Microbial Biotechnology in the Food Industry: Innovations, Applications, and Future Perspectives

A special issue of Microorganisms (ISSN 2076-2607). This special issue belongs to the section "Microbial Biotechnology".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 813

Special Issue Editor


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Guest Editor
Facultad de Química, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico
Interests: microbial biotechnology; food science

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a comprehensive platform for the dissemination of cutting-edge research on microbial biotechnology and its applications in the food industry. Microbial biotechnology offers a diverse array of innovative and sustainable strategies for enhancing the quality, safety, and health-promoting properties of food products. From traditional and emerging fermentation processes to the development of functional foods and bioactive compounds, microbial systems play an essential role in addressing current and future challenges in food production and preservation. We invite high-quality submissions that address the discovery and characterization of novel microbial strains, genetically engineered microorganisms, and microbial consortia, as well as their roles in food processing, spoilage prevention, sensory enhancement, and functional food development. Contributions employing advanced omics-based approaches (such as metagenomics, transcriptomics, proteomics, and metabolomics) and synthetic biology tools for the design and optimization of microbial functions within food systems are also encouraged. This Special Issue seeks to foster a multidisciplinary dialogue that bridges fundamental microbiology, biotechnology, and food science, with a particular emphasis on sustainable and innovative solutions for the food industry.

Topics of Interest:

  • Microbial fermentation of traditional and novel food products
  • Application of prebiotics, probiotics, and postbiotics in functional foods
  • Microbial bioengineering for enhanced production of bioactive metabolites
  • Microbial enzymes and biocatalysts in food processing and preservation
  • Biopreservation strategies and antimicrobial peptides of microbial origin
  • Design and application of synthetic microbial communities in food systems
  • Integration of omics technologies in food microbiology research
  • Microbial metabolites with health-promoting properties
  • Microbial production of flavors, aromas, and sensory enhancers
  • Artificial intelligence approaches in microbial food biotechnology

Types of Expected Contributions: This Special Issue welcomes original research articles, comprehensive review papers, and industrial case studies that demonstrate the application of microbial biotechnology in various sectors of the food industry.

Dr. Diana Rocha-Mendoza
Guest Editor

Manuscript Submission Information

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Keywords

  • microbial biotechnology
  • food fermentation
  • probiotics
  • functional foods
  • microbial metabolites
  • food safety
  • biopreservation
  • synthetic biology
  • omics technologies in food
  • food microbiome
  • precision fermentation
  • artificial intelligence in food science

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Published Papers (1 paper)

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Research

16 pages, 2566 KB  
Article
Predictive Fermentation Control of Lactiplantibacillus plantarum Using Deep Learning Convolutional Neural Networks
by Chien-Chang Wu, Jung-Sheng Chen, Yu-Ching Lu, Jain-Shing Wu, Yu-Fen Huang and Chien-Sen Liao
Microorganisms 2025, 13(11), 2601; https://doi.org/10.3390/microorganisms13112601 - 15 Nov 2025
Viewed by 523
Abstract
The fermentation of Lactiplantibacillus plantarum is a complex bioprocess due to the nonlinear and dynamic nature of microbial growth. Traditional monitoring methods often fail to provide early and actionable insights into fermentation outcomes. This study proposes a deep learning-based predictive system using convolutional [...] Read more.
The fermentation of Lactiplantibacillus plantarum is a complex bioprocess due to the nonlinear and dynamic nature of microbial growth. Traditional monitoring methods often fail to provide early and actionable insights into fermentation outcomes. This study proposes a deep learning-based predictive system using convolutional neural networks (CNNs) to classify fermentation trajectories and anticipate final cell counts based on the first 24 h of process data. A total of 52 fermentation runs were conducted, during which real-time parameters, including pH, temperature, and dissolved oxygen, were continuously recorded and transformed into time-series feature vectors. After rigorous preprocessing and feature selection, the CNN was trained to classify fermentation outcomes into three categories: successful, semi-successful, and failed batches. The model achieved a classification accuracy of 97.87%, outperforming benchmark models such as LSTM and XGBoost. Validation experiments demonstrated the model’s practical utility: early predictions enabled timely manual interventions that effectively prevented batch failures or improved suboptimal fermentations. These findings suggest that deep learning provides a robust and scalable framework for real-time fermentation control, with significant implications for enhancing efficiency and reducing costs in industrial probiotic production. Full article
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