Screening of ionic liquids for the extraction of proteins
from the macroalga Ulva lactuca aiming at an
integrated biorefinery approach
Carlota Fernandes Miranda
Thesis to obtain the Master of Science Degree in
Biotechnology
Supervisors:
Dr. Corjan van den Berg
Dr. Maria Teresa Ferreira Cesário Smolders
Examination Committee
Chairperson: Dr. Helena Maria Rodrigues Vasconcelos Pinheiro
Supervisor: Dr. Maria Teresa Ferreira Cesário Smolders
Members of the Committee: Dr. Maria Catarina Marques Dias de Almeida
November 2017
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Preface The work described in this document was performed under the framework of the curricular
course Master’s Thesis in order to obtain the Master’s degree in Biotechnology at Instituto
Superior Técnico.
All the research work was carried out, from February to July 2017, in the Bioprocess
Engineering Group, part of the Agrotechnology and Food Sciences Group from Wageningen
University and Research (Netherlands), under the supervision of Dr. Corjan van den Berg
(assistant professor) and the PhD student Edgar Suarez Garcia. Dr. Ma Teresa Cesário (post-doc
researcher), from the Bioengineering Department, was the internal supervisor at Instituto Superior
Técnico.
This dissertation consists of a literature review of the research topic under study,
description of the materials and methods used, obtained results and respective discussion.
Finally, conclusions and future prospects are also reported.
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Acknowledgements
The limited space of this acknowledgment section does not allow me to thank, as I should,
to all of the people that, in the last months, direct or indirectly, helped me fulfilling my expectations
and achieving my goals, finalizing such important phase of my academic life. Hence, I leave here
only a few words, but in a very felt and thankful way.
Firstly, I would like to thank Professor Mª Manuela Fonseca and Dr. Mª Teresa Cesário for
giving me the opportunity to do my master’s dissertation abroad and Dr. Corjan van den Berg for
integrating me in his research group. I believe that the technical and social resources I had
available in Wageningen University and Research were plenty for being able to perform an
excellent work. Furthermore, the six months I spent in the Netherlands highly contributed to my
personal development, and, despite not being an easy journey, I truly believe that my limits were
pushed further, and that now I can do more and better.
To Edgar Suarez, I genuinely thank for the orientation, support, opinions and critics, that
improved my scientific knowledge and, undoubtedly, stimulated my wish of knowing more and
more, and the will to do better. Your vast knowledge in the field and your availability to advise me
and revise my work were crucial to the success of this dissertation project.
I would like to mention again Dr. Mª Teresa Cesário, for constantly keeping up with my
work, always showing an availability to discuss results and suggest new strategies. Thank you
also for having reviewed, in detail, this master’s dissertation report.
I am also eternally grateful to Ana Beatriz Cardoso, my partner on this journey and without
whom this would not have been possible. Thank you for the unconditional patience and support,
for the true friendship, for being my family during the months I have been away from home. I will
never be able to thank you enough for everything you have done for me.
Last, but definitely not the least, I would like to thank to my family, especially my mother,
Luísa, my father, Vasco, my brother, Tomás and my sister, Margarida. Thank you for always
walking by my side - even 2000 km apart from me -, for your unconditional support and
comprehension and for all the sacrifices you made for me. Thank for believing in me more than I
believe in myself, for always pushing me to do better and be better, and for all life teachings. I
could not end without mentioning my grandparents, my aunt Teresa and Dr. Carla, for the
constant encouragement, so to successfully finish this great challenge.
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Abstract
Alternative protein sources and production methods are required to fulfil the consumer
needs and to meet the predicted increase of global protein demand. Although seaweeds are
recognized as a valuable source of protein, new techniques for the extraction and separation of
these biomolecules, under mild and stabilizing conditions, are required. Ionic liquids, due to their
unique properties, have been considered promising alternatives for the recovery of value-added
compounds from different sources of biomass.
The green macroalga Ulva lactuca was used in this study. It was composed of 96.50±0.87%
of total solids, of which 22.80±0.35% was ashes, 4.95±0.35% lipids, 17.81±0.79% proteins and
60.92±14.62% carbohydrates. Three methods commonly used for macroalgal protein extraction
were used as reference cases. Extraction by means of aqueous and alkaline solutions
(sequential), mechanical grinding under alkaline conditions and aqueous biphasic system
(PEG/Na2CO3) led to extraction efficiencies of 49.08±6.66%, 6.68±1.09% and 10.52±0.35%,
respectively.
Twenty-five ionic liquids were screened towards the selection of the most suitable for the
disintegration of Ulva lactuca cellular structure. 1-ethyl-3-methylimidazolium dibutyl phosphate
showed the best results, allowing the recovery of 80.62±4.95% of the total protein. Aiming at an
integrated biorefinery approach, an aqueous three-phase partitioning system (1-ethyl-3-
methylimidazolium dibutyl phosphate/K2HPO3) is further proposed as a platform for the single-
step cell disintegration and protein-carbohydrate fractionation. Proteins and carbohydrates are
mainly concentrated in the top and inter- phase, respectively.
Finally, ultrafiltration was evaluated for the recovery of ionic liquid from macroalgal extracts,
yielding recoveries of ~80% of ionic liquid and ~90% of proteins and carbohydrates.
Keywords
Seaweed | Ionic liquid | Integrated biorefinery | Protein | Carbohydrate | Aqueous biphasic
system
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Resumo
Para atender às necessidades dos consumidores e ao previsto aumento global da sua
procura, são necessárias novas fontes de proteína e métodos para a sua produção. Apesar das
macroalgas serem reconhecidas como potenciais fontes de proteína, é preciso desenvolver
novas técnicas para a extração/separação destas biomoléculas, em condições suaves e
estabilizadoras. As propriedades únicas dos líquidos iónicos tornam-nos alternativas promissoras
para recuperar compostos de valor agregado de diferentes fontes de biomassa.
A alga verde Ulva lactuca, usada neste estudo, era composta por 96.50±0.87% de sólidos
totais, dos quais 22.80±0.35% era cinzas, 4.95±0.35% lípidos, 17.81±0.79% proteínas e
60.92±14.62% carboidratos. Três métodos convencionais para a extração de proteínas das
macroalgas foram usados como casos de referência. A extração através de soluções aquosas e
alcalinas (sequencial), de moagem mecânica em condições alcalinas e de um sistema aquoso
bifásico (PEG/Na2CO3) conduziram a eficiências de extração de 49.08±6.66%, 6.68±1.09% e
10.52±0.35%, respetivamente.
Rastrearam-se vinte-cinco líquidos iónicos a fim de selecionar o mais adequado para
desintegrar a estrutura celular da Ulva lactuca. O 1-etil-3-metilimidazol dibutilfosfato mostrou o
melhor resultado, extraindo 80.62±4.95% da proteína total. Visando uma estratégia de
biorrefinaria integrada, é ainda proposto um sistema aquoso de partição trifásico (1-etil-3-metil-
imidazol dibutilfosfato/K2HPO3) como uma plataforma de passo único para a desintegração
celular e separação de proteínas-carboidratos. As proteínas e os carboidratos ficam
concentrados principalmente na fase superior e interfase, respetivamente.
Finalmente, avaliou-se a recuperação do líquido iónico por ultrafiltração a partir dos
extratos algáceos, alcançando-se taxas de recuperação de ~80% de líquido iónico e ~90% de
proteínas/carboidratos.
Palavras-Chave
Alga marinha | Líquido iónico | Biorrefinaria integrada | Proteína | Carboidrato | Sistema
aquoso bifásico
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Contents
Acknowledgements ....................................................................................................................... v
Abstract ........................................................................................................................................ vii
Keywords ...................................................................................................................................... vii
Resumo ......................................................................................................................................... ix
Palavras-Chave ............................................................................................................................. ix
Contents ........................................................................................................................................ xi
List of Figures .............................................................................................................................. xiii
List of Tables ................................................................................................................................ xv
Abbreviations .............................................................................................................................. xvii
1. Introduction ........................................................................................................................ 1
1.1 Context ...................................................................................................................... 3
1.2 Objectives .................................................................................................................. 5
2. State of the Art ................................................................................................................... 7
2.1 Biorefinery concept .................................................................................................... 9
2.1.1 Aquatic biomass .............................................................................................. 10
2.2 Macroalgal backgrounds ......................................................................................... 11
2.2.1 Macroalgae as a source of protein .................................................................. 12
2.3 Extraction of macroalgal proteins ............................................................................ 13
2.4 Ionic liquids as new solvents ................................................................................... 16
2.4.1 Ionic liquids for macroalga cell disintegration .................................................. 17
2.4.2 Ionic liquid-based aqueous biphasic systems for biomolecules fractionation . 18
2.4.3 Ionic liquid recovery ......................................................................................... 22
2.5 Macroalgal proteins applications ............................................................................. 24
2.5.1 Macroalgal bioactive proteins .......................................................................... 25
2.5.1.1 Lectins ......................................................................................................... 25
2.5.1.2 Phycobiliproteins ......................................................................................... 26
2.5.2 Macroalgal bioactive peptides ......................................................................... 27
3. Aim of studies .................................................................................................................. 29
4. Materials and Methods .................................................................................................... 33
4.1 Sample collection and preparation .......................................................................... 35
4.2 Biomass characterization ........................................................................................ 35
4.2.1 Total solids and ash......................................................................................... 35
4.2.2 Total lipid content ............................................................................................ 36
4.2.3 Total carbohydrate content .............................................................................. 37
4.2.4 Total protein content ........................................................................................ 37
4.3 Conventional methods for protein extraction ........................................................... 37
4.3.1 Aqueous and alkaline extraction ..................................................................... 37
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4.3.2 Extraction by high-shear force under alkaline conditions ................................ 38
4.3.3 Aqueous biphasic system PEG/Na2CO3 ......................................................... 38
4.4 Assays with ionic liquids .......................................................................................... 39
4.4.1 Ionic liquid selection ........................................................................................ 39
4.4.2 Aqueous biphasic system: binodal curve ........................................................ 40
4.4.3 Bead mill experimental setup .......................................................................... 40
4.5 Recovery of ionic liquid............................................................................................ 41
4.6 Analytical methods .................................................................................................. 43
4.6.1 Protein quantification ....................................................................................... 43
4.6.2 Carbohydrate quantification ............................................................................ 43
4.6.3 Ionic liquid quantification ................................................................................. 44
4.6.4 Native-PAGE gel electrophoresis .................................................................... 44
4.7 Statistical Analysis ................................................................................................... 45
5. Results and Discussion ................................................................................................... 47
5.1 Chemical composition of Ulva lactuca ..................................................................... 49
5.2 Reference Cases ..................................................................................................... 52
5.3 Experiments with ionic liquids ................................................................................. 56
5.3.1 Ionic liquid selection ........................................................................................ 56
5.3.2 Extraction and fractioning of protein by IL based-ATPS ................................. 60
5.4 Ionic liquid recovery ................................................................................................. 63
6. Conclusions and future prospects ................................................................................... 65
References .................................................................................................................................. 71
Appendix A Protein, carbohydrate and ionic liquid quantification ......................................... A-1
A.1. Protein Quantification ................................................................................................. A-3
A.2. Carbohydrates Quantification ..................................................................................... A-4
A.3. Ionic liquid Quantification ........................................................................................... A-4
Appendix B Binodal Curve .................................................................................................... B-1
B.1. Binodal curve correlation ............................................................................................ B-3
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List of Figures
Figure 2.1 | Macroalga biomass refinery scheme. Adapted from (IEA, 2016a) .......................... 14
Figure 2.2 | Intra- and intermolecular hydrogen bonds in cellulose. Adapted from (Pinker et al.,
2009). .......................................................................................................................................... 17
Figure 2.3 | Proposed dissolution mechanism of cellulose in ionic liquid 1-Butyl-3-
methylimidazolium chloride ([BMIM][Cl]). Adapted from (Pinker et al., 2009). ........................... 18
Figure 2.4 | Phase diagram for a hypothetical system composed of ionic liquid + salt + water
(weight fraction percentage). TCB: Binodal curve; C: Critical point; TB: Tie-line; T: Composition
of the top phase; B: Composition of the bottom phase; X, Y and Z: total composition of the ATPS.
Adapted from (Raja et al., 2012). ................................................................................................ 19
Figure 2.5 | Ordering of the ions and related properties in the Hofmeister series (Zhang and
Cremer, 2006). ............................................................................................................................ 22
Figure 2.6 | Schematic representation of ultrafiltration. Adapted from (Van Der Bruggen et al.,
2003). .......................................................................................................................................... 23
Figure 3.1 | Seaweed Ulva lactuca (Krisp, 2011). ....................................................................... 31
Figure 3.2 | Distribution of the different Ulva sp. cell wall polysaccharides in a schematic cross
section of a thallus (A) and the proposed associations between the different cell wall
polysaccharides (B). Adapted from: (Lahaye and Robic, 2007). ................................................ 31
Figure 4.1 | DYNO-mill equipment and respective components. ................................................ 41
Figure 4.2 | Schematic diagram of stirred cell ultrafiltration assembly used for recovering the ionic
liquid from the supernatant. Adapted from (Merck Millipore, 2015). ........................................... 42
Figure 5.1 | Percentage of protein recovery by high-shear force, aqueous and alkaline extraction
(sequential) and aqueous biphasic system PEG/Na2CO3 (sum of protein extracted into top and
bottom phases). Protein yield expressed as % of total protein (Protein extracted/Total proteins x
100). Results are the means of triplicate determination for aqueous and alkaline extraction and
duplicate determinations for high-shear force extraction and aqueous biphasic system ±SD; (*) -
means are statistically equal (p>0.05). ........................................................................................ 52
Figure 5.2 | Triplicates of positive controls (algae and milliQ water), negative controls (milliQ
water, PEG1000 and Na2CO3) and ATPS formed by milliQ water + PEG1000 + Na2CO3 + algae.
