<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2594-1925</journal-id>
<journal-title><![CDATA[Revista de ciencias tecnológicas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. cienc. tecnol.]]></abbrev-journal-title>
<issn>2594-1925</issn>
<publisher>
<publisher-name><![CDATA[Universidad Autónoma de Baja California]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2594-19252025000400202</article-id>
<article-id pub-id-type="doi">10.37636/recit.v8n4e423</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Artificial intelligence and fermentation: applications, publications and trend analysis]]></article-title>
<article-title xml:lang="es"><![CDATA[Inteligencia artificial y fermentación: aplicaciones, artículos de investigación y análisis de tendencias]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Enríquez-García]]></surname>
<given-names><![CDATA[Hugo César]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Salcedo-Medina]]></surname>
<given-names><![CDATA[Fernando de Jesús]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mateos-Díaz]]></surname>
<given-names><![CDATA[Juan Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Department of industrial biotechnology Research Center and Assistance in Technology and Design of Jalisco´s State (CIATEJ) ]]></institution>
<addr-line><![CDATA[Guadalajara Jalisco]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,University of Guadalajara  ]]></institution>
<addr-line><![CDATA[ Jalisco]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>8</volume>
<numero>4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2594-19252025000400202&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2594-19252025000400202&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2594-19252025000400202&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[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 &#8220;Biotechnology &amp; Applied Microbiology&#8221; category constitutes approximately one-third of the published articles in the WoS database, while &#8220;Chemical Engineering,&#8221; &#8220;Biochemistry,&#8221;and &#8220;Engineering&#8221; account for the greatest quantity of published articles in the Scopus database.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[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 &#8220;Biotecnología y Microbiología Aplicada&#8221; constituye aproximadamente un tercio de los artículos publicados en la base de datos WoS, mientras que &#8220;Ingeniería Química&#8221;, &#8220;Bioquímica&#8221; e &#8220;Ingeniería&#8221; representan la mayor cantidad de artículos publicados en la base de datos Scopus.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Artificial Intelligence]]></kwd>
<kwd lng="en"><![CDATA[Fermentation]]></kwd>
<kwd lng="en"><![CDATA[Deep learning]]></kwd>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
<kwd lng="en"><![CDATA[Precision fermentation]]></kwd>
<kwd lng="en"><![CDATA[Forecast]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[Fermentación]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[Fermentación en precisión]]></kwd>
<kwd lng="es"><![CDATA[Pronóstico]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>1</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Florea]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Sipos]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Stoisor]]></surname>
<given-names><![CDATA[M. C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Applying AI Tools for Modeling, Predicting and Managing the White Wine Fermentation Process,]]></article-title>
<source><![CDATA[Fermentation]]></source>
<year>2022</year>
<volume>8</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>137</page-range></nlm-citation>
</ref>
<ref id="B2">
<label>2</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Itto-Nakama]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[AI-based forecasting of ethanol fermentation using yeast morphological data,]]></article-title>
<source><![CDATA[Bioscience, Biotechnology, and Biochemistry]]></source>
<year>2022</year>
<volume>86</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>125-34</page-range></nlm-citation>
</ref>
<ref id="B3">
<label>3</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[C. C.]]></given-names>
</name>
<name>
<surname><![CDATA[Liao]]></surname>
<given-names><![CDATA[C. S.]]></given-names>
</name>
</person-group>
<source><![CDATA[Novel Lactobacillus Fermentation Prediction Using Deep Learning,]]></source>
<year>2021</year>
<conf-name><![CDATA[ 2021 7th International Conference on Applied System Innovation (ICASI)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>54-7</page-range></nlm-citation>
</ref>
<ref id="B4">
<label>4</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pandey]]></surname>
<given-names><![CDATA[A.K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning in fermentative biohydrogen production: advantages, challenges, and applications,]]></article-title>
<source><![CDATA[Bioresource technology]]></source>
<year>2023</year>
<volume>370</volume>
<page-range>128502</page-range></nlm-citation>
