such as healthcare, finance,
transportation, entertainment, education,
cybersecurity, medicine, food, and
pharmaceuticals [14].
Additionally, Machine Learning (ML), a subfield
of AI, leverages a repertoire of mathematical and
linguistic algorithms. These algorithms have
demonstrably achieved a level of semantic
comprehension and information extraction that
closely resembles human capabilities. ML
models have exhibited the capacity to identify
abstract patterns with superior accuracy
compared to some human experts [15]. A
principal benefit of ML lies in its capacity for
automated execution of tasks after acquiring
knowledge from data [16].
AI, particularly through the integration of
computer vision and machine learning
algorithms, can significantly improve food safety
by enabling rapid and accurate detection of
contaminants [17]. ML algorithms can analyze
vast datasets, including historical records, sensor
data, and market trends, to improve demand
forecasting and inventory management. For
example, AI-driven demand forecasting can
significantly reduce food waste [18].
Another form of AI is Deep Learning (DL),
which is rapidly establishing itself as a
revolutionary technology across diverse
domains, demonstrably impacting fields such as
cancer diagnostics, personalized medicine,
autonomous vehicle navigation, and automatic
speech recognition [19]. Moreover, another high-
impact use, such as drug discovery and enhanced
natural disaster prediction, represents significant
DL advancements [20].
Both fraud detection and self-driving cars rely on
deep learning [21]. While deep learning identifies
fraud by analyzing various factors, in
autonomous vehicles, it simultaneously
identifies, analyzes, and reacts to multiple
elements [22].
Moreover, Artificial Neural Networks (ANN)
constitute a paradigm within ML, characterized
by interconnected processing units termed
artificial neurons. These networks are designed
as adept function approximators, capable of
establishing a precise mapping between input
data (x) and corresponding output (y) [23].
Deep Neural Networks (DNNs) represent an
evolution of ANNs, enabling them to achieve
superior levels of data abstraction and handle
greater complexity within the data they process
[24]. Some typical applications of these cutting-
edge techniques are image recognition,
recommender systems for users, and natural
language processing.
Having detailed various cross-industry
applications of AI, the primary focus of this
research now shifts to understanding how these
AI techniques are specifically applied within
fermentation industries (e.g., energy, food,
pharmaceutical, chemical, and biochemical).
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Analyzing these applications provides the
necessary context to gain insights into the
expected growth by 2030 and to quantify the
publication output at this intersection of
knowledge. Consequently, we next present
several examples of AI integration with
fermentation techniques.
2.3 Diverse applications of AI in Fermentation
Several studies have explored the impact of AI
technology, specifically digital twin and
knowledge graph applications, on enhancing
traditional fermentation processes [25]. This
work simultaneously highlights the challenges
associated with optimizing fermentation within
the context of synthetic biology. Furthermore,
recent advances in computer-aided chemical
engineering for precision fermentation have
emphasized the crucial role of Process Systems
Engineering (PSE) in these developments [26].
PSE, in particular, provides crucial tools for
designing and optimizing fermentation
processes.
AI technology is crucial for advancing precision
fermentation, an emerging field focused on
engineering "cell factories" (primarily yeast and
fungi) to produce customized molecules like
proteins [27].
Furthermore, sustaining ideal bioprocess
conditions depends on dynamically regulating
fermentation parameters. To achieve this,
Reinforcement Learning (RL), a branch of
Machine Learning (ML), has been applied to
build adaptive control methods [28].
Decision Trees, an AI technique, offer a clear
way to see how different variables relate to each
other. They're useful for pinpointing the key
factors that influence the taste, smell, and texture
of traditional fermented foods [29]. In a recent
study, researchers found that their AI model
could accurately forecast ethanol production [2].
The model predicted ethanol production at a
given time and at 60 minutes with considerable
accuracy, achieving a coefficient of
determination (R2) greater than 0.9.
Likewise, future models, by integrating AI
technologies with cutting-edge genomic data,
will be able to provide a clearer understanding of
the complex and overlapping traits found in
probiotic and non-probiotic bacterial genomes
[30].