..................................................................................................................................................... 54
Figure 5.3 | Distribution of extracted proteins and carbohydrates between the top and bottom
phases of the ATPS composed by PEG1000 + water + Na2CO3. .............................................. 54
Figure 5.4 | Electrophoresis gel of protein extracts under non-denaturing conditions. Lane 1:
Protein ladder; Lane 2: Aqueous extract (positive control); Lane 3: Alkaline extract; Lane 4:
Mechanical grinding extract. ........................................................................................................ 55
Figure 5.5 | Suspensions obtained for each ionic liquid after mixing IL + water + algae. A positive
control (+ Ctrl) – algae and water – is placed next to all the samples. ....................................... 57
Figure 5.6 | Percentage of protein recovery by each of the 5 ILs tested (+ control – algae and
water). Protein yield expressed as % of total protein (Protein extracted/Total proteins x 100).
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Results are the means of duplicates for each IL and quadruplicates for positive control ±SD. a, b,
c – means in columns with the same letter are significantly equal (p>0.05). .............................. 59
Figure 5.7 | Binodal curve of [Emim][dbp] and K2HPO4 at 298.15 K. .......................................... 60
Figure 5.8 | ATPS ([Emim][dbp]/K2HPO4) created after biomass processing in bead mill. ......... 61
Figure 5.9 | Distribution of extracted proteins and carbohydrates between the bottom, inter- and
top phases of the aqueous system composed by [Emim][dbp] and K2HPO4. ............................. 61
Figure 5.10 | Recovery of [Emim][dbp] in filtrates and retentates, after ultrafiltration through stirred
ultrafiltration cell and centrifugal filter unit. Results are the means of duplicates ±SD. .............. 63
xv
List of Tables
Table 2.1 | Conventional pre-treatment cell disruption methods and extraction processes for the
isolation of macroalgal proteins. (*) Protein yield expressed as % of total protein (Protein
extracted/Total proteins x 100). ................................................................................................... 15
Table 2.2 | Macroalgal source species and bioactivity of lectins. ............................................... 26
Table 2.3 | Examples of macroalgal peptides exhibiting biological activity. ................................ 27
Table 2.4 | Examples of macroalgal protein hydrolysates exhibiting biological activity. ............. 28
Table 4.1 | Composition of 1 L phosphate buffer saline. ............................................................. 36
Table 4.2 | Reagents and respective purity and supplier used in lysis buffer preparation. ......... 37
Table 4.3 | Ionic liquids tested and respective purity and supplier. ............................................. 39
Table 4.4 | Filtrate and retentate dilutions required for protein, carbohydrate and ionic liquid
quantification. .............................................................................................................................. 42
Table 5.1 | Chemical characteristics of macroalga U. lactuca (% w/w on dry basis). ................. 49
Table 5.2 | Protein content in Ulva lactuca. ................................................................................. 50
Table 5.3 | Ulva lactuca composition determined by thermogravimetric analysis. ..................... 51
Table 5.4 | Ionic liquids selected and respective anion hydrogen bond basicity. ....................... 56
Table 5.5 | Ionic liquids and estimated costs. ............................................................................. 58
Table 5.6 | Output of the third selection. Features of the five ionic liquids selected. .................. 59
Table 5.7 | Rejection coefficients of proteins and carbohydrates for each ultrafiltration setup.
Results are means of duplicates ±SD. ........................................................................................ 63
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Abbreviations
[1,4,4,4-P][MSO4] tributyl(methyl)phosphonium methylsulfate
[6,6,6,14-P][Cl] trihexyltetradecylphosphonium chloride
[6,6,6,14-P][dca] trihexyltetradecylphosphonium dicyanamide
[Bmim][Ac] 1-butyl-3-methylimidazolium acetate
[Bmim][Cl] 1-butyl-3-methylimidazolium chloride
[Bmim][dbp] 1-butyl-3-methylimidazolium dibutyl phosphate
[Bmim][dca] 1-butyl-3-methylimidazolium dicyanamide
[Ch][Ac] Choline acetate
[Ch][Cl] Choline chloride
[Emim][dbp] 1-ethyl-3-methylimidazolium dibutyl phosphate
[P]B Concentration of protein in bottom phase
[P]T Concentration of protein in top phase
AA(s) Amino Acid(s)
ACE Angiotensin Converting Enzyme
AHBB Anion Hydrogen Bond Basicity
AIDS Acquired Immune Deficiency Syndrome
ANOVA Analysis of Variance
ATPS(s) Aqueous Two-Phase System(s)
B Bottom phase
BIL Concentration of ionic liquid in the bottom phase
BPE Bioprocess Engineering
BSA Bovine Serum Albumin
Bsalt Concentration of salt in the bottom phase
CAB Concentration of component A in the bottom phase
CAT Concentration of component A in the top phase
Cf Feed concentration
Cp Permeate concentration
DEA Donor-Electron Acceptor
DW Dry Weight
EAA(s) Essential Amino Acid(s)
xviii
FAO Food and Agriculture Organization
g Centrifugal force
GHG Greenhouse Gases
HIV Human Immunodeficiency Virus
HSD Tukey’s Honest Significant Difference
IEA International Energy Agency
IL(s) Ionic Liquid(s)
K Partition coefficient
M Mixture
p p-value
PEG Polyethylene Glycol
R Rejection coefficient
sB Starting Biomass
SD Standard deviation
SDS Sodium Dodecyl Sulphate
sp Species
STL Slope of the Tie-Line
T Top phase
TGX Tris-Glycine eXtended
TIL Concentration of ionic liquid in the top phase
TL(s) Tie-Line(s)
TLL Tie-Line Length
TPP Three-Phase Partitioning
Tris Tris(hydroxymethyl)aminomethane
Tsalt Concentration of salt in the top phase
U. lactuca Ulva lactuca
ULPC Ultra High-Performance Liquid Chromatography
UNU United Nation University
Vf Final volume
w/w Weight fraction
WHO World Health Organization
wt Weight
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1
1 1. Introduction
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1.1 Context
According to the United Nations, the current world population of 7.3 billion is expected to
reach 9.7 billion in 2050 (FAO, 2017), requiring an estimated 70% increase in food production
(FAO, 2009). In addition, the economic development and the rising in living standards, will result
in changes in food consumption patterns.
It is undeniable that proteins take a crucial role in the human diet. Its global demand is
driven not only by the population increase and socio-economic changes, but also by the growing
recognition of protein role in a healthy diet, particularly for an aging population (Henchion et al.,
2017).
Predictions for protein requirements show that the world demand for animal-derived protein
will have to triple by 2050 (Westhoek et al., 2011), resulting in sustainability and food security
concerns. It is proved that animal-based foods produce higher levels of greenhouse gases (GHG)
than plant-based. These emissions come from, for instance, livestock, manure, feed production
and land conversion (Westhoek et al., 2011). In particular, livestock sector accounts for 14.5% of
all human induced GHG emissions (Gerber et al., 2013), being responsible for substantial
emissions of nitrogen in various forms (ammonia, nitrates), which in turn leads to losses of
terrestrial and aquatic biodiversity (Leip et al., 2015; Westhoek et al., 2011).
Also, the oceans, which are one of the largest sources of protein (fish and seafood), are
reaching the limit of their resources. In 2013, about 89.5% of the global fish stocks were fully or
over exploited (FAO, 2014a), leading to severe consequences for marine ecosystems. Farmed
fish is likely to meet the needs of an ever-growing population; forecasts say it will account for 2/3
of global supply by 2030 (The World Bank, 2013). Nevertheless, it is fast reaching its limit since
farmed fish is heavily dependent on wild caught fish for feed (fishmeal) (FAO, 2014a). Alternative
protein sources and production methods are therefore required to fulfil the demand of consumers
and to meet predicted global protein requirements.
Macroalga, the so called “seaweed”, represents a promising and novel future protein
source. Some species of seaweed, like the red seaweed Porphrya sp., have been shown to
contain levels of protein up to 47% dry weight (wt.) (Marsham et al., 2007). In some cases,
macroalgae can be richer than some conventional protein-rich foods, such as soybean (40%
protein), cereals (15% protein), eggs (9% protein) and fish (25% protein) (Harnedy and
FitzGerald, 2013). In addition, the production of proteins from macroalgae has advantages over
traditional sources in terms of productivity. Macroalgae have higher protein yield per unit area
(2.5-7.5 tons.Ha-1.year-1) compared to terrestrial crops, such as soybean, vegetable seeds, and
wheat (0.6-1.2 tons.Ha-1.year-1, 1-2 tons.Ha-1.year-1, and 1.1 tons.Ha-1.year-1, respectively)
(Krimpen et al., 2013).
Approximately 70% of the total global freshwater is used for agriculture (FAO, 2016) with
animal-based protein production requiring 100 times more water than producing an equivalent
amount of protein from plant sources (Pimentel and Pimentel, 2003). Marine algae do not require
freshwater or arable land to grow, maximising resources that can be used for additional food
production or other cash crops (Bleakley and Hayes, 2017).
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Although seaweeds can be considered a potential source of proteins, their widespread use
is limited by the availability of scalable production methods for protein isolation. The current
processes are time-consuming, economically unviable and may still compromise the protein
functionality due to the severe conditions applied (Wijffels and Barbosa, 2010). New scalable
biorefining processes for the extraction and separation of macroalgal proteins, which ensure high
recovery yields without compromising the extracts functionality, need to be developed.
Besides being promising to meet the future protein demand, macroalgae are also useful
for bioenergy production based on the carbohydrate fraction. A single-unit operation for cell
disruption and component fractionation – integrated biorefinery – that allows for the isolation of
different macroalgal fractions, is essential for the valorisation of macroalgae as feedstocks (IEA,
2017, 2016a).
Recently, the unique properties of ionic liquids (ILs) have triggered the interest of scientific
community as promising solvents for the extraction and separation of biomacromolecules from
biomass, under mild and stabilizing conditions (MacFarlane and Tan, 2010). Although research
on macroalga cell disruption using these novel solvents is limited to date (Malihan et al., 2014;
Pezoa-Conte et al., 2015), their use for the disintegration of lignocellulosic material and algae has
been already proven for several feedstocks (Brandt et al., 2013; Kim et al., 2012). The use of ILs
is easily scaled up to industrial processes (Socha et al., 2014), therefore, this study can result in
a new integrated biorefinery approach for macroalgal protein valorisation.
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1.2 Objectives
The main goal of this experimental work was the screening of ionic liquids for the extraction
of proteins from the green macroalga Ulva lactuca. For that, the following objectives should be
met:
1. Biomass characterization in terms of total solids, ash, lipid, carbohydrate and protein
content.
2. Replication of conventional methods, reported in literature, for the extraction of
protein from macroalgae.
3. Screening of ionic liquids for the disintegration of U. lactuca cell wall and the
consequent protein extraction.
4. Development of an aqueous two-phase system based on the most promising ionic
liquid aiming at an integrated fractionation of biomolecules.
5. Study the recovery of ionic liquid from the macroalgal extracts;
6. Perform an economical evaluation of the proposed integrated biorefinery approach.
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2 2. State of the Art
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2.1 Biorefinery concept
In the long run, our current way of life is unsustainable. Our consumption and waste levels
cannot be maintained without decreasing the possibilities for next generation. Our usage of
ecosystem services is at such level that the earth is unable to regenerate them at the same pace
(Eickhout, 2012).
We live in a fossil-based economy, in which fossil resources are used for all sorts of
everyday applications, such as electricity, cooling and heating, fuels, materials and chemicals.
They have been used at unprecedented rates (Höök and Tang, 2013) and it is becoming evident
that our economies are unsustainable given that the fossil resources are dwindling and set to get
more expensive. In addition to the non-renewable nature of these resources, their over-
consumption also contributes to serious environmental issues. In 2016, the energy sector
accounted for 68% of the GHG emissions, the main cause of global warming (IEA, 2016b).
The world needs feedstocks that are widely available, relatively inexpensive, renewable
and that can be grown and processed in a sustainable manner. It is undeniable that several sorts
of biomass can fulfil these requirements. The conversion of biomass to energy carriers and a
range of useful products, including food and feed, can be carried out in multi-product biorefineries
(IEA, 2009).
The International Energy Agency (IEA) Bioenergy Task 42 defines biorefinery as the
“sustainable and synergetic processing of biomass into marketable food and feed ingredients,
products (chemicals, materials) and energy (fuels, power, heat)”, which includes systems that
may exist as a concept, a facility, a process, a plant, or even a cluster of facilities (IEA, 2014).
A biorefinery is the integral upstream, midstream and downstream processing of biomass
into a range of products (IEA, 2009). The concept of “integrated biorefinery” describes the main
goal of biorefineries, in which biomass can be used in an optimal way, leading to a longer lifespan
of the resource/product, and thereby increasing its efficient and effective application. Under this
system, several conversion technologies are integrated and designed to maximize the valuable
components, while minimizing the waste streams and processing steps. Biomass is separated
into fractions: the valuable molecules are processed into high-value products such as chemicals
and materials, while the lower quality fractions – side streams – can be used to produce fuels or
to energy recovery (Fernando et al., 2006).
A biorefinery can use all kinds of biomass including forest resources, agricultural crops
such as switchgrass, corn and soybeans, organic residues (both plant and animal-derived, and
industrial and municipal wastes) and aquatic biomass (microalga and seaweed). The major
components of these biomasses include carbohydrates (cellulose and hemicellulose for crop
residues, forage crops and woody crops, starch for grain, and primarily sucrose for sugar crops),
lipids (fats, waxes and oils), proteins, aromatic compounds (primarily lignin), and ash (non-carbon
minerals such as calcium, phosphorus, potassium, etc.) (Dale and Kim, 2008).
The development of biorefineries represents the access key to an integrated production of
food, feed, chemicals, goods, and fuels to meet the future demands.
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2.1.1 Aquatic biomass
The main driver for the establishment of biorefineries is sustainability, which implies that
biorefinery products should be produced without impacting the economy and ecosystems from
the life cycle perspective (Jung et al., 2013). Therefore, the feedstock supplies must conform to
these parameters.