</ref>
<ref id="B5">
<label>5</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mazzeo]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Piemonte]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Fermentation and biochemical engineering: principles and applications,]]></article-title>
<source><![CDATA[Studies in Surface Science and Catalysis]]></source>
<year>2020</year>
<volume>179</volume>
<page-range>261-85</page-range><publisher-name><![CDATA[Elsevier]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B6">
<label>6</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Keshavarz]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Fermentation- industrial Control of Fermentation Conditions,]]></article-title>
<source><![CDATA[Encyclopedia of Food Microbiology]]></source>
<year>2014</year>
<edition>Second</edition>
<page-range>762-8</page-range><publisher-name><![CDATA[Academic Press]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B7">
<label>7</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Legras]]></surname>
<given-names><![CDATA[J.L.]]></given-names>
</name>
<name>
<surname><![CDATA[Merdinoglu]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Cornuet]]></surname>
<given-names><![CDATA[J.M.]]></given-names>
</name>
<name>
<surname><![CDATA[Karst]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history,]]></article-title>
<source><![CDATA[Molecular ecology]]></source>
<year>2007</year>
<volume>16</volume>
<numero>10</numero>
<issue>10</issue>
<page-range>2091-102</page-range></nlm-citation>
</ref>
<ref id="B8">
<label>8</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Siddiqui]]></surname>
<given-names><![CDATA[S.A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An overview of fermentation in the food industry-looking back from a new perspective,]]></article-title>
<source><![CDATA[Bioresour. Bioprocess.]]></source>
<year>2023</year>
<volume>10</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>85</page-range></nlm-citation>
</ref>
<ref id="B9">
<label>9</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tamang]]></surname>
<given-names><![CDATA[J.P.]]></given-names>
</name>
<name>
<surname><![CDATA[Thapa]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Beneficial microbiota in ethnic fermented foods and beverages,]]></article-title>
<person-group person-group-type="editor">
<name>
<surname><![CDATA[de-Bruijn]]></surname>
<given-names><![CDATA[F. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Smidt]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Cocolin]]></surname>
<given-names><![CDATA[L. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Sauer]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Dowling]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Thomashow]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<source><![CDATA[Good Microbes in Medicine, Food Production, Biotechnology, Bioremediation, and Agriculture]]></source>
<year>2022</year>
<page-range>130-48</page-range><publisher-name><![CDATA[Wiley]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B10">
<label>10</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ewing]]></surname>
<given-names><![CDATA[T.A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Fermentation for the production of biobased chemicals in a circular economy: a perspective for the period 2022- 2050]]></article-title>
<source><![CDATA[Green Chemistry]]></source>
<year>2022</year>
<volume>24</volume>
<numero>17</numero>
<issue>17</issue>
<page-range>6373-405</page-range></nlm-citation>
</ref>
<ref id="B11">
<label>11</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fackler]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Stepping on the gas to a circular economy: accelerating development of carbon-negative chemical production from gas fermentation,]]></article-title>
<source><![CDATA[Annu. Rev. Chem. Biomol. Eng.]]></source>
<year>2021</year>
<volume>12</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>439-70</page-range></nlm-citation>
</ref>
<ref id="B12">
<label>12</label><nlm-citation citation-type="">
<collab>FAO</collab>
<source><![CDATA[Cell-based food and precision fermentation]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B13">
<label>13</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Ling]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Lin]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial intelligence in renewable energy: A comprehensive bibliometric analysis,]]></article-title>
<source><![CDATA[Energy Reports]]></source>
<year>2022</year>
<volume>8</volume>
<page-range>14072-88</page-range></nlm-citation>
</ref>
<ref id="B14">
<label>14</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Espina-Romero]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Which Industrial Sectors Are Affected by Artificial Intelligence? A Bibliometric Analysis of Trends and Perspectives,]]></article-title>
<source><![CDATA[Sustainability]]></source>
<year>2023</year>
<volume>15</volume>
<numero>16</numero>
<issue>16</issue>
</nlm-citation>
</ref>
<ref id="B15">
