2D Fluorescence spectroscopy for monitoring Dunaliella salina concentration and integrity during membrane harvesting

Sá, Marta; Monte, Joana; Brazinha, Carla; Galinha, Cláudia F.; Crespo, João G.

doi:10.1016/j.algal.2017.04.013

Cited by 25 publications

(27 citation statements)

References 24 publications

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“…In the cell concentration prediction model, it is possible to notice the importance of the emission wavelengths above 600 nm, negative or positively (Figure 6a). This region was described previously as the pigment emission band (Moberg et al, 2001; Sá et al, 2017, 2019b). In previous studies this region was already reported for its contribution in cell concentration prediction models (Sá et al, 2020a).…”

Section: Resultsmentioning

confidence: 89%

“…Fluorescence spectroscopy has recently been studied to monitor cell concentration in other microalgae, such as Dunaliella salina and Nannochloropsis oceanica (Sá et al, 2017, 2019a, 2020a). Nevertheless, according to the authors knowledge, it is the first time that this technique is used to develop one prediction model able to be used in different microalgae species, revealing the high potential of this technique to be used in microalgae production.…”

Section: Resultsmentioning

confidence: 99%

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Production and monitoring of biomass and fucoxanthin with brown microalgae under outdoor conditions

Gao

Sá

Itd

et al. 2020

Biotech & Bioengineering

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The effect of light on biomass and fucoxanthin (Fx) productivities was studied in two microalgae, Tisochrysis lutea and Phaeodactylum tricornutum. High and low biomass concentrations (1.1 and 0.4 g L−1) were tested in outdoor pilot‐scale flat‐panel photobioreactors at semi‐continuous cultivation mode. Fluorescence spectroscopy coupled with chemometric modeling was used to develop prediction models for Fx content and for biomass concentration to be applied for both microalgae species. Prediction models showed high R2 for cell concentration (.93) and Fx content (.77). Biomass productivity was lower for high biomass concentration than low biomass concentration, for both microalgae (1.1 g L−1: 75.66 and 98.14 mg L−1 d−1, for T. lutea and P. tricornutum, respectively; 0.4 g L−1: 129.9 and 158.47 mg L−1 d−1, T. lutea and P. tricornutum). The same trend was observed in Fx productivity (1.1 g L−1: 1.14 and 1.41 mg L−1 d−1, T. lutea and P. tricornutum; 0.4 g L−1: 2.09 and 1.73 mg L−1 d−1, T. lutea and P. tricornutum). These results show that biomass and Fx productivities can be set by controlling biomass concentration under outdoor conditions and can be predicted using fluorescence spectroscopy. This monitoring tool opens new possibilities for online process control and optimization.

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Production and monitoring of biomass and fucoxanthin with brown microalgae under outdoor conditions

Gao

Sá

Itd

et al. 2020

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…permeate) only 4 were selected as useful for predicting microalgae cell disruption, with different weights [20]. Besides the characterization of membrane processes involving biological reactions, PCA was also used to extract information (qualitative information) from fluorescence scans of membrane surfaces during a reverse electrodialysis (RED) process (Figure 9) using real sea and river water streams [22].…”

Section: When Non-mechanistic Modelling Is Seen As Learningmentioning

confidence: 99%

“…Additionally, by acquiring fluorescence in different membrane surfaces (anion-exchange membranes and cation-exchange membranes, river and sea sides) it was possible to confirm that fouling of anion-exchange membranes facing river water was the main factor affecting the RED stack performance. permeate) only 4 were selected as useful for predicting microalgae cell disruption, with different weights [20]. Besides the characterization of membrane processes involving biological reactions, PCA was also used to extract information (qualitative information) from fluorescence scans of membrane surfaces during a reverse electrodialysis (RED) process (Figure 9) using real sea and river water streams [22].…”

Section: When Non-mechanistic Modelling Is Seen As Learningmentioning

confidence: 99%

“…Using the methodology of PCA followed by PLS, 2D fluorescence was posteriorly used to develop a monitoring tool for the harvesting of microalgae ( Dunaliella salina ) by membrane micro- and ultrafiltration [ 20 , 21 ]. These studies focused mainly on monitoring cell concentration ( Figure 7 ) and cell integrity, due to the fragility of the microalga assessed.…”

Section: When Non-mechanistic Modelling Is Seen As Learningmentioning

confidence: 99%

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From Black Box to Machine Learning: A Journey through Membrane Process Modelling

Galinha

Crespo

2021

Membranes

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Membrane processes are complex systems, often comprising several physicochemical phenomena, as well as biological reactions, depending on the systems studied. Therefore, process modelling is a requirement to simulate (and predict) process and membrane performance, to infer about optimal process conditions, to assess fouling development, and ultimately, for process monitoring and control. Despite the actual dissemination of terms such as Machine Learning, the use of such computational tools to model membrane processes was regarded by many in the past as not useful from a scientific point-of-view, not contributing to the understanding of the phenomena involved. Despite the controversy, in the last 25 years, data driven, non-mechanistic modelling is being applied to describe different membrane processes and in the development of new modelling and monitoring approaches. Thus, this work aims at providing a personal perspective of the use of non-mechanistic modelling in membrane processes, reviewing the evolution supported in our own experience, gained as research group working in the field of membrane processes. Additionally, some guidelines are provided for the application of advanced mathematical tools to model membrane processes.

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Biorefinery potential of Dunaliella (Chlorophyta) for production of value‐added compounds

Sharma,

Singh,

Singh

2024

Biofuels Bioprod Bioref

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Dunaliella functions as a microscopic bioindustry that can capture atmospheric carbon dioxide and amass it into a range of economically viable compounds. Dunaliella‐based biorefineries have emerged as sustainable platforms for fostering a green economy and reducing carbon emissions. Dunaliella‐derived value‐added compounds are commercially important due to their application in the food, pharmaceutical, cosmeceutical, and nutraceutical sectors. The present study highlights the economic application of different Dunaliella species and the available sustainable technologies for the cultivation, harvesting, and extraction of bioactive compounds that have the potential for commercial scaleup. Their incorporation into biorefinery processes can reduce dependency on energy‐intensive and low‐yield processes.This review briefly discusses the technoeconomic analysis (TEA) of Dunaliella‐driven industrial approaches for the optimization of effective yield and determination of final production costs. The major algal biorefineries are operational in countries like Australia, China, India, Israel, Japan, Portugal, Spain and the USA. The authors have comprehensively deliberated different aspects of Dunaliella from physiological to industrial applications, meanwhile underscoring the existing knowledge gaps that impede the success of Dunaliella biorefineries. The current industrial methods being employed for biorefineries are limited to producing individual bioproducts, which may not be fully cost effective, making them less competitive in the global market. However, the analysis carried out can serve as a starting point and can guide stakeholders towards the future direction of Dunaliella‐based research and the circular bioeconomy.

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2D Fluorescence spectroscopy for monitoring Dunaliella salina concentration and integrity during membrane harvesting

Cited by 25 publications

References 24 publications

Production and monitoring of biomass and fucoxanthin with brown microalgae under outdoor conditions

Production and monitoring of biomass and fucoxanthin with brown microalgae under outdoor conditions

From Black Box to Machine Learning: A Journey through Membrane Process Modelling

Biorefinery potential of Dunaliella (Chlorophyta) for production of value‐added compounds

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