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Independent variables' coded levels applied in the factorial planning for the extraction of RuBisCO from spinach.
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Ribulose-1,5-biphosphate carboxylase/oxygenase (RuBisCO) is the most abundant protein on the planet, being present in plants, algae and various species of bacteria, with application in the pharmaceutical, chemical, cosmetic and food industries. However, current extraction methods of RuBisCO do not allow high yields of extraction. Therefore, the dev...
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... 2 3 factorial planning contains a central point (level zero), factorial points (1 and −1, level one) and axial points (level α)-cf. the Supporting Information (SI), Table S1. The central and factorial points assume values that depend on the work carried out. ...Context 2
... this case, it was assumed for the central point a pH of 7.0, a solid-liquid ratio of 0.10 and an IL concentration of 1.50 M. The factorial points were defined to analyze a broad range of operating conditions. The independent variables coded levels used in the factorial planning are presented in Table 1. The axial points are encoded at a distance α from the central point (Equation (1)) [36]: ...Context 3
... Statsoft Statistica 10.0© software was used in all statistical analyzes and to draw the response surfaces. The obtained results were statistically analyzed with a 95% confidence level, and a student t-test was applied to verify the statistical significance of the adjusted data (Supplementary Information, Tables S7-S13). The regression coefficient (R 2 ), the lack of fit and the F-value obtained from the analysis of variance (ANOVA) were evaluated to determine the model's adequacy. ...Context 4
... aqueous solution prepared with commercially acquired RuBisCO at 1 mg/mL was used for comparison purposes. Both CD spectra ( Figure 8) were analyzed with the K2D3 web server [41], with the percentages of α-helix (% α-helix) and β-sheet (% β-sheet) being given in Table 2. Relatively to the extraction yield of RuBisCO, from Figure 6D-F and from Figure S11 and Tables S13 and S14 given in the Supplementary Materials, it is clear that high pH values lead to a more efficient extraction of RuBisCO from the spinach biomass. The solid-liquid ratio also has a relevant impact on the yield of RuBisCO, leading to a maximum value at a solid-liquid ratio of 0.12). ...Context 5
... vertical line indicates the statistical significance of the effects, Figure S11: Pareto charts for the standardized main effects in the factorial planning with [Ch][Ac] for RuBisCOs' extraction yield. The vertical line indicates the statistical significance of the effects, Figure S12: Profiles for predicted values and desirability in the factorial planning for both dependent variables with [Ch]Cl, Figure S13: Profiles for predicted values and desirability in the factorial planning for both dependent variables with [Ch][Ac], Table S1: 23 factorial planning for each IL ( [Ch]Cl and [Ch][Ac]), Table S2: pH values of the IL aqueous solutions and of the extracts, Table S3: Experimental data and response surface predicted values of the factorial planning for RuBisCOs' concentration, extracting with [Ch]Cl, Table S4: Experimental data and response surface predicted values of the factorial planning for RuBisCOs' extraction yield, extracting with [Ch]Cl, Table S5: Experimental data and response surface predicted values of the factorial planning for RuBisCOs' concentration, extracting with [Ch][Ac], Table S6: Experimental data and response surface predicted values of the factorial planning for RuBisCOs' concentration, extracting with [Ch][Ac], Table S7: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' concentration from RSM using [Ch]Cl, R2 = 0.95197 and radj. = 0.90875, Table S8: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch]Cl, Table S9: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch]Cl, R2 = 0.75851 and radj. ...Context 6
... 0.90875, Table S8: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch]Cl, Table S9: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch]Cl, R2 = 0.75851 and radj. = 0.54116, Table S10: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch]Cl, Table S11: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' concentration from RSM using [Ch][Ac], R2 = 0.90873 and radj. = 0.82659, Table S12: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch][Ac], Table S13: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch][Ac], R2 = 0.89709 and radj, = 0.80447, Table S14: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch][Ac], Table S15: ANOVA data for RuBisCO concentration when extracting with [Ch]Cl, Table S16: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch]Cl, Table S17: ANOVA data for RuBisCO concentration when extracting with [Ch][Ac], Table S18: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch][Ac]. ...Context 7
