Digitalization in Bioproduction—AI & Digital Twins for Process Optimization

Muhammet Raşit Cesur, Zeynep Girgin Ersoy, Müberra Fatma Cesur, Ğarip Demir and Sedef Tunca ()
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Muhammet Raşit Cesur: Istanbul Medeniyet University
Zeynep Girgin Ersoy: Bursa Uludag University
Müberra Fatma Cesur: Gebze Technical University
Ğarip Demir: Gebze Technical University
Sedef Tunca: Gebze Technical University

Chapter Chapter 20 in The Palgrave Handbook of Supply Chain and Disruptive Technologies, 2025, pp 503-531 from Springer

Abstract: Abstract Food preservation is indispensable to retaining the desired food quality by extending shelf life. Lactic acid bacteria are outstanding to improve food safety and quality by reducing the pathogens using lactic acid and antimicrobial peptides including nisin, which is the only bacteriocin approved by the United States Food and Drug Administration (FDA) as a food additive. Large-scale bioproduction of the antimicrobial food additives under optimized conditions is essential in the food manufacturing industry. Therefore, researchers have dramatically focused to develop effective and integrated manufacturing methods for the food additives at an industrial scale. With the emergence of digital technology and machine learning methods, computational predictions have provided a significant reduction in the number of experimental conditions via the computational design and optimization of these conditions. The combined use of fundamental biotechnology concepts and artificial intelligence technologies has been discussed herein as a promising approach to open novel avenues for designing cost-effective and sustainable microbial production processes. Artificial intelligence is useful to build a digital twin of the bioproduction process and to make experiments in a cyber environment faster and cheaper than real. It is important to emphasize that this computational approach requires a relatively limited number of preliminary experiments by accurately predicting the combinations of optimal conditions. Thus, we presented a case study to demonstrate the potential of artificial intelligence technologies coupled with experimental approaches in the prediction of optimal growth conditions for efficient nisin production.

Keywords: Food Safety; Lactic Acid Bacteria; Nisin; Digitalization in Bioproduction; Digital Twin; Machine Learning; Artificial Neural Network (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-90210-9_20

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DOI: 10.1007/978-3-031-90210-9_20

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