The use of agricultural substrates – corn, soybean, or sugar cane – to feed biorefineries
(mainly to produce biofuels) is involved in controversial debates due to the “food vs energy”
dilemma. Crop biomass is traditionally destined for food and feed applications and its use as
feedstock competes with both purposes for land, water and other resources, which raises the
question of social sustainability (Fernand et al., 2017).
The use of non-food biomass, such as crop residues (corn stover, manure, straw, waste
wood) and energy crops cultivated on non-arable lands (miscanthus, poplar, willow, switchgrass),
is not in confrontation with food and feed availability. However, their use is not cost effective due
to the abundance of lignin in the composition of these biomasses. Several expensive pre-
treatment steps are required to disintegrate these matrices in order to convert such substrates
into biofuels and bioproducts (Sambusiti et al., 2015).
Although woody biomass is considered a potential feedstock option for biorefineries, its
composition is similar to agricultural residues, meaning that the expensive pre-treatment steps
are still required. In addition, getting this kind of biomass from forests, which are rich in biodiversity
and delicate ecosystems, go against the idea of sustainability (Ghatak, 2011).
Aquatic biomass – microalgae and seaweeds – seems to circumvent the concerns related
with terrestrial biomass. Alga production does not affect the conventional agriculture because
these organisms do not need land or fresh water once they have the ability to grow under harsh
conditions like saline, brackish and coastal seawater (Behera et al., 2015). In addition, aquatic
biomass has a lower risk for the competition for food than other crops, since this application is
important in few East Asia countries (Jung et al., 2013). Other macroalgal uses are for
hydrocolloid extraction, application as fertilizer, and animal feed. It should be noted that
macroalgae do not include lignin in their composition (Saqib et al., 2013) because they do not
need to stand rigidly in the water. This allows the processing of algae in relatively mild conditions
comparing with lignocellulosic biomass. Lower temperatures, less severe acid conditions, and
shorter reaction times are generally needed (Van Hal et al., 2014).
Algae can grow through the year, consequently they have a short harvesting cycle in
comparison to other conventional crops, which have harvesting cycles of once or twice in a year
due to the seasonal variations (Chisti, 2008; Schenk et al., 2008). Moreover, they can convert
solar energy into chemical energy with higher photosynthetic efficiency (6-8%) than terrestrial
biomass (1.8-2.2%) (Ross et al., 2009), therefore they can generate and store higher amounts of
carbon and lipid resources, which can be exploited in biomaterial and bioenergy production.
Amongst aquatic biomass, the use of macroalgae in a bio-based economy is more
advantageous than the use of microalgae because seaweeds show higher volumetric production
rates (biomass per volume per time) and biomass densities (Van Hal et al., 2014).
11
2.2 Macroalgal backgrounds
Seaweeds are the most important organisms in marine ecosystem for the preservation of
marine bio-resources and seawater quality by preventing pollution and eutrophication, as well as
absorbing and fixating CO2 aided by solar energy (Notoya, 2010).
Macroalgae are multicellular organisms and belong to the lower plants, consisting of a leaf-
like thallus instead of roots, stems, and leaves (Cavalier-Smith, 2007; Lobban and Wynne, 1983).
They are photoautotrophic organisms, therefore, produce and store organic carbon by the
conversion of CO2 and HCO3 (Gao and McKinley, 1994). Their photosynthetic rates vary
according to the species but are usually higher than those of terrestrial biomass, like corn and
switchgrass (Chung et al., 2011).
The pigment content, growth rates, and chemical composition of macroalgae are
significantly affected by their habitat conditions such as light, temperature, salinity, nutrient,
pollution and even water motion (Lobban and Wynne, 1983). Light represents the most important
factor affecting pigmentation, allowing to classify macroalgae into Phaeophyta (brown),
Rhodophyta (red) and Chlorophyta (green) classes (Chan et al., 2006). According to the
respective pigments, algae are able to selectively absorb light with specific wavelengths. For
instance, some red algae are able to grow in the deep sea (over 25 m below the surface)
(Santelices, 1991).
In terms of chemical composition, macroalgae have high contents of carbohydrates (20-
60%) (Jung et al., 2013), minerals (10-50% dry wt.) (Ross et al., 2008) and proteins (7-47% dry
wt.) (Jensen, 1993; Marsham et al., 2007); lipids are also present but in very small quantities (1-
5% dry wt) (Jensen, 1993).
The content and specific carbohydrate composition vary significantly among macroalga
classes. As recently reviewed by Jung et al. (Jung et al., 2013), green algae (25-50% dry wt.
carbohydrates) are mainly composed of mannan, ulvan, starch (i.e., α-1,4-glucan) and cellulose.
The proportion of starch is very small (1-4%) and ulvan, which major components are D-
glucoronic acid, D-xylose, L-rhamnose, and sulfate, is a distinctive feature of green algae.
The main polysaccharides of red algae are carrageenan (up to 75% dry wt.), which consists
of repeating D-galactose units and anhydrogalactose, and agar (up to 52% dry wt.), made up of
alternating β-D-galactose and α-L-galactose, with scarce sulfations (Jung et al., 2013). Purified
carrageenan from red algae is generally used to form thick solutions or gels, for instance, it is
used in air freshener gels, toothpaste, firefighting foam, shampoo, cosmetic creams, as food
additives for dairy products, and also in biotechnology as a gel to immobilise cells/enzymes
(Necas and Bartosikova, 2013). In turn, agar is applied in food, pharmaceutical and biological
industries (e.g. it is used as food ingredients, as laxative in pharmaceutical industry, and as solid
media for microbial growth) (McHugh, 2003).
Finally, brown algae are mainly composed of alginic acid (i.e., alginate), laminarin,
fucoidan, cellulose and mannitol. Alginate, composed of mannuronic and guluronic acid blocks,
is the principal constituent of the cell wall (up to 40% dry wt.) and is mostly used in textile (50%)
12
and food (30%) industries (McHugh, 2003). Laminarin, composed of β-1,3-glucan units, accounts
to 35% dry wt. of brown algae (Jung et al., 2013).
Almost 95% of seaweeds used by humans is a result of cultivation activities (FAO, 2014b,
2014c). Seaweed farming is practised in about 50 countries and it expanded 8% per year in the
past decade (FAO, 2014a). Indonesia is the major contributor for aquatic plant production growth,
with an increase in annual farmed seaweeds output by more than 10 times, from less than a
million tonnes in 2005 to 10 million tonnes in 2014 (FAO, 2014a). This impressive expansion is
mainly due to the vast areas of sunlit shallow sea, which are suitable for culture sites of the tropical
seaweed Kappaphycus alvarezii and Eucheuma sp. (FAO, 2014a). In addition to these species,
Laminaria japonica, Gracilaria sp., Undaria pinnatifida and Porphyra sp are also the most
commonly farmed. (FAO, 2014a).
In 2014, 28.5 million tonnes of seaweeds were harvested for direct consumption or further
processing for food (traditionally in Japan, the Republic of Korea and China), fertilizers,
pharmaceuticals, cosmetics and other purposes (FAO, 2014a).
Although the new developments in which seaweeds are considered potential feedstocks
for bioenergy production, it is shown that no commercial process can be developed only based
on the generation of bioenergy (IEA, 2017). However, the maximization of the inherent value of
all macroalgal components in integrated biorefinery approaches could make the process
economically viable.
Growing attention is also focused on the nutritional value of several seaweed species due
to the high content of natural vitamins, minerals, and plant-based proteins. Macroalgae have
shown to provide a rich source of natural bioactive compounds with anti-viral, anti-fungal, anti-
bacterial, anti-oxidant, anti-inflammatory, hypercholesterolemia, hypolipidemic and anti-
neoplastic properties (Plaza et al., 2008).
Recently, different food and drink products containing seaweed flavours have been
commercialized. The majority of seaweed-flavoured food and drink products are currently
launched in the Asia Pacific region, accounting for 88% of global product launched between 2011
and 2015, Europe launched 7% in this time, outpacing both North America (4%) and Latin
America (1%) (Mintel, 2016).
2.2.1 Macroalgae as a source of protein
The protein content and amino acid (AA) composition in seaweeds vary with species and
season (Harnedy and FitzGerald, 2013). The highest protein levels are commonly found during
the period of winter-early spring and the lowest during summer-early autumn (Galland-Irmouli et
al., 1999; Khairy and El-Shafay, 2013; Rouxel et al., 2001). Such fluctuations have been related
to several variables, including nutrient supply, temperature, available light and salinity (Harnedy
and Fitzgerald, 2011). Generally, the protein content is low in brown seaweeds (3-25% dry wt.),
moderate in green algae (9-26% dry wt.) and high in red seaweeds (<47% dry wt.) (Fleurence,
2004).
The bioactive properties of a protein can be determined essentially by the content,
proportion and availability of its amino acids (Millward et al., 2008). The World Health Organization
13
(WHO), Food and Agricultural Organization of the United Nations (FAO) and the United Nation
University (UNU) have been focused on the energy and nutritional needs of the world population
and have produced recommendations on fundamental nutritional requirements, including the
estimation of indispensable amino acid demand (reference pattern of protein in diet)
(WHO/FAO/UNU, 2007).
The comparison between the amino acid composition in seaweeds, reference pattern
(WHO/FAO/UNU, 2007) and other food-proteins, give us a first estimation about the macroalga
protein quality (based only on chemical analysis). However, a biological evaluation including
human and animal feeding studies (to assess in vivo protein digestibility and bioavailability of
essential amino acids) is required to establish the complete nutritional value of macroalgae
(Kumar and Kaladharan, 2007).
The digestibility in vivo of algal proteins is not well studied, and available studies about their
assimilation by humans and animals have not provided conclusive results (Conde et al., 2013).
However, a high rate of algal protein degradation in vitro by proteolytic enzymes such as pepsin,
pancreatin and pronase has been described (e.g. 78%, 87% and 95% for Pyropia tenera, Ulva
pinnatifida and Ulva pertusa, respectively) (Fleurence, 1999).
Despite seasonal and interspecies variability, seaweeds contain all the essential amino
acids (EAAs) and seem to be able to contribute with the adequate levels of EAAs, meeting the
requirements reported in reference pattern (Kumar and Kaladharan, 2007; Matanjun et al., 2009).
Moreover, macroalgae show higher concentrations of EAAs than those commonly present in
other protein sources, such as soy beans and eggs (Bleakley and Hayes, 2017; Lourenço et al.,
2002).
Concerning the EAAs, lysine and tryptophan are often in short supply, in relation to
metabolic requirements, in most alga species (Bleakley and Hayes, 2017). The most frequent
EAA in brown, green and red macroalgae is phenylalanine (Matanjun et al., 2009). Cysteine,
methionine, leucine and isoleucine typically occur at low levels in seaweeds (Bleakley and Hayes,
2017; Cerna, 2011).
In the case of non-essential amino acids, all three classes of seaweeds show similar
profiles (Matanjun et al., 2009), with glutamic acid and aspartic acid representing the highest
fraction. Both AAs reach together up to 23% of total AAs in red algae, 28% in green algae, and
30% in brown algae. They are responsible for the distinctive ‘umami’ taste associated to these
organisms (Biancarosa et al., 2017; Wells et al., 2017).
2.3 Extraction of macroalgal proteins
In general, most of macroalga proteins are located intracellularly, surrounded by a cell wall.
The disintegration of the cell wall and the consequent release of intercellular proteins are very
important to ensure the commercial application of such biomolecules. For instance, seaweeds
have poor protein digestibility in their raw, unprocessed form (Bleakley and Hayes, 2017), which
limits their use in food and feed applications; the extraction of macroalgal proteins will improve
their bioavailability, increasing their commercial value.
14
In addition, as mentioned in section 2.2, although seaweeds are considered potential
feedstocks to produce bioenergy, the economic viability of the process is dependent on the
valorisation of the seaweed remaining components, such as proteins, through integrated
biorefinery approaches – Figure 2.1.
Figure 2.1 | Macroalga biomass refinery scheme. Adapted from (IEA, 2016a)
Algal proteins are conventionally extracted by means of aqueous, acidic, and alkaline
methods, followed by several fractionation techniques such as centrifugation, ultrafiltration,
precipitation and/or chromatography (Kadam et al., 2017). However, one of the most important
factors influencing the successful extraction of proteins is the accessibility of such molecules,
which can be substantially hindered by macromolecular cell wall assemblies, cross-linked via
disulphide bonds to polysaccharides (Harnedy and Fitzgerald, 2015). Therefore, cell-disruption
methods and specific chemical reagents (e.g. reducing agents) are often used to improve the
efficiency of protein extraction.
To date osmotic shock, mechanical grinding, ultrasonic treatment and enzyme aided
digestion are the most common methods used for macroalga cell-disruption - Table 2.1. In
general, after cell disintegration, aqueous protein extracts are first obtained. A second extraction
step under strong alkaline conditions (pH~12) is usually performed to recover the fraction of
insoluble proteins (Harnedy and FitzGerald, 2013).
15
Table 2.1 | Conventional pre-treatment cell disruption methods and extraction processes for the isolation of
macroalgal proteins. (*) Protein yield expressed as % of total protein (Protein extracted/Total proteins x 100).
Cell-disruption method
Extraction Process
Macroalga species
Protein Recovery Yield
Reference
Osmotic shock
Aqueous & alkaline
(sequential)
Palmaria Palmata 6.77±0.22% dry
wt. (Harnedy and
FitzGerald, 2013)
Ulva rigida 26.80±1.3%* (Fleurence et al.,
1995)
Ulva rotundata 36.10±1.4%* (Fleurence et al.,
1995)
Aqueous
Ulva rigida 9.73±0.6%* (Fleurence et al.,
1995)
Ulva rotundata 14.00±1.8%* (Fleurence et al.,
1995)
High shear force Aqueous &
alkaline (Sequential)
Palmaria Palmata 6.92±0.12% dry
wt. (Harnedy and
FitzGerald, 2013)
Porphyra acanthophora var.
acanthophora
8.94±0.53% dry wt.