... 0.90875, Table S8: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch]Cl, Table S9: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch]Cl, R2 = 0.75851 and radj. = 0.54116, Table S10: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch]Cl, Table S11: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' concentration from RSM using [Ch][Ac], R2 = 0.90873 and radj. = 0.82659, Table S12: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch][Ac], Table S13: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch][Ac], R2 = 0.89709 and radj, = 0.80447, Table S14: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch][Ac], Table S15: ANOVA data for RuBisCO concentration when extracting with [Ch]Cl, Table S16: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch]Cl, Table S17: ANOVA data for RuBisCO concentration when extracting with [Ch][Ac], Table S18: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch][Ac]. ...Context 8
... 0.54116, Table S10: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch]Cl, Table S11: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' concentration from RSM using [Ch][Ac], R2 = 0.90873 and radj. = 0.82659, Table S12: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch][Ac], Table S13: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch][Ac], R2 = 0.89709 and radj, = 0.80447, Table S14: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch][Ac], Table S15: ANOVA data for RuBisCO concentration when extracting with [Ch]Cl, Table S16: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch]Cl, Table S17: ANOVA data for RuBisCO concentration when extracting with [Ch][Ac], Table S18: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch][Ac]. ...Context 9
... 0.54116, Table S10: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch]Cl, Table S11: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' concentration from RSM using [Ch][Ac], R2 = 0.90873 and radj. = 0.82659, Table S12: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch][Ac], Table S13: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch][Ac], R2 = 0.89709 and radj, = 0.80447, Table S14: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch][Ac], Table S15: ANOVA data for RuBisCO concentration when extracting with [Ch]Cl, Table S16: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch]Cl, Table S17: ANOVA data for RuBisCO concentration when extracting with [Ch][Ac], Table S18: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch][Ac]. ...Context 10
... 0.54116, Table S10: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch]Cl, Table S11: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' concentration from RSM using [Ch][Ac], R2 = 0.90873 and radj. = 0.82659, Table S12: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch][Ac], Table S13: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch][Ac], R2 = 0.89709 and radj, = 0.80447, Table S14: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch][Ac], Table S15: ANOVA data for RuBisCO concentration when extracting with [Ch]Cl, Table S16: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch]Cl, Table S17: ANOVA data for RuBisCO concentration when extracting with [Ch][Ac], Table S18: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch][Ac]. ...Context 11
... 0.54116, Table S10: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch]Cl, Table S11: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' concentration from RSM using [Ch][Ac], R2 = 0.90873 and radj. = 0.82659, Table S12: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch][Ac], Table S13: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch][Ac], R2 = 0.89709 and radj, = 0.80447, Table S14: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch][Ac], Table S15: ANOVA data for RuBisCO concentration when extracting with [Ch]Cl, Table S16: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch]Cl, Table S17: ANOVA data for RuBisCO concentration when extracting with [Ch][Ac], Table S18: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch][Ac]. ...Context 12
... 0.54116, Table S10: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch]Cl, Table S11: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' concentration from RSM using [Ch][Ac], R2 = 0.90873 and radj. = 0.82659, Table S12: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch][Ac], Table S13: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch][Ac], R2 = 0.89709 and radj, = 0.80447, Table S14: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch][Ac], Table S15: ANOVA data for RuBisCO concentration when extracting with [Ch]Cl, Table S16: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch]Cl, Table S17: ANOVA data for RuBisCO concentration when extracting with [Ch][Ac], Table S18: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch][Ac]. ...Context 13