Researchers found that Support Vector Machines
(SVMs) in combination with ANN were a
powerful tool for classification tasks essential for
quality control in brewing [31]. They're
especially good at handling complex, high-
dimensional data. This makes them ideal for
analyzing things like raw material quality (for
example, detecting defects in barley) or closely
monitoring different stages of fermentation.
An application of the production of L-
asparaginase by S. violaceoruber has been
explored, confirmed, and predicted through an
artificial neural network (ANN), which was
compared against Central Composite Design
(CCD) [32].
Finally, AI-powered machine learning is
generating significant interest because of its
wide-ranging uses in areas like bioprocess
engineering, biopharmaceutical fermentative
processes, and drug discovery [37].
3. Methodology
For the identification of relevant literature on the
synergy between fermentation and artificial
intelligence (AI), we primarily utilized the Core
Collection of the Web of Science (WoS) and
Scopus.
For the WoS and Scopus database investigations,
a search strategy was developed based on a
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comprehensive literature review defining the
relevant terms for AI and fermentation. The code
implemented is represented by the following
search formula:
fermentation OR "biomass fermentation" OR
"precision fermentation" AND "artificial
intelligence" OR "machine learning" OR "deep
learning" OR "data mining" OR "neural network"
Exclusion criteria:
✓ The "Abstract" field was chosen rather than the
"All Fields" search option due to the enhanced
specificity of the former. It was observed that
employing the "All Fields" option yielded
irrelevant results that did not align with the
defined search criteria (e.g., articles were
retrieved that contained only a segment described
as "Plus keywords" related to fermentation,
without addressing this topic).
✓ Publications prior to the year 2000 were
excluded from the present study.
✓ Our selection focused on "Articles" and
"Review articles" within the WoS and Scopus
platforms, thereby discarding "books," "book
chapters," "proceeding papers," and other
document types.
To facilitate comprehension of the article
selection process, we present a PRISMA-style
flow table below, which details the inclusion and
exclusion criteria.
Table 1: Systematic literature screening process and exclusion criteria (Web of Science and Scopus).
Stage
Step/ Criteria applied
Records filtered
Identification
WoS & Scopus core collection search: Initial search using the defined
query: fermentation OR "biomass fermentation" OR "precision
fermentation" AND "artificial intelligence" OR "machine learning" OR
"deep learning" OR "data mining" OR "neural network"
WoS: 1,571 records
found.
Scopus: 1,840 records
found.
Screening
Exclusion 1: The field of search was limited to only the "Abstract" field
for enhanced specificity (excluding irrelevant results from "All Fields").
223 Records excluded at WoS.
247 Records excluded from the Scopus website.
Exclusion 2: Time Period. Limited publication years from 2000 to 2024
(excluding articles published before 2000).
34 Records excluded at WoS
48 Records excluded from the Scopus website.
Exclusion 3: Document Type. Limited document types to only "Article"
and "Review Article" (excluding "Books," "Proceedings Papers," “book
chapters”, etc.
612 Records excluded at WoS
598 Records excluded from the Scopus website.
WoS: 1,348 records
found. Scopus: 1,593
records found.
WoS: 1,314 records
found. Scopus: 1,545
records found.
WoS: 736 records found.
Scopus: 995 records
found.
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Bibliometric indicators
Our research aims to determine the quantity of
publications and citations published between
2000 and 2024 concerning the AI and
Fermentation intersection. Moreover, we will
determine which affiliations and countries have
the greatest relevance in these fields, as well as
the authors within the WoS and Scopus
categories that publish most frequently.
Forecast method.
The Triple Exponential Smoothing (TES)
algorithm, synonymous with the Holt-Winters
method, will be implemented. This
methodological choice is underpinned by several
advantages:
Data Sparsity Accommodation: TES exhibits a
robust capacity to handle limited historical data
[33], aligning with the data constraints of this
analysis within the WoS categories from 2013-
2023 (article production).
Long-Term Forecasting Capability: This
method demonstrates efficacy in generating
projections across extended timeframes, enabling
extrapolation of article production to the year
2030.