(Barbarino and Lourenço, 2005)
Sargassum vulgare
6.91±0.15% dry wt.
(Barbarino and Lourenço, 2005)
Ulva fasciata 7.30±0.84% dry
wt. (Barbarino and
Lourenço, 2005)
Enzyme hydrolysis
Aqueous/protein released by action of enzyme activity
& alkaline (sequential)
Palmaria Palmata 11.57±0.08% dry
wt. (Harnedy and
FitzGerald, 2013)
Aqueous/protein released by action of enzyme activity
& acidic (sequential)
Ulva rigida 18.48±2.10%* (Fleurence et al.,
1995)
Ulva rotundata 22.00±1.20%* (Fleurence et al.,
1995)
Sonication Acidic
Ulva rigida 10.35±0.80%* (Fleurence et al.,
1995)
Ulva rotundata 16.08±0.90%* (Fleurence et al.,
1995)
None - direct
Acidic
Ulva rigida 9.37±1.60%* (Fleurence et al.,
1995)
Ulva rotundata 13.80±1.20%* (Fleurence et al.,
1995)
Acidic & alkaline (sequential)
Ulva rigida 17.50±1.30%* (Fleurence et al.,
1995)
Ulva rotundata 25.17±1.90%* (Fleurence et al.,
1995)
Aqueous biphasic system
(PEG/K2CO3)
Ulva rigida 19.10±1.10%* (Fleurence et al.,
1995)
Ulva rotundata 31.56±2.10%* (Fleurence et al.,
1995)
Little or no studies to date have looked at industrial significant protocols for the extraction
of macroalga proteins. Most of the conventional methods described above are laborious and time-
consuming, and there are also scalability concerns associated to some of them. However, the
16
main drawback is the use of alkaline and acidic solutions to mediate the protein extraction in the
majority of these conventional methods. The feasibility of a method for protein extraction depends
mainly on the type of product and the intended downstream application. The use of severe
conditions (extreme pH) can affect the integrity and the consequent functionality of the extracted
proteins.
Novel methods for cell-disruption and extraction of proteins from macroalgae are required.
Since the value of isolated proteins depends on their functional properties, mild operation
conditions for their recovery and separation are desirable. It is important to note that, in addition
to ensure protein functionality, the extractive performance (high protein recovery yields) is also a
crucial parameter to take into account when attempting the development of novel extraction
processes (Harnedy and Fitzgerald, 2015).
2.4 Ionic liquids as new solvents
In the last decades, the world “green” acquired a new meaning in chemistry-related fields,
changing the way by which academia and industry design chemical processes. “Green chemistry”
has demonstrated how fundamental scientific methodologies can protect human health and the
environment in an economically beneficial manner (Anastas and Kirchhoff, 2002). In this context,
besides the development of renewable feedstocks, the design of safer chemicals and
environmentally benign solvents became a priority (Capello et al., 2007). Ionic liquids have
triggered the attention of scientific community as a possible alternative to meet the requirements
of “green chemistry”. At the same time, ILs have been proposed as new solvents for the extraction
and separation of value-added compounds from biomass (Passos et al., 2014), being able to
maintain the stability of extracted molecules, namely, proteins, enzymes and nucleic acids
(Naushad et al., 2012; Vijayaraghavan et al., 2010). These evidences make the use of ILs
promising for the extraction and isolation of proteins from macroalgae.
Ionic liquids are organic salts, with melting points below 100ºC, composed by cations and
anions with low symmetry and low charge density (Seddon, 1997). The choice of these
components determines the distinct physicochemical properties of each IL (melting point, polarity,
viscosity, chemical and thermal stability, solvation properties) (Stark, 2011). They are described
as “designer solvents” since there is a large degree of cation/anion combinations, allowing the
possibility of tuning their characteristics, such as thermo-physical properties, biodegradation
ability or toxicological issues, as well as their hydrophobicity and solution behaviour (Passos et
al., 2014).
ILs’ features and all related advantages make them competitive against conventional
organic solvents (Canales and Brennecke, 2016). They stand out by their negligible vapour
pressure, non-flammability, wide electrochemical window, high chemical and thermal stability and
ability to dissolve a wide range of organic and inorganic compounds, facilitating diverse types of
chemical transformations and separation processes (Bennett and Leo, 2004). The replacement
of volatile organic solvents by non-volatile ILs eliminates solvent losses to the atmosphere,
17
decreasing the environmental footprint. This is the main reason by which ionic liquids are
considered “green” (Zhao et al., 2005).
2.4.1 Ionic liquids for macroalga cell disintegration
The basis of the extraction mechanism proposed in this dissertation lies on the interaction
of ionic liquids with the macroalga cell wall. It is expected that such interaction leads to the
complete or partial cell wall disintegration, allowing access to the desired protein molecules.
The core component of plant cell walls are cellulose microfibrils, accounting to roughly one-
third of the total mass of many plants (Somerville, 2006). Although the proportion of cellulose in
marine algae varies more than in higher plants, cellulose remains a significant fraction in the
majority of seaweeds. In most green algae, the cellulose content can reach up to 70% of the dry
wt. (e.g. Cladophorales and Ulvales) (Baldan et al., 2001). However, in red and brown algae, the
cellulose content is lower and can be lacking in some species, being replaced by xylans and
mannans, which also perform structural function, although with less rigidity than cellulose
microfibrils.
Cellulose consists of a polydisperse linear glucose polymer chain which forms hydrogen-
bonded supramolecular structures (Finkenstadt and Millane, 1998). Glucose units are linked by
1,4-β glycosidic bonds, which stability is reinforced by intrachain hydrogen bonds (Hinterstoisser
and Salmén, 2000).
Although the monomer glucose and short oligomers are water-soluble, cellulose is not.
Reasons for this are its high molecular weight (solubility is usually inversely related to polymer
length) and the comparatively low flexibility of cellulose polymer chains (Arneniades and Baer,
1977), which arises from the strong glycosidic bonds and the huge degree of both intra- and
intermolecular hydrogen bonds (Figure 2.2) (Pinker et al., 2009).
Figure 2.2 | Intra- and intermolecular hydrogen bonds in cellulose. Adapted from (Pinker et al., 2009).
To separate the polymeric structure of cellulose, an excellent solvent must be employed in
order to disrupt the intermolecular H-bond interactions, leading to the polymer dissolution (Mohd
et al., 2017).
In 2002, it was found that several ionic liquids can dissolve large amounts of cellulose
(Swatloski et al., 2002), which opened up the way to the development of new cellulose solvent
systems. The most successful cations for cellulose dissolution are based on methylimidazolium
18
and methylpyridinium cores, with allyl-, ethyl-, or butyl- side chains and the most promising anions
are chloride, acetate, and formate (Pinker et al., 2009).
It is thought that both anions and cations are involved in the cellulose dissolution process.
Figure 2.3 shows the proposed dissolution mechanism of cellulose by ionic liquids, in which
electron donor-electron acceptor (EDA) complexes are formed between the hydroxyl groups of
cellulose and the ionic liquids. In these complexes, the oxygen atoms serve as electron pair
donors and the hydrogen atoms act as electron acceptors. On the other hand, the cations of the
ionic liquid act as electron acceptor centres and the anions as electron donor centres. Both
centres must be located close enough in space to allow the interaction and the formation of EDA
complexes. Upon interaction of the cellulose-OH with the ionic liquid, the oxygen and hydrogen
atoms from hydroxyl groups are separated, resulting in the opening of the hydrogen bonds
between the molecular chains, and their dissolution (Feng and Chen, 2008).
Figure 2.3 | Proposed dissolution mechanism of cellulose in ionic liquid 1-Butyl-3-methylimidazolium chloride
([BMIM][Cl]). Adapted from (Pinker et al., 2009).
It has been demonstrated that the capability of ILs to dissolve cellulose are directly
correlated to their strong hydrogen bond basicity and lower viscosity (Zhang et al., 2017). The
hydrogen bond basicity describes the ability of a molecule to act as a hydrogen bond acceptor
(Oliferenko et al., 2004), a role played by ionic liquid anions according to the proposed
mechanism. In this sense, the better the hydrogen bond acceptor ability of the IL anion, the higher
is its ability to solubilize cellulose (Stark et al., 2012). On the other hand, the chemical structure
of cations also has an important effect on the cellulose dissolution. In general, when the chain of
alkyl groups or the symmetry of the cations increases, the dissolution rate of cellulose in ILs
decreases, due to the rise of the viscosity and/or decrease of cation hydrogen bond acidity (Zhang
et al., 2017).
2.4.2 Ionic liquid-based aqueous biphasic systems for biomolecules
fractionation
Aqueous two-phase systems (ATPSs) have been used for the recovery of a broad array of
biomolecules (e.g. proteins, polysaccharides, nucleic acids) through their partition between the
two aqueous liquid phases (Liu et al., 2012), which makes its use a promising strategy for refining
other seaweed components beyond the desired proteins. This allows the maximization of the
inherent value of several components from seaweed, which may be crucial for the profitable use
of seaweeds as raw-materials (Van Hal et al., 2014).
19
ATPS consists of two immiscible aqueous-rich phases based on polymer-polymer,
polymer-salt or salt-salt combinations. Although the most investigated ATPSs are the polymer-
based systems, in the last decade, ionic liquids were proposed as alternative phase-forming
components (Gutowski et al., 2003).
In an ATPS, both solutes that make up the system are water soluble, they separate into
two coexisting phases above their critical concentration, in which each phase will contain
predominantly only one type of solute (Freire et al., 2012). The coexisting phases will offer
different physicochemical environments for the biomolecules, driving them more extremely to one
phase, according to the biomolecule properties (e.g. hydrophobicity, charge, size) (Andrews and
Asenjo, 1989).
The phase diagram can be considered as a fingerprint of an ATPS under specific conditions
(e.g. temperature and pH). It is unique and shows the potential working area of the ATPS,
providing information about the required concentration of phase-forming components for phase
splitting, the concentration of phase-components in the top and bottom phases, and the ratio of
phase volumes (Raja et al., 2012). Figure 2.4 represents a phase diagram for a hypothetical
system, in which the binodal curve (TCB) indicates the boundaries of mono- and biphasic regions.
For mixture compositions above the binodal curve, there is the formation of a two-phase system,
while mixture compositions below this curve fit into the monophasic region and will not phase
separate. The three systems X, Y and Z differ in their initial compositions and in volume ratios.
However, in equilibrium, all the systems have the same top phase composition (T IL,Tsalt) and the
same bottom phase composition (BIL,Bsalt). This is because they are lying on the same tie-line
(TL) – TB in Figure 2.4 –, whose end points determine the equilibrium phase composition. Point
C on the binodal curve is called critical point, just above this point the volume of both phases is
theoretically equal, and the tie-line length (TLL) is 0.
Figure 2.4 | Phase diagram for a hypothetical system composed of ionic liquid + salt + water (weight fraction
percentage). TCB: Binodal curve; C: Critical point; TB: Tie-line; T: Composition of the top phase; B:
Composition of the bottom phase; X, Y and Z: total composition of the ATPS. Adapted from (Raja et al.,
2012).
20
The units of TLL are % w/w, same as the component concentrations, and it can be
estimated by using the ratio of the mass compositions (Equation 2.1) (Freire, 2016a):
𝑉𝑇𝜌𝑇
𝑉𝐵𝜌𝐵
=𝑋𝐵
𝑋𝑇
Equation 2.1
in which V and ρ stands for volumes and densities of top (T) and bottom (B) phases while XB and
XT are the segment lengths of the tie-line as shown in Figure 2.4.
Equation 2.2 describes an alternative and more accurate way for the TLL determination
based on the equilibrium phase compositions (Freire, 2016a):
𝑇𝐿𝐿 = √[𝐵𝑠𝑎𝑙𝑡 − 𝑇𝑠𝑎𝑙𝑡]2 + [𝑇𝐼𝐿 − 𝐵𝐼𝐿]2
Equation 2.2
Bsalt and Tsalt are the concentrations of salt in the bottom and top phases, respectively,
whereas TIL is the concentration of IL in the top phase, and BIL is the concentration of ionic liquid
in the bottom phase.
The tie-lines are commonly parallel, and their slope (STL) can be determined by
Equation 2.3 (Freire, 2016a), facilitating the construction of further TLs.
𝑆𝑇𝐿 =[𝑇𝐼𝐿 − 𝐵𝐼𝐿]
[𝐵𝑠𝑎𝑙𝑡 − 𝑇𝑠𝑎𝑙𝑡]=
∆𝐼𝐿
∆𝑠𝑎𝑙𝑡
Equation 2.3
The knowledge of the binodal curve is essential when working with ATPS because the
composition of the two phases must be known in order to understand the partition of biomolecules.
The binodal curve can be determined by three methods: turbidimetric titration, cloud-point and
node determination methods (Iqbal et al., 2016).
In most of IL-based ATPS studies, the binodal experimental data is usually fitted according
to Equation 2.4 (Merchuk et al., 1998), in which Y and X are the mass fraction percentages of the
ionic liquid and the salting-out agent, respectively. a, b and c are fitting parameters obtained by
least squares regression.
𝑌 = 𝑎𝑒𝑥𝑝[(𝑏 × 𝑋0.5) − (𝑐 × 𝑋3)]
Equation 2.4
The fitting of the binodal data allows the determination of TLs by a weight balance
relationship. The compositions of the top and bottom phases and the overall system composition
are calculated by the lever-arm rule. Commonly, the following system of four unknown constants
is applied to fit the tie-line data (Equation 2.5 to Equation 2.8) (Merchuk et al., 1998): T, B and M,
designate the top phase, the bottom phase and the mixture, respectively; Y represents the IL
weight fraction percentage, whereas X represents the salting-out agent; α is the ratio between the
mass of the top phase and the total mass of the mixture; and A, B and C are the fitted constants
obtained by the application of Equation 2.4.
