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

ISSN: 2594-1925

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: https://orcid.org/0000-0003-1678-4850.

Received: August 13, 2025 Accepted: October 10, 2025 Published: November 2, 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 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.

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

Revista de Ciencias Tecnológicas (RECIT). Volumen 8 (4): e423.

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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

comprehensive literature review defining the

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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

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.

Figure 3. Quantity of documents by subject within the intersection of fermentation and AI (Scopus).

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

10.7

Biochemistry Molecular Biology

6.4

Environmental Sciences

6.1

Computer Science Artificial Intelligence

4.9

Microbiology

4.7

Biochemical Research Methods

4.6

Engineering Environmental

4.4

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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).

Funding Agencies

Record Count

National Natural Science Foundation of China NSFC

14.3

National Key Research Development Program of China

3.8

Fundamental Research Funds for The Central Universities

2.5

UK Research Innovation UKRI

2.2

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Capes

1.8

Conselho Nacional De Desenvolvimento Científico e Tecnológico CNPQ

1.6

Natural Science Foundation of Jiangsu Province

1.6

National Key R D Program of China

1.5

Natural Science Foundation of Shandong Province

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.

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.

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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.

Jiangsu University

Council of Scientific Industrial Research.

CSIR India.

Chinese Academy of Sciences.

Jiangnan University.

University of California Systems.

National Institute of Technology. NIT

system.

University of California Davis.

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

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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).

103

118

132

143

153

COUNT OF ARTICLES

Revista de Ciencias Tecnológicas (RECIT). Volumen 8 (4): e423.

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.

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.

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Carlos Mateos Díaz

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