The target variable for prediction is the “annual
publication count”, which will serve as a proxy
for assessing the ongoing growth trajectory
driven by the research interest in fermentation
and AI.
4. Results and analysis
Figure 1. Quantitative analysis of publications and citations spanning the period from 2000 to July 2024 (WoS).
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A positive correlation between publication and
citation counts has been observed since 2000.
While publication numbers exhibited
fluctuations with periods of growth and decline
from 2000 to 2018, a sustained upward trajectory
in both publications and citations has been
evident since 2019. Within the 2018-2023 period,
citation counts have experienced a 308%
increase, while a 415% growth has been observed
in publication output. As of mid-2024 (WoS), the
combined publication and citation output for the
entire year of 2020 has already been surpassed.
This growth in research output may be attributed
to
1. The combination of AI with fermentation
processes is a key factor in achieving Industry 4.0
standards in biomanufacturing, turning manual,
error-prone labs into fully automated, self-
optimizing "smart factories.”
2. Additionally, by leveraging historical
data, ML and DL algorithms can forecast product
yields well in advance of the fermentation
process being completed. This capability
provides essential lead time for streamlining
operational scheduling, maximizing resource
efficiency, and ensuring high-standard quality
assurance.
Figure 2. Quantity of publications by year, from 2000 to 2024 (Scopus).
The Scopus database similarly reflects a positive
trend, which became rapidly accentuated starting
in 2020. The total growth observed between 2020
and 2024 is 167%, as the document count
escalated from 59 to 158 over that period. This
result might be driven by artificial intelligence,
which has experienced significant growth,
largely thanks to the popularity of chatbots and
AI's ability to simplify, optimize, and improve
operations across many sectors.
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Table 2. Publication distribution across Web of Science categories from 2000 to July 2024.
The leading category is Biotechnology &
Applied Microbiology, a fascinating and rapidly
evolving scientific field that combines the
principles of microbiology (the study of
microscopic organisms like bacteria, fungi,
viruses, and protozoa) with biotechnology (the
application of biological organisms, systems, or
processes to create useful products or solve
problems).
Applications in this field are wide-ranging. For
instance, microbes help us produce life-saving
drugs (such as antibiotics), develop vaccines,
create diagnostic tests, and deliver genes for
therapy. They also contribute to gut health with
probiotics. In the food and beverage industry,
these techniques are essential for brewing beer,
making cheese, preserving food, and boosting
their nutritional value. In the second and third
positions are Chemical Engineering and Food
Science & Technology, respectively.
These top three categories collectively
encompass 70.7% of the total research output,
representing 462 published documents.
Web of Science Categories
Record Count
%
Biotechnology Applied Microbiology
217
33.2
Engineering Chemical
136
20.8
Food Science Technology
109
16.7
Energy Fuels
70
10.7
Biochemistry Molecular Biology
42
6.4
Environmental Sciences
40
6.1
Computer Science Artificial Intelligence
32
4.9
Microbiology
31
4.7
Biochemical Research Methods
30
4.6
Engineering Environmental
29
4.4
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Figure 3. Quantity of documents by subject within the intersection of fermentation and AI (Scopus).
Conversely, the Scopus database employs a
distinct subject classification. In this system,
Chemical Engineering emerges as the field with
the highest article production, accounting for
15.2% of the output. The Biochemistry, Genetics,
and Molecular Biology category follows closely
with 14.6%, and Engineering rounds out the top
three at 10.6%. These results may be attributed to
the optimization of processes. Industrial
bioreactors are non-linear, dynamic systems
where temperature, pH, aeration, and mixing
must be perfectly managed. AI and ML models
are trained to improve control.
Table 3. WoS top funding agencies worldwide at the intersection “Fermentation & AI” (period from 2000- July 2024).
Record Count
%
94
14.3
25
3.8
17
2.5
15
2.2
12
1.8
11
1.6
11
1.6
10
1.5
10
1.5
These are the organizations that provided
financial support for research projects at the
intersection of “Fermentation & AI”. These
agencies may be government-funded, private, or
a combination of both. Analysis of funding
sources provided by these agencies enables us to
identify financial drivers contributing to the
advancement of high-impact, cutting-edge
technologies within the socioeconomic
landscape.