<label>15</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Nichols]]></surname>
<given-names><![CDATA[J. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Herbert-Chan]]></surname>
<given-names><![CDATA[H. W.]]></given-names>
</name>
<name>
<surname><![CDATA[Baker]]></surname>
<given-names><![CDATA[M.A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning: applications of artificial intelligence to imaging and diagnosis,]]></article-title>
<source><![CDATA[Biophysical reviews]]></source>
<year>2019</year>
<volume>11</volume>
<page-range>111-8</page-range></nlm-citation>
</ref>
<ref id="B16">
<label>16</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mahesh]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning algorithms-a review]]></article-title>
<source><![CDATA[Int. J. Sci. Res. (IJSR)]]></source>
<year>2020</year>
<volume>9</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>381-6</page-range></nlm-citation>
</ref>
<ref id="B17">
<label>17</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Earles]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Wisuthiphaet]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Yi]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Nitin]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging,]]></article-title>
<source><![CDATA[Applied and Environmental Microbiology]]></source>
<year>2023</year>
</nlm-citation>
</ref>
<ref id="B18">
<label>18</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Munyanyi]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<source><![CDATA[The Integration of Artificial Intelligence in Optimizing Food Supply Chain Management: Opportunities, Challenges, and Implications,]]></source>
<year>2024</year>
<conf-name><![CDATA[ the International Business Conference]]></conf-name>
<conf-loc> </conf-loc>
</nlm-citation>
</ref>
<ref id="B19">
<label>19</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shrestha]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Mahmood]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Review of deep learning algorithms and architectures,]]></article-title>
<source><![CDATA[IEEE Access]]></source>
<year>2019</year>
<volume>7</volume>
<page-range>53040-65</page-range></nlm-citation>
</ref>
<ref id="B20">
<label>20</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Nevo]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[ML for flood forecasting at scale,]]></article-title>
<source><![CDATA[arXiv preprint arXiv]]></source>
<year>2019</year>
<volume>1901</volume>
<page-range>09583</page-range></nlm-citation>
</ref>
<ref id="B21">
<label>21</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alsheibani]]></surname>
<given-names><![CDATA[S.A.]]></given-names>
</name>
<name>
<surname><![CDATA[Cheung]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Messom]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Ahosni]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<source><![CDATA[Winning AI Strategy: Six-Steps to Create Value from Artificial Intelligence,]]></source>
<year>2020</year>
<page-range>1-10</page-range><publisher-name><![CDATA[AMCIS 2020 Proceedings]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B22">
<label>22</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alsheibani]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Messom]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Cheung]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<source><![CDATA[Re-thinking the competitive landscape of artificial intelligence,]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B23">
<label>23</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Xiao]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Sun]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deep Neural Networks (DNN),]]></article-title>
<source><![CDATA[Introduction to Deep Learning for Healthcare]]></source>
<year>2021</year>
<page-range>41-61</page-range><publisher-loc><![CDATA[Cham ]]></publisher-loc>
<publisher-name><![CDATA[Springer International Publishing]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B24">
<label>24</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Masood]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Ahmad]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance,]]></article-title>
<source><![CDATA[J. Clean. Prod.]]></source>
<year>2021</year>
<volume>322</volume>
<page-range>129072</page-range></nlm-citation>
</ref>
<ref id="B25">
<label>25</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Xia]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhuang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Opportunities and challenges for fermentation optimization and scale-up technology in the artificial intelligence era,]]></article-title>
<source><![CDATA[Sheng wu Gong Cheng xue bao= Chinese Journal of Biotechnology]]></source>
<year>2022</year>
<volume>38</volume>
<numero>11</numero>
<issue>11</issue>
<page-range>4180-99</page-range></nlm-citation>
</ref>
<ref id="B26">
