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Syed T, Krujatz F, Ihadjadene Y, Mühlstädt G, Hamedi H, Mädler J, Urbas L. A review on machine learning approaches for microalgae cultivation systems. Comput Biol Med 2024; 172:108248. [PMID: 38493599 DOI: 10.1016/j.compbiomed.2024.108248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 02/15/2024] [Accepted: 03/06/2024] [Indexed: 03/19/2024]
Abstract
Microalgae plays a crucial role in biomass production within aquatic environments and are increasingly recognized for their potential in generating biofuels, biomaterials, bioactive compounds, and bio-based chemicals. This growing significance is driven by the need to address imminent global challenges such as food and fuel shortages. Enhancing the value chain of bio-based products necessitates the implementation of an advanced screening and monitoring system. This system is crucial for tailoring and optimizing the cultivation conditions, ensuring the lucrative and efficient production of the final desired product. This, in turn, underscores the necessity for robust predictive models to accurately emulate algae growth in different conditions during the initial cultivation phase and simulate their subsequent processing in the downstream stage. In pursuit of these objectives, diverse mechanistic and machine learning-based methods have been independently employed to model and optimize microalgae processes. This review article thoroughly examines the techniques delineated in the literature for modeling, predicting, and monitoring microalgal biomass across various applications such as bioenergy, pharmaceuticals, and the food industry. While highlighting the merits and limitations of each method, we delve into the realm of newly emerging hybrid approaches and conduct an exhaustive survey of this evolving methodology. The challenges currently impeding the practical implementation of hybrid techniques are explored, and drawing inspiration from successful applications in other machine-learning-assisted fields, we review various plausible solutions to overcome these obstacles.
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Affiliation(s)
- Tehreem Syed
- Institute of Automation, Technische Universität Dresden, 01062, Saxony, Germany
| | - Felix Krujatz
- Faculty of Natural and Environmental Sciences, University of Applied Sciences Zittau/Görlitz, 02763, Zittau, Germany; Institute of Natural Materials Technology, Technische Universität Dresden, 01069, Saxony, Germany
| | - Yob Ihadjadene
- Institute of Natural Materials Technology, Technische Universität Dresden, 01069, Saxony, Germany
| | | | - Homa Hamedi
- Institute of Process Engineering and Environmental Technology, Technische Universität Dresden, 01062, Saxony, Germany
| | - Jonathan Mädler
- Institute of Process Engineering and Environmental Technology, Technische Universität Dresden, 01062, Saxony, Germany.
| | - Leon Urbas
- Institute of Automation, Technische Universität Dresden, 01062, Saxony, Germany; Institute of Process Engineering and Environmental Technology, Technische Universität Dresden, 01062, Saxony, Germany
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Molnár C, Drigla TD, Barbu-Tudoran L, Bajama I, Curean V, Cîntă Pînzaru S. Pilot SERS Monitoring Study of Two Natural Hypersaline Lake Waters from a Balneary Resort during Winter-Months Period. BIOSENSORS 2023; 14:19. [PMID: 38248396 PMCID: PMC10813592 DOI: 10.3390/bios14010019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/13/2023] [Accepted: 12/26/2023] [Indexed: 01/23/2024]
Abstract
Water samples from two naturally hypersaline lakes, renowned for their balneotherapeutic properties, were investigated through a pilot SERS monitoring program. Nanotechnology-based techniques were employed to periodically measure the ultra-sensitive SERS molecular characteristics of the raw water-bearing microbial community and the inorganic content. Employing the Pearson correlation coefficient revealed a robust linear relationship between electrical conductivity and pH and Raman and SERS spectral data of water samples, highlighting the interplay complexity of Raman/SERS signals and physicochemical parameters within each lake. The SERS data obtained from raw waters with AgNPs exhibited a dominant, reproducible SERS feature resembling adsorbed β-carotene at submicromole concentration, which could be related to the cyanobacteria-AgNPs interface and supported by TEM analyses. Notably, spurious SERS sampling cases showed molecular traces attributed to additional metabolites, suggesting multiplexed SERS signatures. The conducted PCA demonstrated observable differences in the β-carotene SERS band intensities between the two lakes, signifying potential variations in picoplankton abundance and composition or environmental influences. Moreover, the study examined variations in the SERS intensity ratio I245/I1512, related to the balance between inorganic (Cl--induced AgNPs aggregation) and organic (cyanobacteria population) balance, in correlation with the electrical conductivity. These findings signify the potential of SERS data for monitoring variations in microorganism concentration, clearly dependent on ion concentration and nutrient dynamics in raw, hypersaline water bodies.
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Affiliation(s)
- Csilla Molnár
- National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donath, 400293 Cluj-Napoca, Romania
- Biomolecular Physics Department, Babeş-Bolyai University, Kogălniceanu 1, 400084 Cluj Napoca, Romania; (T.D.D.); (I.B.)
| | - Teodora Diana Drigla
- Biomolecular Physics Department, Babeş-Bolyai University, Kogălniceanu 1, 400084 Cluj Napoca, Romania; (T.D.D.); (I.B.)
| | - Lucian Barbu-Tudoran
- Electron Microscopy Centre, Babeș-Bolyai University, Clinicilor 5-7, 400006 Cluj-Napoca, Romania;
| | - Ilirjana Bajama
- Biomolecular Physics Department, Babeş-Bolyai University, Kogălniceanu 1, 400084 Cluj Napoca, Romania; (T.D.D.); (I.B.)
| | - Victor Curean
- Faculty of Pharmacy, “Iuliu Hatieganu” University of Medicine and Pharmacy, Victor Babes 8, 400347 Cluj-Napoca, Romania;
| | - Simona Cîntă Pînzaru
- Biomolecular Physics Department, Babeş-Bolyai University, Kogălniceanu 1, 400084 Cluj Napoca, Romania; (T.D.D.); (I.B.)
- Institute for Research, Development and Innovation in Applied Natural Sciences, Babes-Bolyai University, Fantanele 30, 400327 Cluj-Napoca, Romania
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