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A substantial allocation of financial resources
dedicated to research within these categories
originates from China. Notably, the National
Natural Science Foundation of China (NSFC)
and the National Key Research and Development
Program of China collectively account for 18.1%
of the total funding.
Figure 4. Quantity of documents (2000-2024) by funding sponsor within the intersection of fermentation and AI (Scopus).
Similarly, the Scopus database reveals that
Chinese agencies are the predominant funders,
with the top three organizations (the same as the
WoS database) originating from that country.
This finding underscores China's significant
potential to invest in these innovative knowledge
techniques. In contrast to the WoS database
results, Indian funding agencies appear to be less
prominent here, appearing in eighth place.
Likewise, three agencies from Brazil appear as
the main funders.
Table 4. WoS top publication production by institution (period from 2000- July 2024).
Institution
Documents
Indian Institute of Technology Systems. IT
systems.
23
Jiangsu University
22
Council of Scientific Industrial Research.
CSIR India.
21
Chinese Academy of Sciences.
17
Jiangnan University.
17
University of California Systems.
13
National Institute of Technology. NIT
system.
13
University of California Davis.
10
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This section underscores the prominent role of
India in scientific production within the
intersection, as evidenced by the presence of two
Indian institutions within the top three. This
observation suggests India's advanced standing
in this field. Conversely, three Chinese
institutions occupy positions within the top five.
For the past twenty years, biotechnology has
been a top priority for China, and it is now seen
as a key driver for the nation's "new-quality
productive forces." The government has
consistently poured money into biotech research.
Interestingly, China's advancements in biotech
research and innovation have progressed so
rapidly that its capabilities now exceed its current
domestic needs in sectors like healthcare,
chemicals, energy, and agriculture.
Figure 5. Quantity of documents by affiliation (2000-2024) within the intersection of fermentation and AI (Scopus).
Figure 6. Article production by country with their respective co-citation nodes (period from 2000- July 2024).
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The institutional analysis shows minor deviations
compared to the WoS database. Notably, Indian
institutions are not featured among the top-
ranked affiliations in the Scopus database.
Instead, Northeastern University of
Massachusetts is observed to be present.
The VOSviewer software effectively illustrates
the geographic distribution of article production
within the intersection.
China, India, the United States, and England
emerge as leading contributors in this domain.
India has significant healthcare needs, and
biotechnology can provide affordable solutions,
including vaccines, biosimilars, and diagnostics.
Figure 7. Quantity of documents by country (2000-2024) within the intersection of fermentation and AI (Scopus).
The Scopus database reflects the same
publication pattern as the WoS data: the nations
demonstrating the highest scientific output in
terms of article peoduction in high-impact
journals are China, India, and the United States.
The parallel trajectory of publications and R&D
investments observed in this graph suggests that
China, India, and the US will likely sustain their
prominence in this research domain. Likewise,
these are some of the largest economies in the
world.
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Figure 8. Reference Co-Citation Analysis (Total link strength).
The Links and Total link strength metrics
quantify, respectively, the quantity and
cumulative weight of connections between a
given entity and others within the network [34].
For instance, in a co-authorship network, the
Links attribute represents the number of
collaborative relationships established by a
specific researcher with their peers.
Figure 8 depicts a network of authors categorized
into distinct collaborative clusters based on color
differentiation. The size of each circle
corresponds to the respective author's level of
contibution.
Figure 9. Forecast of published articles (2024-2030) count within the intersection Note: Data selected for forecast: from 2013
to 2023 (WoS).
8
14
11
8
7
9
12
14
14
13
14
16
20
12
15
16
21
29
20
32
43
64
86
103
95
91
99
118
132
143
153
COUNT OF ARTICLES
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ISSN: 2594-1925
To enhance forecast accuracy, the analysis period
was restricted to the years 2013 to 2023 to
leverage more recent data points. The graphical
representation reveals a linear growth trajectory
commencing in 2018. The projected publication
output for the year 2030 is 153 documents within
the fermentation and AI intersection,
representing a 48% increase relative to the 2023
data.