<label>26</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Vinestock]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Short]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Ward]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Guo]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Computer-aided chemical engineering research advances in precision fermentation,]]></article-title>
<source><![CDATA[Current Opinion in Food Science]]></source>
<year>2024</year>
<volume>101196</volume>
</nlm-citation>
</ref>
<ref id="B27">
<label>27</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Amore]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Philip]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial intelligence in food biotechnology: trends and perspectives,]]></article-title>
<source><![CDATA[Front. Ind. Microbiol.]]></source>
<year>2023</year>
<volume>1</volume>
<page-range>1255505</page-range></nlm-citation>
</ref>
<ref id="B28">
<label>28</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Nian]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A review on reinforcement learning: Introduction and applications in industrial process control,]]></article-title>
<source><![CDATA[Comput. Chem. Eng.]]></source>
<year>2020</year>
<volume>139</volume>
<page-range>106886</page-range></nlm-citation>
</ref>
<ref id="B29">
<label>29</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yee]]></surname>
<given-names><![CDATA[C.S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Smart Fermentation Technologies: Microbial Process Control in Traditional Fermented Foods,]]></article-title>
<source><![CDATA[Fermentation]]></source>
<year>2025</year>
<volume>11</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>323</page-range></nlm-citation>
</ref>
<ref id="B30">
<label>30</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Asar]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Understanding the Functionality of Probiotics on the Edge of Artificial Intelligence (AI) Era,]]></article-title>
<source><![CDATA[Fermentation]]></source>
<year>2025</year>
<volume>11</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>259</page-range></nlm-citation>
</ref>
<ref id="B31">
<label>31</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Nettesheim]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Burggräf]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Steinberg]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Applications of machine learning in the brewing process: a systematic review,]]></article-title>
<source><![CDATA[Discover Artificial Intelligence]]></source>
<year>2024</year>
<volume>4</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>80</page-range></nlm-citation>
</ref>
<ref id="B32">
<label>32</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[El-Naggar]]></surname>
<given-names><![CDATA[N.E.A.]]></given-names>
</name>
<name>
<surname><![CDATA[Hamouda]]></surname>
<given-names><![CDATA[R.A.]]></given-names>
</name>
<name>
<surname><![CDATA[Elshafey]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial intelligence-based optimization for extracellular L-glutaminase free L-asparaginase production by Streptomyces violaceoruber under solid state fermentation conditions,]]></article-title>
<source><![CDATA[Scientific Reports]]></source>
<year>2024</year>
<volume>14</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>29625</page-range></nlm-citation>
</ref>
<ref id="B33">
<label>33</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Villarreal]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<source><![CDATA[Introducción a los Modelos de Pronósticos]]></source>
<year>2016</year>
<page-range>1-121</page-range><publisher-name><![CDATA[Univ. Nac. del Sur]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B34">
<label>34</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Van-Eck]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Waltman]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<source><![CDATA[VOS viewer manual]]></source>
<year>2022</year>
</nlm-citation>
</ref>
<ref id="B35">
<label>35</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pereira]]></surname>
<given-names><![CDATA[A.A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Precision fermentation in the realm of microbial protein production: State-of-the-art and future insights,]]></article-title>
<source><![CDATA[Food Research International]]></source>
<year>2024</year>
<volume>115527</volume>
</nlm-citation>
</ref>
<ref id="B36">
<label>36</label><nlm-citation citation-type="">
<collab>Grand View Report</collab>
<source><![CDATA[Precision Fermentation Market Size | Industry Report 2025-2030,]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B37">
<label>37</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wainaina]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Taherzadeh]]></surname>
<given-names><![CDATA[M.J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review,]]></article-title>
<source><![CDATA[Bioresource Technology]]></source>
<year>2023</year>
<volume>369</volume>
<page-range>128421</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
