New insights into the effects of growth phase and enzymatic treatment on the cell-wall properties of Chlorella vulgaris microalgae

Duval, Jérôme F. L.; Razafitianamaharavo, Angélina; Bihannic, Isabelle; Offroy, Marc; Lesniewska, Nicolas; Sohm, Bénédicte; Cordier, Hélène; Mustin, Christian; Pagnout, Christophe; Beaussart, Audrey

doi:10.1016/j.algal.2022.102955

Cited by 6 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5B ). These results were consistent with the previous studies (Ahamed et al 2022; Duval et al 2023; Zienkewwicz et al 2020).…”

Section: Discussionsupporting

confidence: 94%

“…Around 5 µL of the algal suspension was loaded and images were recorded using Confocal Laser Scanning Microscope-Fluoview Fv3000 at 40x objective (Olympus, Japan). The detection ranges were as follows: λ exc =488 nm and λ em =510-630 nm for carotenoids, λ exc =560 nm and λ em =640-750 nm for chlorophyll, and λ exc =530 nm and λ em =636 nm for nile red (Zienkiewicz et al, 2020; Duval et al, 2023).…”

Section: Morphological Data Acquisitionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing cellular production of fucoxanthin through machine learning assisted predictive approach inIsochrysis galbana

Manochkumar,

Jonnalagadda,

Cherukuri

et al. 2024

Preprint

View full text Add to dashboard Cite

The marine microalgae, Isochrysis galbana is a prolific producer of fucoxanthin which is a xanthophyll carotenoid with substantial global market value boasting extensive applications in the food, nutraceutical, pharmaceutical, and cosmetic industries. Although supplementation of different phytohormones to medium enhances fucoxanthin production, the quantification of pigment by conventional means is time-consuming and labor-intensive. This study addressed the multiple methodological limitations of HPLC-based fucoxanthin quantification and emphasized the need to develop a Machine Learning (ML) model as optimization and precise prediction remain a challenging task. Hence, an integrated experimental approach coupled with ML models was employed to predict fucoxanthin yield by supplementation of various phytohormones. The accuracy of fucoxanthin prediction excluding and including hormone descriptors was compared and evaluated using the ML models namely Random Forest (RF), Linear Regression (LR), Artificial Neural Network (ANN), and Support Vector Machine (SVM). RF model provided the most accurate prediction excluding hormone descriptors with coefficient of determination (R^2=0.809) and root-mean-square error (RMSE=0.776) followed by the ANN model with (R^2=0.722) and (RMSE=0.937). The inclusion of hormone descriptors for training and pre-processing of data further improved the fucoxanthin prediction accuracy of the RF model to (R^2=0.839) and (RMSE=0.712) and ANN model to (R^2=0.738) and (RMSE=0.909). These results indicated that the combination of low-cost, Ultraviolet (UV) spectrometric-based fucoxanthin quantification coupled with ML algorithms can be efficiently used for reliable prediction and enhanced fucoxanthin production, therefore highlighting a promising approach and furnishing invaluable insights towards the commercialization of microalgal fucoxanthin production.

show abstract

“…5B ). These results were consistent with the previous studies (Ahamed et al 2022; Duval et al 2023; Zienkewwicz et al 2020).…”

Section: Discussionsupporting

confidence: 94%

Section: Morphological Data Acquisitionmentioning

confidence: 99%

Enhancing cellular production of fucoxanthin through machine learning assisted predictive approach inIsochrysis galbana

Manochkumar,

Jonnalagadda,

Cherukuri

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…3a and b and on those reported elsewhere. 19 We further note that similar circular and concentric patterns are discernible in some of the CFM maps reported in literature for other types of algae. 37 Remarkably, these concentric patterns are systematically centred on the top of the microalgae surface (dome) with respect to the sample support on which C. vulgaris is attached.…”

Section: Force Spectroscopy Measurements On Microalgaesupporting

confidence: 83%

“…However, to date, studies dealing with the physicochemical characterization of microalgae surfaces remain relatively scarce 16,17 and, most often, retrieved descriptors of cell surface properties cannot be considered as intrinsic attributes but, instead, adjustable variables that strongly depend on cell growth conditions and environmental factors. [18][19][20] Among them, pH is one of the key parameters that influences microalgae reactivity. As an illustration, strong variations of pH, as met in acid mine drainage, can dramatically affect the bioremediation capacity of microalgae due to unfavourable change in their electrostatic interactions with heavy metals.…”

Section: Introductionmentioning

confidence: 99%

Physicochemical surface properties of Chlorella vulgaris: a multiscale assessment, from electrokinetic and proton uptake descriptors to intermolecular adhesion forces

Lesniewska,

Duval,

Caillet

et al. 2024

Nanoscale

Self Cite

View full text Add to dashboard Cite

show abstract

“…These phases include lag phase, exponential growth phase, and stationary phase. [ 32,33 ] Microalgae within Ca500 beads displayed the steepest slope of exponential growth compared to other beads, reaching the stationary phase by the end of the experiment. Meanwhile, microalgae in other beads likely remained in the exponential growth phase.…”

Section: Resultsmentioning

confidence: 99%

Calcium Alginate Core–Shell Liquid Beads Encapsulated with Microalgae for Wastewater Treatment

Tran,

Nguyen,

Yadav

et al. 2024

Adv Energy and Sustain Res

View full text Add to dashboard Cite

Liquid beads are core–shell particles with a liquid core and a solid shell. Calcium alginate liquid beads have been emerging as a promising platform for cell encapsulation. These beads have demonstrated their capability of encapsulating and culturing a wide range of human cells for tissue engineering. However, a significant research gap remains in the application of alginate liquid beads for encapsulating photosynthetic microorganisms. Herein, fast‐growing microalgae strain Chlorella vulgaris is encapsulated into calcium alginate liquid beads to facilitate the removal of nutrients from wastewater, minimizing the risk of eutrophication. Liquid bead‐microalgae systems are prepared, using different calcium ion concentrations as crosslinking ions. It has been thoroughly characterized for their morphologies, cell growth patterns, nutrient removal capabilities, and overall stability throughout the wastewater treatment process, with upflow anaerobic sludge blanket effluent as the wastewater model. The results indicate that the liquid bead‐microalgae system with the highest calcium ion concentration (5%) performs more efficiently, exhibiting a well‐formed crosslinking structure, leading to rapid cell growth with the highest cell density and the most effective removal of nutrients. The findings from this study provide valuable insights for future optimization and upscaling efforts in wastewater treatment systems based on calcium alginate liquid beads.

show abstract

New insights into the effects of growth phase and enzymatic treatment on the cell-wall properties of Chlorella vulgaris microalgae

Cited by 6 publications

References 35 publications

Enhancing cellular production of fucoxanthin through machine learning assisted predictive approach inIsochrysis galbana

Enhancing cellular production of fucoxanthin through machine learning assisted predictive approach inIsochrysis galbana

Physicochemical surface properties of Chlorella vulgaris: a multiscale assessment, from electrokinetic and proton uptake descriptors to intermolecular adhesion forces

Calcium Alginate Core–Shell Liquid Beads Encapsulated with Microalgae for Wastewater Treatment

Contact Info

Product

Resources

About