... 0.54116, Table S10: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch]Cl, Table S11: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' concentration from RSM using [Ch][Ac], R2 = 0.90873 and radj. = 0.82659, Table S12: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch][Ac], Table S13: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch][Ac], R2 = 0.89709 and radj, = 0.80447, Table S14: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch][Ac], Table S15: ANOVA data for RuBisCO concentration when extracting with [Ch]Cl, Table S16: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch]Cl, Table S17: ANOVA data for RuBisCO concentration when extracting with [Ch][Ac], Table S18: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch][Ac]. ...Context 14
... 0.54116, Table S10: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch]Cl, Table S11: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' concentration from RSM using [Ch][Ac], R2 = 0.90873 and radj. = 0.82659, Table S12: Effects of the variables in the second-order polynomial model for the extraction RuBisCO concentration using [Ch][Ac], Table S13: Regression coefficients of the predicted second-order polynomial model for the RuBisCOs' extraction yield from RSM using [Ch][Ac], R2 = 0.89709 and radj, = 0.80447, Table S14: Effects of the variables in the second-order polynomial model for the extraction yield of RuBisCO using [Ch][Ac], Table S15: ANOVA data for RuBisCO concentration when extracting with [Ch]Cl, Table S16: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch]Cl, Table S17: ANOVA data for RuBisCO concentration when extracting with [Ch][Ac], Table S18: ANOVA data for the extraction yield of RuBisCO when extracting with [Ch][Ac]. ...Similar publications
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... The pH of the supernatant (brown juice) was adjusted to 4.5 with 1 M HCl, and Figure 1. Schematic overview of the fractionation processes used to yield LPC TA (leaf protein concentrate obtained after thermal-acid extraction), LPC AA (leaf protein concentrate obtained after alkaline-acid extraction), and LPC 40 , LPC 60 and LPC 80-100 obtained by stepwise precipitation with ammonium sulfate to 40%, 60%, and 80-100% saturation, respectively. ...
... The supernatant (brown juice) was then subjected to protein precipitation using ammonium sulfate. Proteins were fractionated based on their differential solubility at 40, 60 and 80-100% saturation in ammonium sulfate, resulting in three fractions labeled LPC 40 , LPC 60 , and LPC 80-100 . Specifically, brown juice was combined with a calculated mass of ammonium sulfate to achieve 40% saturation, with constant stirring using a magnetic stirrer (700 rpm; IKA RCT basic, IKA-Werke GmbH & Co. KG, Breisgau, Germany) at room temperature for 15 min. ...
... Specifically, brown juice was combined with a calculated mass of ammonium sulfate to achieve 40% saturation, with constant stirring using a magnetic stirrer (700 rpm; IKA RCT basic, IKA-Werke GmbH & Co. KG, Breisgau, Germany) at room temperature for 15 min. The precipitated protein was collected by centrifugation (10,000× g for 10 min at 4 • C), and the resulting pellet was designated as LPC 40 . The supernatant was then subjected to higher ammonium sulfate saturation (60-100%). ...
This study aimed to assess pumpkin leaves as a protein source and determine the feasibility of these proteins to form complexes with alginate for the encapsulation of folic acid. Different isolation protocols, two based on isoelectric precipitation (one with thermal pretreatment and the other with alkali pre-extraction) and one based on stepwise precipitation with ammonium sulfate, were compared regarding the yield and structural properties of the obtained leaf protein concentrates (LPC). The highest purity of protein was achieved using the thermal-acid protocol and the salting-out protocol at 40% saturation. RuBisCO protein was detected by SDS-PAGE in all LPCs, except for the fractions obtained through salting-out at saturation level ≥ 60%. Complexation of the LPC solutions (1 mg/mL) and sodium alginate solution (10 mg/mL) was monitored as a function of LPC:alginate ratio (2:1, 5:1, and 10:1) and pH (2–8) by zeta-potential measurements and confirmed by FT-IR analysis. Based on the results, the strongest interaction between LPCs and alginate occurred at a pH between 2.20 and 2.80 and an LPC:alginate ratio of 10:1. Complexation resulted in particle yields of 42–71% and folic acid entrapment of 46–92%. The LPC-folic acid interactions elucidated by computational protein–ligand docking demonstrated the high potential of RuBisCO as a biocarrier material for folic acid. The in vitro release study in the simulated gastrointestinal fluids indicated that complexes would be stable in gastric conditions, while folic acid would be gradually released in the intestinal fluids.