21
𝑌𝑇 = 𝑎𝑒𝑥𝑝[(𝑏 × 𝑋𝑇0.5)] − (𝑐 × 𝑋𝑇
3)]
Equation 2.5
𝑌𝐵 = 𝐴𝑒𝑥𝑝[(𝐵 × 𝑋𝐵0.5)] − (𝐶 × 𝑋𝐵
3)]
Equation 2.6
𝑌𝑇 =𝑌𝑀
𝛼−
1 − 𝛼
𝛼× 𝑌𝐵
Equation 2.7
𝑌𝑇 =𝑋𝑀
𝛼−
1 − 𝛼
𝛼× 𝑋𝐵
Equation 2.8
The ratio of equilibrium compositions in the ATPS top and the bottom phases determines
the partition coefficient (K) of biomolecules, which is given by Equation 2.9.
𝐾 =𝐶𝐴𝑇
𝐶𝐴𝐵
Equation 2.9
where CAT is the equilibrium concentration of component A in the top phase and CAB is the
equilibrium concentration of A in the bottom phase. The partition coefficient is often used to
evaluate the extension of biomolecules separation in the ATPS. The more different the K of a
target biomolecule from the K of the remaining biomolecules present in the system, the better the
extraction. In other words, K values greater than one indicate the effectiveness of partitioning in
the ATPS (Mazzola et al., 2008).
IL-based ATPSs have been firstly proposed in 2003 (Gutowski et al., 2003), and have been
widely used in biological separation and purification fields (Chen et al., 2014). The system is
usually formed by water + ionic liquid + inorganic salt, owing to the high ability of salt ions to
induce the salting-out of IL. This way, the phase formation is due to the preferential hydration of
the high charge-density salt ions, over the low-symmetry and charge delocalized IL ions, which
are only able of weak directional intermolecular interactions (and hence weakly hydrated as
compared to the common salting-out inducing salts) (Freire, 2016b).
Obviously, the nature of the inorganic salt used as salting-out agent plays a crucial role in the
definition of any ATPS. The ATPS formation trend follows closely the well-known Hofmeister
series, which ranks the relative influence of ions on the physical behaviour of a wide variety of
aqueous processes (Zhang and Cremer, 2006). Generally, this influence is more pronounced for
anions than for cations (Schneider et al., 2011), and the typical ordering of the ions and some of
its related properties are shown in Figure 2.5. The species in the green region are described as
kosmotropes, while those in red region are chaotropes. These terms refer to the ion ability to alter
the hydrogen bonding network of water (Collins and Washabaugh, 1985). The kosmotropes,
which are believed to be “water structure makers”, are strongly hydrated and have stabilizing and
salting-out effects on proteins. On the other hand, chaotropes (“water structure breakers”) are
known to destabilize folded proteins and give rise to salting-in behaviour.
22
Figure 2.5 | Ordering of the ions and related properties in the Hofmeister series (Zhang and Cremer, 2006).
Several studies have reported the successful application of IL-based ATPS for protein
partition. The proteins were mainly concentrated in the IL-rich phase (top phase) and, although
the partition mechanism involved in the ATPS is still poorly understood, hydrophobic and
electrostatic interactions, as well as salting-out effects are suggested to be the main driving forces
responsible for such behaviour (Ventura et al., 2017). The formation of IL aggregate-protein
complexes has also been proposed as the driving force for the selective separation of proteins
(Pei et al., 2010).
From an engineering point of view, the extraction and separation methods should be
connected, preferably merged and conducted in a single-step. Therefore, high expectations are
placed on the use of ionic liquids as novel solvents once they can provide a promising platform,
by means of an ATPS, for the simultaneous extraction and selective separation of compounds
from macroalgae.
2.4.3 Ionic liquid recovery
Ionic liquids remain to date the most expensive research-grade solvents, under
investigation, for the dissolution of biomass, which is one of the major drawbacks when envisaging
their large-scale application (George et al., 2015). In addition, when considering their application
at an industrial scale, the possible environmental impact must be considered. Although the lack
of volatility of ionic liquids does not contribute to the release of harmful vapour into the
atmosphere, there still exist concerns about their toxicity and biodegradability, which can cause
severe water contamination upon the release to aquatic environments (Frade and Afonso, 2010).
In this sense, it is of the most importance the recovery of ionic liquids from the algal extracts,
so that they can be reused in further extractions/separations steps, saving the overall costs of the
process and avoiding the associated environmental impact. Depending on the nature of the ILs
and the target molecules to extract, a wide range of recovery methods have been proposed in
literature (Mai et al., 2014). However, barrier processes stand out from competitive techniques
because, in addition to their high-performance (Kuzmina, 2016), membrane separations are a
well-known procedure, commercially available to industrial deployment and considered a low-
23
energy demanding process, in which no chemicals are added, decreasing the costs (Haerens et
al., 2010; Wu et al., 2009).
A membrane process performs a certain separation by means of a membrane capable of
retaining specific compounds, while allowing the passage of others. In pressure-driven membrane
processes (reverse osmosis, nanofiltration, ultrafiltration and microfiltration), when a force is
exerted across the membrane, the feed stream goes through it and is split into permeate and
retentate. Both fractions contain the same solvent, permeate is rich in lower size molecules
whereas the retentate is concentrated with larger size molecules (Beier, 2015).
Particles and dissolved components are (partially) retained based on the properties of the
membrane such as size, shape and charge. The separation efficiency is expressed by the
rejection coefficient (R) of a given compound (Equation 2.10):
𝑅 = 1 −𝐶𝑝
𝐶𝑓
Equation 2.10
where Cp stands for permeate concentration and Cf stands for feed concentration of the specific
compound. The rejection coefficient ranges from 0 for complete permeation to 1 for complete
rejection (Van Der Bruggen et al., 2003).
Pressure-driven membrane processes can be classified by several criteria: the
characteristics of the membrane (pore size), size and charge of the retained particles or
molecules, and pressure exerted on the membrane (Beier, 2015). This classification distinguishes
microfiltration, ultrafiltration, nanofiltration, and reverse osmosis. In the specific case of this
master’s dissertation, ultrafiltration was performed in order to recover the ionic liquid from the
macroalga extracts (Figure 2.6). Ultrafiltration membranes have pores with 2-100 nm that allow
the retention of macromolecules (e.g. proteins) while it is permeable to salts (e.g. ionic liquid)
(Van Der Bruggen et al., 2003).
Figure 2.6 | Schematic representation of ultrafiltration. Adapted from (Van Der Bruggen et al., 2003).
24
2.5 Macroalgal proteins applications
Seaweeds are rich sources of proteins and contain all the essential amino acids at various
concentrations (Kumar and Kaladharan, 2007; Matanjun et al., 2009), which suggest the high-
quality of macroalgal proteins. Although further studies in in vivo digestibility of seaweed proteins
are necessary to evaluate their functional properties, macroalga proteins seem to be suitable for
the formulation of rich-protein supplements for human and animal dietary or to enhance the
protein content of foods and feed, which will be useful to fulfil the future needs. In addition, it is
also important to note that proteins are an essential nutritional component in the diet of athletes,
required to repair and build muscle during exercise (Kerksick et al., 2006). The American College
of Sports Medicine recommends a protein intake of 1.2-1.7 g.kg-1.day-1 for endurance- and
resistance-trained athletes (Gerovasili et al., 2009). Once again, algae could represent a valuable
resource for athletes requiring high levels of protein, especially for vegan athletes for whom eggs
and dairy whey protein may not be suitable (Bleakley and Hayes, 2017).
Macroalgal protein extracts can also be a potential source for valuable amino acids for food
and feed supplements. The presence of essential amino acids in right quantities is crucial to make
quality proteins and they cannot be replaced by other “less-valuable” building-blocks (Holdt and
Kraan, 2011).
Besides the nutritional purposes in food, proteins can also provide and/or stabilize the
characteristic structure of individual foods. The functional properties of proteins are mainly
associated with their ability to form and/or stabilize networks (gels and films), foams, emulsions
and sols, which is often revealed by their tertiary structure (Foegeding and Davis, 2011). For
instance, in milk proteins, the intrinsically unfolded structure of caseins, in contrast to the compact
globular structure of whey proteins, alters their film forming ability and thereby foaming properties
(Marinova et al., 2009).
During the last decade, consumer requirements in the field of food production have
changed considerably (Siró et al., 2008). Consumers increasingly believe that food contribute
directly to their health and, today, foods are no more intended to only satisfy hunger and to provide
the necessary nutrients, but also and specially to prevent nutrition-related diseases and to
improve physical and mental well-being (Bigliardi and Galati, 2013). In this regard, there is a
market trend in the food industry towards the development and manufacturing of functional
products1 (Plaza et al., 2010). The general contribution of proteins/peptides with specific
bioactivities is of increasing importance prompting the development of food products based on
protein-related health issues (Kitts and Weiler, 2003).
1 Functional products are considered to be fortified, enriched, altered or enhanced foods that provide an additional health or well-being benefit besides the energetic and nutritional aspects that every food must confer (Hasler, 2002).
25
Some macroalgal proteins (lectins and phycobiliproteins) are considered bioactive
compounds2 and therefore, they have great potential to be used as ingredients for functional
foods, in addition to be useful for pharmacologic proposes (Samarakoon and Jeon, 2012).
Seaweed proteins are also sources of bioactive peptides that, although inactive within the parent
proteins, can be released through microbial fermentation or enzymatic hydrolysis (Daliri et al.,
2017; Jiménez-Escrig et al., 2011). These protein fragments have useful properties for human
health, such as anti-microbial, anti-fungal, anti-viral, anti-oxidative, anti-thrombotic, anti-
hypertensive, anti-tumor, immunomodulatory and appetite suppression activities (Korhonen and
Pihlanto, 2006; Perez Espitia et al., 2012).
Algal proteins and derivatives are also suitable for several cosmetic applications. For
instance, these biomolecules are important for moisture retention on hair and skin. On the other
hand, they also show a strong affinity for both, improving their nourishments. In the sense that
cosmeceuticals are supposed to be involved in healing and repairing damage skin, not only by
moisturizing, but also maintaining the nourishment, proteins and peptides derived from algae can
be applied in cosmeceutical products (Hagino and Salto, 2004).
The anti-oxidative marine algal peptides might be an interesting source of primary
ingredients for the formulation of future cosmeceuticals due to its protective effect from the
reactive oxygen species damaging activities (Samarakoon and Jeon, 2012).
2.5.1 Macroalgal bioactive proteins
2.5.1.1 Lectins
Lectins are glycoproteins that selectively recognize and reversibly bind to carbohydrates
without initiating their further modifications through the associated enzymatic activity (Weis and
Drickamer, 1996). They are involved in many biological processes, such as host-pathogen
interactions, cell-to-cell recognition and signalling, induction of apoptosis, cancer metastasis, cell
growth and differentiation (Ziółkowska and Wlodawer, 2006). Furthermore, due to their high-
specific carbohydrate binding capacity, lectins have been found to increase the agglutination of
blood cells (erythrocytes). They are also useful as cancer biomarkers or as targets for drug
delivery and in the detection of disease-related alterations of glycan synthesis, including infectious
agents such as viruses, bacteria, fungi and parasites (Holdt and Kraan, 2011; Naeem et al., 2007).
Nevertheless, other bioactive properties exhibited by macroalgal lectins include antibiotic,
mitogenic, cytotoxic, anti-nociceptive, anti-inflammatory, anti-adhesion, anti-human
immunodeficiency virus (HIV), and human platelet aggregation inhibition activities (Harnedy and
Fitzgerald, 2011) – Table 2.2.
2 Bioactive compounds are essential and non-essential compounds that occur in nature, are part of the food chain, and have an effect on human health (Biesalski et al., 2009).
26
Table 2.2 | Macroalgal source species and bioactivity of lectins.
Macroalgal source species Bioactivity Reference
Caulerpa cupressoides Anti-nociceptive and anti-
inflammatory activity (Vanderlei et al., 2010)
Codium fragile Host-pathogen interactions (Hori et al., 2000; Smit, 2004)
Eucheuma amakusaensis Induction of apoptosis,
metastasis and differentiation (Hori et al., 2000)
Eucheuma cottonii Recognition and binding of
carbohydrates, including virus, bacteria, fungi and parasites
(Bird et al., 1993; Cardozo et al., 2007; Hori et al., 2000)
Eucheuma serra
Induction of apoptosis in several cancer cell-lines (including
Colo201 and HeLa) (Sugahara et al., 2001)
Suppression of colonic carcinogenesis in mice
Growth inhibition of 35 human cancer cell-lines
(Hori et al., 2007)
Antibacterial against fish pathogen Vibrio vulnificus
(Liao et al., 2003)
Galaxaura marginata Anti-HIV (Mori et al., 2005; Smit, 2004)
Gracilaria sp.
Antibacterial against fish pathogen Vibrio vulnificus
(Liao et al., 2003)
Griffithsia sp. Anti-adhesion (Smit, 2004)
Hypnea japonica
Inhibition of human platelet aggregation
(Matsubara et al., 1996)
Cytotoxic (Smit, 2004)
Solieria robusta Mitogenic activity on mouse
spleen lymphocytes (Hori et al., 1988)
Ulva sp. Antibiotic (Smit, 2004)
2.5.1.2 Phycobiliproteins
Phycobiliproteins are water-soluble, coloured and fluorescent proteins with an important
role in photosynthesis (Glazer, 1994). These molecules can constitute a major proportion of red
alga cell proteins, with reported values of 12% (w/w) dry wt. in Palmaria palmata (Guangce et al.,
2002). There are four main groups of phycobiliproteins – phycoerythrin, phycocyanin,
allophycocyanin and phycoerythrocyanin – which are classified based on their colour and
absorption characteristics (Sekar and Chandramohan, 2008).
