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Arduino Soft Sensor for Monitoring Schizochytrium sp. Fermentation, a Proof of Concept for the Industrial Application of Genome-Scale Metabolic Models in the Context of Pharma 4.0. Processes (Basel) 2022. [DOI: 10.3390/pr10112226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Schizochytrium sp. is a microorganism cultured for producing docosahexaenoic acid (DHA). Genome-scale metabolic modeling (GEM) is a promising technique for describing gen-protein-reactions in cells, but with still limited industrial application due to its complexity and high computation requirements. In this work, we simplified GEM results regarding the relationship between the specific oxygen uptake rate (−rO2), the specific growth rate (µ), and the rate of lipid synthesis (rL) using an evolutionary algorithm for developing a model that can be used by a soft sensor for fermentation monitoring. The soft sensor estimated the concentration of active biomass (X), glutamate (N), lipids (L), and DHA in a Schizochytrium sp. fermentation using the dissolved oxygen tension (DO) and the oxygen mass transfer coefficient (kLa) as online input variables. The soft sensor model described the biomass concentration response of four reported experiments characterized by different kLa values. The average range normalized root-mean-square error for X, N, L, and DHA were equal to 1.1, 1.3, 1.1, and 3.2%, respectively, suggesting an acceptable generalization capacity. The feasibility of implementing the soft sensor over a low-cost electronic board was successfully tested using an Arduino UNO, showing a novel path for applying GEM-based soft sensors in the context of Pharma 4.0.
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Hassoun A, Bekhit AED, Jambrak AR, Regenstein JM, Chemat F, Morton JD, Gudjónsdóttir M, Carpena M, Prieto MA, Varela P, Arshad RN, Aadil RM, Bhat Z, Ueland Ø. The fourth industrial revolution in the food industry-part II: Emerging food trends. Crit Rev Food Sci Nutr 2022; 64:407-437. [PMID: 35930319 DOI: 10.1080/10408398.2022.2106472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The food industry has recently been under unprecedented pressure due to major global challenges, such as climate change, exponential increase in world population and urbanization, and the worldwide spread of new diseases and pandemics, such as the COVID-19. The fourth industrial revolution (Industry 4.0) has been gaining momentum since 2015 and has revolutionized the way in which food is produced, transported, stored, perceived, and consumed worldwide, leading to the emergence of new food trends. After reviewing Industry 4.0 technologies (e.g. artificial intelligence, smart sensors, robotics, blockchain, and the Internet of Things) in Part I of this work (Hassoun, Aït-Kaddour, et al. 2022. The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition, 1-17.), this complimentary review will focus on emerging food trends (such as fortified and functional foods, additive manufacturing technologies, cultured meat, precision fermentation, and personalized food) and their connection with Industry 4.0 innovations. Implementation of new food trends has been associated with recent advances in Industry 4.0 technologies, enabling a range of new possibilities. The results show several positive food trends that reflect increased awareness of food chain actors of the food-related health and environmental impacts of food systems. Emergence of other food trends and higher consumer interest and engagement in the transition toward sustainable food development and innovative green strategies are expected in the future.
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Affiliation(s)
- Abdo Hassoun
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
- Syrian AcademicExpertise (SAE), Gaziantep, Turkey
| | | | - Anet Režek Jambrak
- Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb, Croatia
| | - Joe M Regenstein
- Department of Food Science, Cornell University, Ithaca, New York, USA
| | - Farid Chemat
- Green Extraction Team, INRAE, Avignon University, Avignon, France
| | - James D Morton
- Department of Wine Food and Molecular Biosciences, Lincoln University, Lincoln, New Zealand
| | - María Gudjónsdóttir
- Faculty of Food Science and Nutrition, School of Health Sciences, University of Iceland, Reykjavík, Iceland
| | - María Carpena
- Department of Analytical Chemistry and Food Science, Faculty of Science, Universidade de Vigo, Nutrition and Bromatology Group, Ourense, Spain
| | - Miguel A Prieto
- Department of Analytical Chemistry and Food Science, Faculty of Science, Universidade de Vigo, Nutrition and Bromatology Group, Ourense, Spain
| | - Paula Varela
- Fisheries and Aquaculture Research, Nofima - Norwegian Institute of Food, Ås, Norway
| | - Rai Naveed Arshad
- Institute of High Voltage & High Current, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
| | - Zuhaib Bhat
- Division of Livestock Products Technology, SKUAST-J, Jammu, India
| | - Øydis Ueland
- Fisheries and Aquaculture Research, Nofima - Norwegian Institute of Food, Ås, Norway
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Wang S, Wan W, Wang Z, Zhang H, Liu H, Arunakumara KKIU, Cui Q, Song X. A Two-Stage Adaptive Laboratory Evolution Strategy to Enhance Docosahexaenoic Acid Synthesis in Oleaginous Thraustochytrid. Front Nutr 2021; 8:795491. [PMID: 35036411 PMCID: PMC8759201 DOI: 10.3389/fnut.2021.795491] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
Thraustochytrid is a promising algal oil resource with the potential to meet the demand for docosahexaenoic acid (DHA). However, oils with high DHA content produced by genetic modified thraustochytrids are not accepted by the food and pharmaceutical industries in many countries. Therefore, in order to obtain non-transgenic strains with high DHA content, a two-stage adaptive laboratory evolution (ALE) strategy was applied to the thraustochytrid Aurantiochytrium sp. Heavy-ion irradiation technique was first used before the ALE to increase the genetic diversity of strains, and then two-step ALE: low temperature based ALE and ACCase inhibitor quizalofop-p-ethyl based ALE were employed in enhancing the DHA production. Using this strategy, the end-point strain E-81 with a DHA content 51% higher than that of the parental strain was obtained. The performance of E-81 strain was further analyzed by component analysis and quantitative real-time PCR. The results showed that the enhanced in lipid content was due to the up-regulated expression of key enzymes in lipid accumulation, while the increase in DHA content was due to the increased transcriptional levels of polyunsaturated fatty acid synthase. This study demonstrated a non-genetic approach to enhance lipid and DHA content in non-model industrial oleaginous strains.
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Affiliation(s)
- Sen Wang
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Provincial Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
- Shandong Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Qingdao Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
| | - Weijian Wan
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Provincial Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Qingdao Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
| | - Zhuojun Wang
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Provincial Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- University of Chinese Academy of Sciences, Beijing, China
- Shandong Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Qingdao Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
| | - Huidan Zhang
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Provincial Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
- Shandong Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Qingdao Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
| | - Huan Liu
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Provincial Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
- Shandong Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Qingdao Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
| | - K. K. I. U. Arunakumara
- Department of Crop Science, Faculty of Agriculture, University of Ruhuna, Kamburupitiya, Sri Lanka
| | - Qiu Cui
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Provincial Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
- University of Chinese Academy of Sciences, Beijing, China
- Shandong Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Qingdao Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
| | - Xiaojin Song
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Provincial Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
- University of Chinese Academy of Sciences, Beijing, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
- Shandong Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Qingdao Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
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Rathore AS, Mishra S, Nikita S, Priyanka P. Bioprocess Control: Current Progress and Future Perspectives. Life (Basel) 2021; 11:life11060557. [PMID: 34199245 PMCID: PMC8231968 DOI: 10.3390/life11060557] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 02/07/2023] Open
Abstract
Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.
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