... Different types of non-animal biomass and respective residues are discussed as protein resources, i.e., algae, plants, fruits, and vegetables. 7,[13][14][15][16][17][18][19][20][21][22][23] The IL-and DES-based approaches for protein extraction and separation overviewed correspond to (i) conventional solid-liquid extraction (SLE), (ii) ultrasoundassisted extraction (UAE), (iii) microwave-assisted extraction (MAE) and (iv) aqueous biphasic systems (ABSs) or aqueous twophase systems (ATPS). Finally, the economic and environmental challenges of using such alternative solvents in industrial applications are discussed, as well as the need of using predicting tools to properly select these solvents. ...
Advances towards the development of a sustainable economy must address the exploitation of bio-based products combined with the use of greener solvents and manufacturing processes that can preserve natural resources and the environment. To address the growing demand for proteins, their production must focus on non-animal sources, such as vegetal biomass and respective residues/waste, and on the use of sustainable solvents and processes for their recovery. This review provides an overview of the advances achieved in the separation and purification processes of proteins from vegetable biomass and respective residues using ionic liquids (ILs) and deep eutectic solvents (DESs) as alternative greener solvents. It begins with an overview of the ability of ILs and DESs to stabilize proteins, followed by the assessment of the extraction and separation of biomass-derived proteins assisted by ILs and DESs. Different types of non-animal biomass and respective residues are considered as protein resources, i.e., algae, plants (e.g., aloe vera and holy basil), cereals (e.g., wheat and oat), fruits (e.g., papaya, pineapple, pomegranate, and seabuckthorn berries), and vegetables (e.g., spinach, radish, and ginger). Several IL- and DES-based approaches are discussed, comprising (i) conventional solid–liquid extraction (SLE), (ii) ultrasound-assisted extraction (UAE), (iii) microwave-assisted extraction (MAE), and (iv) aqueous biphasic systems (ABSs). Finally, the economic and environmental challenges of using such alternative solvents in industrial applications are discussed, including technoeconomic analysis and life cycle assessment.
... Because ultrasound has rarely been used for enzyme extraction and HVED is a relatively new extraction technique, there is a lack of research and results related to the extraction of RuBisCO from sugar beet leaves or other plant sources. Compared to some other methods of extraction, such as extraction with biocompatible ionic liquids (IL), i.e., IL derived from choline and glycine-betaine analogues, US and HVED extractions gave lower yields of RuBisCO [85]. Specifically, by extracting RuBisCO from spinach using choline acetate ([Ch][Ac]) and choline chloride ([Ch]Cl), a yield of 10.92 -10.57 ...
... Lower yields can be attributed to the presence of phenolic compounds, chlorophyll, and some other compounds, which make the extraction of RuBisCO challenging [36]. In general, currently available extraction methods are not sufficiently efficient and selective, resulting in lower amounts of RuBisCO [85]. Furthermore, apart from the extraction conditions, the RuBisCO yield also depends on the sample purification methods [87]. ...
Ultrasound (US) and high voltage electric discharge (HVED) with water as a green solvent represent promising
novel non-thermal techniques for protein extraction from sugar beet (Beta vulgaris subsp. vulgaris var. altissima)
leaves. Compared to HVED, US proved to be a better alternative method for total soluble protein extraction with
the aim of obtaining high yield of ribulose-1,5-bisphosphate carboxylase-oxygenase enzyme (RuBisCO).