Phycobiliprotein pigment molecules spontaneously fluoresce in vivo and in vitro (Aneiros
and Garateix, 2004; Fleurence, 2004) and, therefore, they are used in fluorescent immunoassays,
fluorescent immunohistochemistry assays, biomolecule (proteins, antibody, nucleic acid)
labelling, and fluorescent microscopy (Harnedy and Fitzgerald, 2011). They are also currently
used as natural pigments for food (chewing gums and dairy products) and in cosmetic applications
(Sekar and Chandramohan, 2008; Viskari and Colyer, 2003). Niohan Siber Hegner Ltd. and
Dainippon Ink & Chemical Inc., two companies in Tokyo, market the phycobiliprotein phycocyanin
as a natural food dye (Houghton, 1996). Nevertheless, seventeen patents for the therapeutic
application of phycobiliproteins have been submitted worldwide due to the anti-oxidant, anti-
27
inflammatory, neuroprotective, hypocholesterolaemic, hepatoprotective, anti-viral, anti-tumour,
liver-protecting, atherosclerosis treatment, serum lipid-reducing and lipase inhibition activities that
these biomolecules have shown (Sekar and Chandramohan, 2008).
2.5.2 Macroalgal bioactive peptides
Several protein hydrolysates and associated peptides exhibiting bioactive properties have
been found in marine macroalgae - Table 2.3 and Table 2.4. Therefore, they have potential to be
applied as functional food ingredients (Harnedy and Fitzgerald, 2011). It is important to note that
several studies have reported that alga-derived peptides are able to resist gastrointestinal
digestion from enzymes such as trypsin, pepsin, and chymotrypsin. This is an essential trait for
bioactive peptides in order to achieve their physiological effect at their site of action (Bleakley and
Hayes, 2017).
Table 2.3 | Examples of macroalgal peptides exhibiting biological activity.
Macroalga Species Sequence/Name Biological Activity Reference
Bryopsis sp. Kahalalide A and F
Anti-cancer, anti-tumour and anti-AIDS
(acquired immune deficiency syndrome)
(Smit, 2004)
Galaxaura filamentous Galaxamide Anti-Cancer (Xu et al., 2008)
Hizikia fusiformis Gly-Lys-Tyr; Ser-Val-Tyr;Ser-Lys-Thr-Tyr
Anti-hypertensive (Suetsuna, 1998a)
Palmaria palmata (dulse)
Val-Tyr-Arg-Thr; Leu-Asp-Tyr; Leu-Arg-Tyr; Phe-Glu-Gln-Trp-Ala-
Ser
Anti-hypertensive (Furuta et al., 2016)
Asn-Ile-Gly-Gln Anti-inflammatory (Fitzgerald et al., 2013)
Porphyra yezoensis
Ile-Tyr; Met-Lys-Tyr; Ala-Lys-Tyr-Ser-Tyr;
Ley-Arg-Tyr Anti-hypertensive
(Cha et al., 2006; Suetsuna, 1998b)
Ala-Lys-Tyr-Ser-Tyr Anti-hypertensive (Saito and Hagino,
2005)
Ulva sp. Glu-Asp-Arg-Ley-Lys-
Pro Mitogenic in skin
fibroblasts (Ennamany et al.,
1998)
Undaria pinnatifida Val-Tyr; Ile-Tyr; Phe-
Tyr; Ile-Trp
Reduction of blood pressure in
spontaneously hypertensive rats
(Sato et al., 2002)
Undaria pinnatifida Val-Tyr; Ile-Tyr; Ala-
Trp; Phe-Tyr; Val-Trp; Ile-Trp; Leu-Trp
Anti-hypertensive (Sato et al., 2002)
28
Table 2.4 | Examples of macroalgal protein hydrolysates exhibiting biological activity.
Macroalga species Biological activity Reference
Caulerpa racemosa Anti-oxidant, anti-cancer (Lakmal et al., 2014)
Costaria costata Anti-oxidant, anti-tumour, ACE
(angiotensin converting enzyme)-inhibitory
(Lee et al., 2005)
Ecklonia cava Anti-hypertensive (Cha et al., 2006)
Antioxidant (Heo et al., 2003)
Enteromorpha prolifera Anti-hypertensive, anti-oxidant,
anti-tumour (Lee et al., 2005)
Grateloupia filicina Anti-hypertensive, anti-oxidant,
anti-tumour (Lee et al., 2005)
Hizikia fusiformis Anti-hypertensive (Suetsuna, 1998a)
Ishige okamurae Anti-oxidant (Heo et al., 2003)
Sargassum coreanum Anti-oxidant (Heo et al., 2003)
Sargassum fullvelum Anti-oxidant (Heo et al., 2003)
29
3 3. Aim of studies
30
31
Although seaweeds show great potential as sources of proteins for both human and animal
nutrition, to date there is no feasible procedure for the recovery of these proteins (Bleakley and
Hayes, 2017). In order to meet this gap, this master’s dissertation aims to study a new integrated
biorefinery approach for the single-step extraction and separation of proteins from macroalga Ulva
lactuca, using ionic liquids as new solvents.
Among the seaweed species available in European Atlantic waters, Ulva sp. (Figure 3.1)
is extensively characterised (Fleurence et al., 1995), having a high protein content (up to 44% dry
wt.) (Holdt and Kraan, 2011). In addition, this species is successfully cultivated in integrated multi-
trophic aquaculture systems, enabling not only scalable and controllable cultivation conditions
(Marinho et al., 2013; Robertson-Andersson et al., 2008) but also the removal of N- and P-rich
compounds in waste water from land-based aquaculture (Lawton et al., 2013). On the other hand,
since Ulva sp. is the world’s most abundant seaweed, this can have a negative impact on the
environment and tourism in the coastline, making its harvesting essential (Briand and Morand,
1997).
Figure 3.1 | Seaweed Ulva lactuca (Krisp, 2011).
One of the limitations in protein extraction from seaweed is the presence of
polysaccharides as structural components of macroalga cell walls (Harnedy and Fitzgerald,
2015). The main polysaccharides in U. lactuca cell wall are ulvan and cellulose, which have a
structural behaviour as described in Figure 3.2.
Figure 3.2 | Distribution of the different Ulva sp. cell wall polysaccharides in a schematic cross section of a
thallus (A) and the proposed associations between the different cell wall polysaccharides (B). Adapted from:
(Lahaye and Robic, 2007).
32
Ulvan is a water-soluble polysaccharide, which main constituents are sulphate, rhamnose,
xylose, and glucoronic acid (Lahaye and Robic, 2007). On the other hand, cellulose is insoluble
in water and in most common organic liquids, mainly due to the many intermolecular hydrogen
bonds present in its structure (Swatloski et al., 2002). The insoluble character of cellulose is a big
challenge in cell disruption methods, especially when the desired molecules are proteins, which
functionality can be easily compromised by aggressive pre-treatment and extraction processes.
Fortunately, ionic liquids have demonstrated a good performance in the complete dissolution of a
wide range of biomass matrices mainly due to their ability to dissolve cellulose (Brandt et al.,
2013). Moreover, they have also been proposed as promising alternative solvents for the
extraction and separation of added-value compounds from biomass. Ionic liquids can provide a
non-denaturing environment for biomolecules, maintaining the protein structure and, if applicable,
ensuring their enzymatic activity (Passos et al., 2014).
In this perspective, this thesis focuses on the development of a process in which U. lactuca
biomass is processed for protein extraction in the presence of a suitable ionic liquid. An aqueous
biphasic system aiming to separate the proteins from the carbohydrates is also studied. Hereby,
the proposed biorefinery approach would allow not only the valorisation of seaweed proteins but
also of carbohydrates for further use, for instance, for bioenergy generation. Finally, the recovery
of the ionic liquid from the macroalga extracts is evaluated by means of ultrafiltration.
33
4 4. Materials and Methods
34
35
4.1 Sample collection and preparation
Ulva lactuca was kindly provided by Dr. Willem de Visser from Wageningen Plant
Research. In short, cultures were maintained in 1 m3 tanks containing filtered sea water in a
greenhouse property of Wageningen University and Research (Nergena, Wageningen – The
Netherlands). Samples were collected every month in the period June-August 2016. After
biomass collection, the excess water was removed and the samples were freeze dried in a
sublimator 2x3x3 (Zirbus Technology GmbH, Germany) for 72 hours. Dried samples were stored
in sealed bags and maintained in the dark, at room temperature, until further use. Before starting
the experiments, the dried alga was milled in a coffee grinder to obtain a fine and homogeneous
powder, which was stored under the same conditions and regarded as starting biomass (sB).
4.2 Biomass characterization
The algal batch under study was characterized in terms of ash and moisture content. The
quantification of total lipids, carbohydrates and proteins was also performed.
4.2.1 Total solids and ash
The dry weight concentration and ash content were determined according to NREL
(National Renewable Energy Laboratory) analytical procedure, specific for algal biomass (Van
Wychen and Laurens, 2013). For total solids determination, three aluminum trays were weighed
in an analytical balance (ME 23 SP, Santorius, Germany), and the weight of each aluminum tray
was recorded. An amount of 100 mg of sB was weighted in the pre-weighed trays (experiment
performed in triplicate); the weight of each tray and sample was then recorded. Samples were
placed in a convection drying oven (Nabertherm, Germany), and dried for 18 hours at 105ºC and
atmospheric pressure. Afterwards, trays were removed and cooled to room temperature in a
desiccator. Trays and dried samples were weighed and the respective weights recorded. In order
to determine the percentage of total solids and moisture content, Equation 4.1 and Equation 4.2
were, respectively, used. All the results reported in this dissertation are expressed on dry wt. basis
(DW), defined as the weight of biomass mathematically corrected for the amount of moisture in
the sample (Equation 4.3).
% 𝑇𝑜𝑡𝑎𝑙 𝑆𝑜𝑙𝑖𝑑𝑠 = 𝑊𝑒𝑖𝑔ℎ𝑡𝑎𝑙𝑢𝑚𝑖𝑛𝑢𝑚 𝑡𝑟𝑎𝑦+𝑑𝑟𝑦 𝑠𝑎𝑚𝑝𝑙𝑒 − 𝑊𝑒𝑖𝑔ℎ𝑡𝑎𝑙𝑢𝑚𝑖𝑛𝑢𝑚 𝑡𝑟𝑎𝑦
𝑊𝑒𝑖𝑔ℎ𝑡𝑠𝐵
× 100
Equation 4.1
% 𝑀𝑜𝑖𝑠𝑡𝑢𝑟𝑒 = 100 − % 𝑇𝑜𝑡𝑎𝑙 𝑠𝑜𝑙𝑖𝑑𝑠
Equation 4.2
𝐷𝑊𝑠𝑎𝑚𝑝𝑙𝑒 =𝑊𝑒𝑖𝑔ℎ𝑡𝑠𝐵 × % 𝑇𝑜𝑡𝑎𝑙 𝑆𝑜𝑙𝑖𝑑𝑠
100
Equation 4.3
To access the ash content, two glass tubes (VWR International, USA) were weighed in an
analytical balance, and each glass tube weight was recorded. An amount of 100 mg of sB was
36
weighted in the pre-weighted glass tube (experiment performed in duplicate). Samples were
ignited and incinerated using an ash-oven (muffle furnace L 24/11, Nabertherm, Germany)
equipped with a ramping program: temperature was ramped to 105ºC and maintained at this
temperature for 30 minutes; temperature was ramped again until 575ºC in 4 hours and maintained
at this temperature for more 4 hours; temperature was finally dropped until 105ºC in 4 hours.
Samples were removed and cooled down to room temperature in a desiccator. The weight of
glass tubes and respective ashes was recorded. The ashes were expressed as a percentage of
dry matter according to Equation 4.4.
%𝐴𝑠ℎ =𝑊𝑒𝑖𝑔ℎ𝑡𝑔𝑙𝑎𝑠𝑠 𝑡𝑢𝑏𝑒+𝑎𝑠ℎ − 𝑊𝑒𝑖𝑔ℎ𝑡𝑔𝑙𝑎𝑠𝑠 𝑡𝑢𝑏𝑒
𝐷𝑊𝑠𝑎𝑚𝑝𝑙𝑒
× 100
Equation 4.4
4.2.2 Total lipid content
The total lipid content was determined according to Folch et al. (Folch et al., 1987). An
amount of 100 mg of sB was weighed in a glass tube, using an analytical balance (experiment
performed in duplicate). The total lipid extraction began with the addition of 1 mL of chloroform
(HPLC grade, Biosolve Chimie SARL, France), followed by the addition of 1 mL of methanol
(HPLC supra gradient, Biosolve Chimie SARL, France). The mixture was vortexed for 3 minutes,
after which 1 mL of chloroform was added; the mixture was again vortexed for 3 minutes. A
volume of 0.8 mL of phosphate buffer saline (prepared according to Table 4.1) was added and
the resulting suspension was mixed for 45 minutes in an orbital shaker (LD79, Labinco,
Netherlands) at 150 rpm.
Table 4.1 | Composition of 1 L phosphate buffer saline.
Components Purity Supplier
0.21 g KH2PO4 ≥ 99% Merck Millipore, Germany
0.48 g Na2HPO4.2H2O ≥ 98% Merck Millipore, Germany
9.00 g NaCl ≥ 99% Merck Millipore, Germany
1 L distilled water
Samples were then centrifuged at 1000xg (Allegra X-30R Centrifuge, Beckman Coulter,
USA) for 1 minute to accelerate phase separation, and the bottom layer was collected to a pre-
weighed glass tube. The steps described before were performed four times, using the middle and
upper layers as starting material. The bottom layers of the following four extractions were also
collected and the solvent was evaporated using a rotational vacuum concentrator for 45 minutes
(RVC 2-25 CDplus, Martin Christ Freeze Dryers, Germany). Samples were left overnight, at room
temperature, under N2 flow to remove any remaining traces of solvent. The weight of glass tubes
and respective lipids was recorded, and the total lipid content was determined by Equation 4.5.