This outcome aligns with the anticipated growth
of the precision fermentation (PF) market, as
these techniques represent a significant
commercial and investment trend in the industry,
despite obstacles such as regulations [35].
Numerous companies have already successfully
engineered microorganisms to produce diverse
proteins. The global precision fermentation
market, valued at an estimated $4.01 billion in
2024, is expected to expand significantly. It's
projected to grow at a Compound Annual Growth
Rate (CAGR) of 43.5% from 2025 to 2030,
primarily driven by rising consumer demand for
products that are both sustainable and eco-
friendly [36].
The anticipated market growth and the increasing
use of advanced fermentation techniques for
products like meat and dairy are sparking greater
research interest globally, leading to more
publications on these subjects.
5. Conclusions
Artificial intelligence, a transformative
technology, is accelerating innovation across
numerous economic sectors, including the
fermentation industries. The convergence of
these knowledge domains has garnered
significant academic and research attention.
Despite notable advancements, substantial
untapped potential for development persists
within these two fields.
This study provides a valuable contribution to
knowledge by enhancing the understanding of
the synergy between AI and fermentation
techniques. Given the transformative impact of
artificial intelligence on modern analysis and
interpretation, this research was necessary to
assist researchers, stakeholders, industries, and
students in grasping the current trends and
developing novel methodologies. This work fills
a notable gap in the literature, as comparable
comprehensive analyses for this specific
intersection are currently unavailable,
positioning this study as a pioneering effort.
After the execution of our analysis and
forecasting methodologies, these key insights are
presented:
The synergistic potential of these distinct
technologies to drive the evolution of more
sophisticated R&D methodologies within the
fermentation domain is anticipated in the near
future. Precision fermentation and biomass
fermentation, as contemporary techniques,
exhibit substantial promise in terms of social and
economic impact, aligning with the paradigm of
Fermentation Industry 4.0. Consequently,
sustained investment in these areas by
universities and public and private funding
entities is imperative.
Our forecast projects an upward
trajectory in global publication output,
potentially indicative of expanding R&D
activities and technological progress at the
intersection.
Authors from China and India emerge as
dominant contributors to this knowledge
intersection, with Brazilians exhibiting a notable
presence. The collective influence of the BRICS
bloc is evident in this domain. Concurrently, the
United States, particularly through some of its
universities, demonstrates a moderate growth
trajectory in publication output.
Revista de Ciencias Tecnológicas (RECIT). Volumen 8 (4): e423.
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ISSN: 2594-1925
This research provides valuable insights
for funding agencies, industry stakeholders, and
researchers seeking to comprehend the
evolutionary trajectory of AI and fermentation
and how it has evolved recently, assisting the
development of technologies. Additionally, the
findings offer a strategic roadmap for
biotechnology and AI programming students
aspiring to navigate the emerging trends
anticipated for 2030.
6. Authors acknowledgement
Hugo César Enríquez García: Project
administration, investigation, methodology, data
analysis, software, writing, review. Fernando de
Jesús Salcedo Medina: Data curation,
methodology, supervision and resources. Juan
Carlos Mateos Diaz: Conceptualization,
validation, review and editing.
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.
Revista de Ciencias Tecnológicas (RECIT). Volumen 8 (4): e423.
17
ISSN: 2594-1925
[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://internationalbusinessconfe
rence.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/a4
21ac9a-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=0
D9OEAAAQBAJ&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.
Revista de Ciencias Tecnológicas (RECIT). Volumen 8 (4): e423.
18
ISSN: 2594-1925
[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/m
aterial/Introduccion_a_los_Modelos_de_Pronost
icos.pdf
[34] N. Van Eck and L. Waltman, “VOS viewer
manual,” 2022. Accessed: [Online]. URL:
https://www.vosviewer.com/documentation/Man
ual_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.
Derechos de Autor (c) 2025 Hugo César Enríquez García, Fernando de Jesús Salcedo Medina, Juan
Carlos Mateos Díaz
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