Regardless of the solvent temperature, the highest protein yields were observed at 100% amplitude and 9 min
treatment time (84.60 ± 3.98 mg/gd.m. with cold and 96.75 ± 4.30 mg/gd.m. with room temperature deionized
water). US treatments at 75% amplitude and 9 min treatment time showed the highest abundance of RuBisCO
obtained by immunoblotting assay. The highest protein yields recorded among HVED-treated samples were
observed at a voltage of 20 kV and a treatment time of 3 min, disregarding the used gas (33.33 ± 1.06 mg/gd.m.
with argon and 34.89 ± 1.59 mg/gd.m. with nitrogen as injected gas), while the highest abundance of the
RuBisCO among HVED-treated samples was noticed at 25 kV voltage and 3 min treatment time. By optimizing
the US and HVED parameters, it is possible to affect the solubility and improve the isolation of RuBisCO, which
could then be purified and implemented into new or already existing functional products.
... 11 Moreover, aqueous solutions of ILs have shown a remarkable performance in the extraction of a plethora of biomolecules, such as alkaloids and flavonoids from complex sources like biomass. 12 Biomass is a complex matrix, requiring several unit operations to extract and separate high-value compounds. However, there is a high demand to develop integrated extraction-separation processes, or stimuli-responsive ones, combining multiple unit operations that could make them more economical and sustainable. ...
An integrated extraction–separation process: extraction and selective separation of chlorophylls and betalains using thermoreversible aqueous biphasic systems.
Increased incomes, urbanization, and an aging population, are leading to changes in consumption patterns, resulting in a growing demand for proteins. From a sustainability perspective, there is a consensus that animal protein production has a disproportionately impact on the environment, particularly in intensive systems that require significant amounts of feed crops. Macroalgae have emerged as a promising feedstock for transitioning towards a blue bioeconomy. Red seaweed stands out as a particularly attractive action, as it can contain protein concentrations of up to 47 %, the highest among terrestrial plants and other algae divisions. These proteins offer a rich source of essential amino acids, making them excellent candidates for human food formulation. Nevertheless, compared to other major components such as carbohydrates, red macroalgae proteins remain underexploited. This review focuses on the potential of red algae as a protein source within an environmentally friendly biorefinery development strategy, primarily for food and biomedical applications. It also explores the strategies and limitations associated with protein extraction and purification, emphasizing the need for further in vivo and toxicological studies, particularly regarding the digestibility and bioavailability of red algal proteins
Novel series of amide functionalized analogues of glycine-betaine-based ionic liquids (AGB-ILs), namely, [N,N,N-tri(n-butyl)(2-aminoalkyl-2-oxoethyl)]ammonium (alkyl = n-butyl, and n-octyl) associated with tetrafluoroborate (BF4⁻), hexafluorophosphate (PF6⁻), bis(trifluorosulfonyl)imide (Tf2N⁻), dicyanamide (Dca⁻), 4-chlorosalicylate (ClSal⁻) and saccharinate (Sac⁻) anions have been synthesized and fully characterized by spectroscopic methods (IR, ¹H and ¹³C NMR) and elemental analysis. The alkyl chain length of the organic cation and the chemical nature of inorganic or organic anion on physicochemical properties such as melting point, glass transition and decomposition temperatures, and viscosity have been described. To determine the sensitivity of Dca ligand to pH, the effect of the pH on the extraction properties of aqueous Cu²⁺ and Co²⁺ ions has been investigated in the pH range 1 to 5.
Ionic liquids (ILs) and deep eutectic solvents (DESs) debuted with a promise of a superior sustainability footprint due to their low vapor pressure. However, their toxicity and high cost compromise this footprint, impeding their real‐world applications. Fortunately, their property tunability through a rational selection of precursors, including bioderived ones, provides a strategy to ameliorate toxicity, lower cost, and endow new functions. This Review discusses whether ILs and DESs are sustainable solvents and how they contribute to sustainable chemical processes.
Ionic liquids (ILs) and deep eutectic solvents (DESs) debuted with a promise of a superior sustainability footprint due to their low vapor pressure. However, their toxicity and high cost compromise this footprint, impeding their real‐world applications. Fortunately, their property tunability through a rational selection of precursors, including bioderived ones, provides a strategy to ameliorate toxicity, lower cost and endow new functions. This review addresses whether ILs and DESs are sustainable solvents or contribute to sustainable chemical processes.