% 𝑇𝑜𝑡𝑎𝑙 𝑙𝑖𝑝𝑖𝑑𝑠 =𝑊𝑒𝑖𝑔ℎ𝑡𝑔𝑙𝑎𝑠𝑠 𝑡𝑢𝑏𝑒+𝑙𝑖𝑝𝑖𝑑𝑠 − 𝑊𝑒𝑖𝑔ℎ𝑡𝑔𝑙𝑎𝑠𝑠 𝑡𝑢𝑏𝑒
𝐷𝑊𝑠𝑎𝑚𝑝𝑙𝑒
× 100
Equation 4.5
37
4.2.3 Total carbohydrate content
Concerning the determination of total carbohydrate content in macroalgae, 1 mg of sB was
weighed in lysing matrix D tubes (MP Biomedicals, USA), followed by the addition of 1 mL milliQ
water (experiment performed in triplicate). In order to get a homogeneous suspension, the tubes
were placed in a tissue homogenizer (Precellys 24 Homogenizer, Bertin Instruments, France) and
bead beaten for 3 cycles of 60 seconds at 6500 rpm, with 120 seconds break between cycles.
Samples were diluted 10x with milliQ water and analyzed for carbohydrates as described in
section 4.6.2.
4.2.4 Total protein content
To determine the total protein content, two different colorimetric procedures were used,
namely the Lowry’s method and the Bradford assay, each one requiring a particular sample
preparation (experiment performed in triplicate and quadruplicate, respectively). For the first
method, 1 mg of sB was resuspended in 1 mL of lysis buffer (60 mM Tris pH 9, 2% SDS – Table
4.2) in lysing matrix D tubes, whereas for the second assay, the biomass was resuspended in 0.1
M NaOH (Sigma Aldrich, Germany). Tubes were placed in the tissue homogenizer and bead
beaten for 3 cycles of 60 seconds at 6500 rpm, with 120 seconds break between cycles, followed
by 30 minutes of incubation at 100ºC in a block heater (SBH200D/3, Stuart, UK). Then, samples
were centrifuged for 10 minutes at 3500 rpm (Centrifuge 5424R, Eppendorf, Germany), and
analyzed for protein quantification as described in section 4.6.1.
Table 4.2 | Reagents and respective purity and supplier used in lysis buffer preparation.
Reagent Purity Supplier
Tris(hydroxymethyl)aminomethane (Tris) ≥ 99.9% Sigma Aldrich, Germany
Sodium dodecyl sulphate (SDS) ≥ 98.5% Sigma Aldrich, Germany
4.3 Conventional methods for protein extraction
After literature review for the most common methods applied in protein extraction from
macroalgae, three conventional procedures were replicated to be used as reference cases.
4.3.1 Aqueous and alkaline extraction
Aqueous and alkaline extraction (sequential) was performed according to Fleurence et al.
(Fleurence et al., 1995). 1 g of sB was resuspended in 20 mL of milliQ water, and gently stirred
overnight at 4ºC (experiment performed in triplicate). The suspension was centrifuged for 20
minutes at 10000xg. The supernatant was collected for further analysis (aqueous extract)
whereas the pellet was resuspended in 10 mL of NaOH 0.1 M. The resulting suspension was
gently stirred, at room temperature, for 1 hour in a tube shaker (Multi Reax, Heidolph, Germany)
and subsequently centrifuged at 10000xg for 20 minutes. The supernatant, corresponding to
alkaline extract, was collected for further analysis.
38
Both aqueous and alkaline extracts were analyzed for protein by Bradford assay, as
described in section 4.6.1.
The pH was measured in alkaline extract using a pH electrode (SI-600, Sentron,
Netherlands).
4.3.2 Extraction by high-shear force under alkaline conditions
High-shear disruption of macroalgal cells was performed based on Harnedy and FitzGerald
(Harnedy and FitzGerald, 2013) (experiment performed in duplicate). An amount of 2.5 g of sB
was resuspended in 50 mL of a pH 8 solution, and the biomass was processed using an Ultra-
turrax (Ultra-Turrax® T25, IKA®, Germany), at 6400 rpm, for 10 minutes, at room temperature.
The beaker was placed in an ice bath to avoid sudden temperature fluctuations, which were
controlled with a thermometer along the experiment.
After mechanical grinding, the suspension was centrifuged at 10000xg for 20 minutes and
the supernatant was collected for protein analysis. The concentration of protein in the resulting
extract as determined by Bradford assay as described in section 4.6.1.
4.3.3 Aqueous biphasic system PEG/Na2CO3
Aqueous two-phase system was created based on the binodal curves reported by Snyder
et al. (Snyder et al., 1992). The system was prepared at room temperature by mixing the required
amount of 9.90% (w/w) Polyethylene Glycol 1000 (PEG) (Sigma Aldrich, Germany), 10.90% (w/w)
Na2CO3 (≥ 99%, Sigma Aldrich, Germany), and milliQ water in microtubes (Eppendorf, Germany)
to obtain the final weight of 1.5 mg (experiment performed in duplicate). Afterwards, 45 mg of sB
was added to the tube, which was thoroughly stirred for 20 minutes in the tube shaker. Then it
was centrifuged at 4500xg for 20 minutes to speed up the phase formation. The volumes of each
phase were determined, and the content of both phases was analysed for protein and
carbohydrate concentration. A system composed by 9.90% (w/w) PEG 1000, 10.90% (w/w)
Na2CO3, and milliQ water was prepared as negative control, as well as a mixture composed by
45 mg of algal biomass and 1.5 mL of milliQ water as a positive control. The concentration of
protein in each phase was determined by Lowry’s method (section 4.6.1) requiring a 2x dilution
with lysis buffer (120 mM TRIS pH 9, 4% SDS). For carbohydrate quantification (section 4.6.2),
samples were diluted 20x with milliQ water. The negative control was used as blank in both
analytical methods.
The partition coefficient of protein in the formed system was determined by Equation 4.6
𝐾 =[𝑃]𝑇
[𝑃]𝐵
Equation 4.6
where [P]T and [P]B are the protein concentration in the top phase and bottom phase,
respectively.
39
4.4 Assays with ionic liquids
4.4.1 Ionic liquid selection
The screening of ionic liquids for protein extraction and fractionation comprised three
stages. Firstly, using the physicochemical properties of the ionic liquids to predict their ability to
interact with the macroalga cell wall, ten ionic liquids were selected among the twenty-five
available in Bioprocess Engineering (BPE) group from Wageningen University and Research.
Several reports have identified features of ionic liquids associated with cellulose dissolution. For
instance, the anion hydrogen bond basicity (AHBB) is one of the properties that most influence
the ability of certain ionic liquids to dissolve cellulose (i.e. the better the hydrogen bond acceptor
ability of the anion, the higher the solubility of cellulose) (Stark et al., 2012). In this perspective,
based on a BPE group’s database3 comprising different physicochemical properties of several
ionic liquids and respective cations and anions, the ten ILs selected were those with the highest
values of anion hydrogen bond basicity.
A second selection allowed to reduce the number of candidates from ten to five. This
selection was based on a qualitative analysis of ionic liquid-macroalga interaction, IL costs
(information from suppliers) and toxicity (information from literature). In order to evaluate
qualitatively the interaction of ionic liquids and macroalgae, solutions of 40% (w/w) IL were
prepared for each of the 10 ILs (Table 4.3) in 2 mL-microtubes and 10 mg of sB was added to all
of them. Positive controls, composed by sB and milliQ, and negative controls, composed by 40%
(w/w) IL and milliQ water, were also prepared and the samples were then vortexed for 2 minutes
– the experiment was performed in triplicate for each ionic liquid.
Under this experimental setting, each suspension had a particular behaviour, and by its
observation (e.g. phase formation; supernatant colour development), the interaction of the tested
ionic liquids and alga was evaluated.
Table 4.3 | Ionic liquids tested and respective purity and supplier.
Ionic Liquid Purity Supplier
Choline acetate ([Ch][Ac]] 98% Iolitec, Germany
1-butyl-3-methylimidazolium acetate ([Bmim][Ac]) 95% Iolitec, Germany
1-ethyl-3-methyl-imidazolium dibutyl phosphate ([Emim][dbp]) 97% Iolitec, Germany
1-butyl-3-methylimidazolium dibutyl phosphate ([Bmim][dbp]) 98% Iolitec, Germany
1-butyl-3-methylimidazolium chloride ([Bmim][Cl]) 98% Iolitec, Germany
Choline chloride ([Ch][Cl]) 98% Sigma Aldrich, Germany
trihexyltetradecylphosphonium chloride ([6,6,6,14-P][Cl]) 97% Iolitec, Germany
tributyl(methyl)phosphonium methylsulfate ([1,4,4,4-P][MSO4]) 98% Sigma Aldrich, Germany
1-butyl-3-methylimidazolium dicyanamide ([Bmim][dca]) 95% Iolitec, Germany
trihexyltetradecylphosphonium dicyanamide ([6,6,6,14-P][dca]) 95% Iolitec, Germany
3 The databased was made by Edgar Suarez, member of BPE group of Wageningen University and Research, based on Cho et al. (Cho et al., 2012, 2011).
40
Finally, the third stage of the screening was carried out by quantitative analysis, and
allowed the selection the most suitable ionic liquid, among the five previously chosen, for the
following experiments. A mixture of 40% (w/w) IL and 10 mg of sB was prepared in lysing matrix
D tubes for each of the five ionic liquids (positive controls, composed by sB and milliQ water, and
negative controls, composed by 40% (w/w) IL and milliQ water, were also prepared) – the
experiment was performed in duplicate for each ILs and quadruplicate for positive controls.
Suspensions were placed in the tissue homogenizer and processed under a program composed
by three cycles of 60 seconds at 6500 rpm, with 120 seconds break between cycles. Samples
were then centrifuged for 10 minutes at 3500 rpm and analyzed for proteins by Bradford assay
(section 4.6.1), requiring a dilution of 5x with milliQ water. The ionic liquid that led to the highest
fraction of released proteins was selected to the following experiments.
4.4.2 Aqueous biphasic system: binodal curve
After the selection of the most suitable IL for the extraction of proteins, an aqueous biphasic
system composed by ionic liquid and salt was created for the extraction and separation of
proteins, requiring the construction of a binodal curve. Aqueous solutions of 50% (w/w) K2HPO4
(≥ 99%, Merck Millipore, Germany) and 60% (w/w) IL were prepared for the determination of the
corresponding binodal curve, which was established at room temperature and at atmospheric
pressure through the cloud point titration method (Kaul, 2000). Repetitive dropwise addition of the
aqueous salt solution to the ionic liquid aqueous solution (with known weight), was followed by
vortexing and time to settle. Upon settling, if a cloudy solution was formed (i.e. biphasic region),
the cloud point is considered reached and the concentration of phase-forming components was
dertermined based on the mass of aqueous salt solution added. The mixture was then diluted
with milliQ water until the formation of a clear solution (i.e. monophasic region), the weight of
milliQ water added was recorded and the above process was repeated until enough points were
measured for an accurate curve. All the measurements were taken as weight percentage.
The experimental binodal curve was fitted by least-squares regression according to
Equation 4.7 (Merchuk et al., 1998) (correlation shown in Appendix B).
[𝐼𝐿] = 𝑎𝑒𝑥𝑝[(𝑏 × [𝐾2𝐻𝑃𝑂4]0.5) − (𝑐 × [𝐾2𝐻𝑃𝑂4]3)]
Equation 4.7
4.4.3 Bead mill experimental setup
The DYNO-Mill Research Lab (Willy A. Bachofen AG Maschinenfabrik, Switzerland) was
used to conduct simultaneous disintegration, extraction and fractionation of compounds from
macroalgae. The DYNO-mill (Figure 4.1) consists of a horizontal milling chamber (Vchamber = 80
mL), with the engine connected over a central shaft. 65% (v/v) of the milling chamber volume was
filled with 1 mm ZrO2 beads (YTZ® beads, Tosoh, Japan) and a suspension composed of 1%
(w/w) sB, 11% (w/w) IL and 24% (w/w) K2HPO4 (Vf = 200 mL) was fed to the system. The beads
were accelerated in the opposite direction of the shaft (the average of agitator shaft speed was
2039 rpm) by a single DNYO-accelerator (Ø 56.20 mm). Different layers of beads were formed
41
moving at different speeds causing a grinding effect responsible for cell disintegration. The major
part of the energy introduced (energy input ~0.04 kWh) was converted to heat due to friction and,
therefore, a cooling jacket integrated in the milling chamber and a cooling coil in the feed funnel
were used to remove the heat, avoiding temperatures that could lead to protein denaturation (the
average temperature was ~24ºC). The algal suspension and grinding beads were separated by
a sieve plate at the outlet, and the algal suspension was fed back to the feed funnel. The bead
mill was operated under batch recirculation mode (i.e. the algal suspension was recirculated,
passaging multiple times through milling chamber) for 1 hour. In the end of the experiment, the
processed mixture was removed from the bead mill to 50 mL-centrifuge tubes (Sarstedt,
Germany). After time to settle and phase formation, the weight of each phase was noted down
and the top and bottom fractions were analysed for proteins and carbohydrates, requiring a 2x
dilution with milliQ water for the former and a 100x dilution for the latter. A negative control
composed by 11% (w/w) IL, 24% (w/w) K2HPO4 and water was used as blank in both analytical
methods.
Figure 4.1 | DYNO-mill equipment and respective components.
4.5 Recovery of ionic liquid
A bead mill experiment was run under the same conditions described in section 4.4.3,
however the feed suspension was composed by 15% (w/w) IL and 10% (w/w) sB. The resulting
mixture was centrifuged, at 4700 rpm, for 20 minutes (Alegra X-30R, Beckman Coulter, USA) and
aliquots of supernatant were analysed for protein (sample previously diluted 3x with milliQ water).
An ultrafiltration process was carried out in order to recover the ionic liquid from the algal
extract (previously diluted 2x with milliQ water). Two different setups were tested: a stirred
ultrafiltration cell (model 8050, EMD Millipore AmiconTM Bioseparations, USA) – Figure 4.2 – and
a centrifugal filter unit (Amicon Ultra-4 centrifugal filter with Ultracel-3 membrane, Millipore, USA)
– both setups were performed in duplicate.
In the first setup, a Biomax polyethersulfone membrane (EMD Millipore AmiconTM
Bioseparations, USA) with a pore size of 10 kDa was used. The stirred cell was filled with 10 mL
42
of supernatant, and the stir bar/pressure cap assembly was fitted onto the cell. The stirred cell
was placed on a magnetic stirrer plate with the stirring speed set to 600 rpm, preventing the build-
up of a biomass layer at the membrane surface. The pressure inside the chamber was maintained
at 32.50 psi by nitrogen gas, which provided the driving force for the filtration. The separation
process was carried out at a constant flow rate (0.10 mL.min-1) until the retentate volume is
reduced to approximately 2 mL.
Figure 4.2 | Schematic diagram of stirred cell ultrafiltration assembly used for recovering the ionic liquid from
the supernatant. Adapted from (Merck Millipore, 2015).
In the second setup, a centrifugal filter unit equipped with an ultracel-3 membrane (pore
size of 3 kDa) was filled with 4 mL of supernatant and spun, at 4000xg, for 10 minutes (Allegra
X-30R Centrifuge, Beckman Coulter, USA). The centrifugation step was repeated 4 times until
the filtrate volume reached 3 mL.
The concentration of ionic liquid, protein and carbohydrates in retentates and filtrates were
determined. Proteins were quantified by Bradford assay (section 4.6.1), whereas carbohydrates
and ionic liquid were quantified according to section 4.6.2 and section 4.6.3, respectively. The
required dilutions for each assay are described in Table 4.4.
Table 4.4 | Filtrate and retentate dilutions required for protein, carbohydrate and ionic liquid quantification.
Protein quantification Carbohydrate quantification IL quantification
Retentates 3x 500x 50x
Filtrates Without dilution 50x 50x
The rejection coefficients of proteins and carbohydrates in both membranes were determined
according to Equation 4.8, in which Cp and Cf stand for the concentration of protein/carbohydrate
in the permeate and in the feed, respectively.
𝑅 = 1 −𝐶𝑝
𝐶𝑓
Equation 4.8
43
4.6 Analytical methods
4.6.1 Protein quantification
Lowry’s method or Bradford assay were used for protein quantification depending on the
assay. For both methods, a calibration curve was prepared using bovine serum albumin (BSA)
(Sigma Aldrich, Germany) as standard, for a working range of 0–1.40 mg.mL-1 for Lowry’s method
and 0–2 mg.mL-1 for Bradford assay.
Lowry’s method was performed, with a commercial kit (DCTM Protein Assay, Bio-Rad,
USA), following the microplate assay protocol. 10 µL of each standard and unknown samples
were transferred into a 96-well plate (Greiner Bio-One International, Austria), and after the
addition of Lowry’s reagents, as described in the protocol, the microplate was covered with
aluminium foil and incubated, at room temperature, for 30 minutes. The absorbance was then
measured at 750 nm using a microplate reader (Infinite M200, Tecan, Switzerland).
The Bradford assay was also performed following the microplate assay protocol of a
commercial kit (PierceTM Coomassie protein assay, Thermo Fisher Scientific, USA). 5 µL of
standards and unknown samples were added to a 96-well microplate and after the addition of 250
µL of Coomassie reagent, the microplate was shaken for 30 seconds and incubated 10 minutes
at room temperature. The absorbance was measured at 595 nm in the microplate reader.
According to the experiment, the concentration of protein was determined by direct
interpolation from the calibration curves described in Appendix A.1. The total protein content in
macroalgae and the fraction of released proteins in each extraction procedure were determined
by Equation 4.9 and Equation 4.10, respectively.
% 𝑇𝑜𝑡𝑎𝑙 𝑝𝑟𝑜𝑡𝑒𝑖𝑛 =[𝑃𝑟𝑜𝑡𝑒𝑖𝑛](𝑚𝑔. 𝑚𝐿−1) × 𝑉𝑜𝑙𝑢𝑚𝑒𝑠𝑎𝑚𝑝𝑙𝑒(𝑚𝐿)
𝐷𝑊𝑠𝑎𝑚𝑝𝑙𝑒(𝑚𝑔)× 100
Equation 4.9
% 𝑅𝑒𝑙𝑒𝑎𝑠𝑒𝑑 𝑝𝑟𝑜𝑡𝑒𝑖𝑛 =[𝑃𝑟𝑜𝑡𝑒𝑖𝑛]𝑒𝑥𝑡𝑟𝑎𝑐𝑡(𝑚𝑔. 𝑚𝐿−1) × 𝑉𝑜𝑙𝑢𝑚𝑒𝑒𝑥𝑡𝑟𝑎𝑐𝑡(𝑚𝐿)
𝐷𝑊𝑠𝑎𝑚𝑝𝑙𝑒(𝑚𝑔) ×%𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑜𝑡𝑒𝑖𝑛
100
× 100
Equation 4.10
4.6.2 Carbohydrate quantification
The quantification of carbohydrates was carried out according to Dubois et al. (Dubois et
al., 1956). Calibration curves were prepared using glucose (Sigma Aldrich, Germany) as
standard, for a working range of 0-0.10 mg.mL-1. 40 µL of standard and unknown samples were
transferred to a 96-well microplate, followed by the addition of 40 µL of 5% (w/w) phenol solution
(molecular biology grade, Sigma Aldrich, Germany), and 200 µL of concentrated H2SO4 (95-98%,
Sigma Aldrich, Germany). The microplate was covered by aluminum foil and incubated, at room
temperature, for 10 minutes, followed by an incubation step for 35 minutes, at 35ºC. The
absorbance was measured at 483 nm in the plate reader.
The concentration of carbohydrates was determined, according to the experiment, by direct
interpolation from the calibration curves described in Appendix A.2. The percentage of total
44
carbohydrate content in macroalgae and the fraction of released carbohydrates in each extraction
procedure were determined by Equation 4.11 and Equation 4.12, respectively.
% 𝑇𝑜𝑡𝑎𝑙 𝑐𝑎𝑟𝑏𝑜ℎ𝑦𝑑𝑟𝑎𝑡𝑒𝑠 =[𝐶𝑎𝑟𝑏𝑜ℎ𝑦𝑑𝑟𝑎𝑡𝑒𝑠](𝑚𝑔. 𝑚𝐿−1) × 𝑉𝑜𝑙𝑢𝑚𝑒𝑠𝑎𝑚𝑝𝑙𝑒
𝐷𝑊𝑠𝑎𝑚𝑝𝑙𝑒(𝑚𝑔)× 100
Equation 4.11
% 𝑅𝑒𝑙𝑒𝑎𝑠𝑒𝑑 𝑐𝑎𝑟𝑏𝑜ℎ𝑦𝑑𝑟𝑎𝑡𝑒𝑠 =[𝐶𝑎𝑟𝑏𝑜ℎ𝑦𝑑𝑟𝑎𝑡𝑒𝑠]𝑒𝑥𝑡𝑟𝑎𝑐𝑡(𝑚𝑔. 𝑚𝐿−1) × 𝑉𝑜𝑙𝑢𝑚𝑒𝑒𝑥𝑡𝑟𝑎𝑐𝑡(𝑚𝐿)
𝐷𝑊𝑠𝑎𝑚𝑝𝑙𝑒(𝑚𝑔) ×%𝑇𝑜𝑡𝑎𝑙 𝐶𝑎𝑟𝑏𝑜ℎ𝑦𝑑𝑟𝑎𝑡𝑒𝑠
100
× 100
Equation 4.12
4.6.3 Ionic liquid quantification
The concentration of ionic liquid in the fractions resulting from the ultrafiltration experiment
was estimated using an Ultra High-Performance Liquid Chromatography (UPLC) apparatus
(Nexera X2, Shimadzu, USA) equipped with a RezexTM ROA-Organic Acid H+ 8% (300mm x
7.8mm) column (Phenomenex, USA), coupled with a security guard (Phenomenex, USA), a pump
(LC-30AD, Shimadzu, USA), an autosampler (SIL-30AC, Shimadzu, USA) and a refractive index
detector (RID-20A, Shimadzu, USA). The injection volume was 20 µL and the isocratic elution
was performed with 0.005 N H2SO4 solution, as mobile phase. The column was kept at 60ºC,
under a pressure of 55 bar, and the pump operated at a flow rate of 0.60 mL.min-1.
The concentration of ionic liquid was determined by direct interpolation of the calibration
curve described in Appendix A.3.
The ionic liquid recovery was calculated according to Equation 4.13.
𝐼𝐿𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑦(%) =[𝐼𝐿]𝑓𝑖𝑙𝑡𝑟𝑎𝑡𝑒(𝑚𝑔. 𝑚𝐿−1) × 𝑉𝑓𝑖𝑙𝑡𝑟𝑎𝑡𝑒(𝑚𝐿)
[𝐼𝐿]𝑓𝑒𝑒𝑑(𝑚𝑔. 𝑚𝐿−1) × 𝑉𝑓𝑒𝑒𝑑(𝑚𝐿)× 100
Equation 4.13
4.6.4 Native-PAGE gel electrophoresis
Native-PAGE electrophoresis was performed to study how the conditions used in aqueous
and alkaline extraction and mechanical grinding extraction affect the protein conformation.
Aliquots from extracts were diluted with milliQ water in order to obtain solutions with the same
concentration of protein. Afterwards, those samples were diluted with native sample buffer
(BioRad, USA) in a proportion of 1:2. After mixing, 25 µL of each sample were transferred into a
4-20% Criterion Tris-Glycine eXtended (TGX) precast gel (BioRad, USA), as well as 5 µL of
NativeMark™ Unstained Protein Ladder (Invitrogen, EUA). Samples were run in 10x Tris/Glycine
buffer (BioRad, USA), at 125 V, for 75 minutes.
Native gel was stained using the Pierce® silver stain kit (Thermo Fisher Scientific, USA),
following the respective user guide. The image of native gel were acquired with LabScan 6.0
software, using the ImageScanner III (GE Healthcare, UK).
45
4.7 Statistical Analysis
R-studio was used to carry out the statistical analysis of results. Replicates were performed
for almost all experiments and data are presented as mean values ±SD (standard deviation).
Experimental data were analysed by one-way analysis of variance (ANOVA) with the significance
level of p=0.05. When significant differences were found (i.e. p<0.05), the Tukey’s Honest
Significant Difference (HSD) test was used to detect significant differences among treatments.
46
47
5 5. Results and Discussion
48
49
5.1 Chemical composition of Ulva lactuca
The freeze-dried Ulva lactuca was analysed and its composition determined. The average
composition is shown in Table 5.1.
Table 5.1 | Chemical characteristics of macroalga U. lactuca (% w/w on dry basis).
Components Values
Total Solids (%) 96.50±0.87
Ash 22.80±0.35% dry wt.
Protein 17.81±0.79% dry wt. (Lowry’s method)
3.70±0.30% dry wt. (Bradford assay)
Lipid 4.95±0.35% dry wt.
Carbohydrate 60.92±14.62% dry wt.
The moisture content (3.50±0.87% dry wt.) is very low because the starting algal biomass
has been lyophilized previously. Lyophilization (freeze-drying) is a gentle dehydration process
that allows the removal of cellular water, leading to a residual moisture contents in the biomass
(Dejoye et al., 2011; Wessman et al., 2013).
The ash content (22.80±0.35% dry wt.) is comparable with values published in previous
studies. For instance, Wong and Cheung measured 21.30±2.78% dry wt. of ash in U. lactuca,
whereas Yaich et al. determined 19.59±0.51% dry wt. of ash in the same species (Wong and
Cheung, 2000; Yaich et al., 2015).
The lipid content (4.95±0.35% dry wt.) is found to be significantly higher than that usually
found in U. lactuca, which varies between 0.30% dry wt. and 1.79% dry wt.(Ortiz et al., 2006;
Tabarsa et al., 2012; Wong and Cheung, 2000). This difference could be attributed to factors such
as the conditions for seaweed growth such as climate and geography, as well as the timing for
harvest or the method used for lipid extraction (Yaich et al., 2015).
The carbohydrate content determined in this study (60.92±14.62% dry wt.) agrees with
values reported in literature for U. lactuca (De Pádua et al., 2004; Ortiz et al., 2006). However,
although the value is within expectations, it is important to note the large standard deviation
obtained, which indicates some variability in the value of carbohydrate content determined
between the different biological replicates. In order to validate this analysis, the experiment should
be performed again.
The total protein content in U. lactuca varies considerably between published studies
(Table 5.2). In addition to season’s related variability, the method applied for protein quantification
strongly affects the reliability of the determined value (Cerna, 2011).
50
Table 5.2 | Protein content in Ulva lactuca.
Protein content in U. lactuca
% (w/w) dry wt. Reference
8.46±0.01 (Yaich et al., 2015)
10.69±0.67 (Tabarsa et al., 2012)
7.06±0.06 (Wong and Cheung, 2000)
27.02±1.10 (Ortiz et al., 2006)
18.35 (De Pádua et al., 2004)
18.10 (Shuuluka et al., 2013)
17.88±0.10 (Khairy and El-Shafay, 2013)
The determination of total protein content in this study was carried out by two different
colorimetric methods: the Lowry’s method and the Bradford assay. The total protein content
determined by Lowry’s method is substantially higher than that determined by Bradford assay –
17.81±0.79% dry wt. and 3.70±0.30% dry wt., respectively. Several authors have reported that
Bradford assay generates lower protein values for a large number of organisms compared to
Lowry’s method (Berges et al., 1993; Clayton Jr. et al., 1988; Eze and Dumbroff, 1982). The
comparison between both methods was already performed in other macroalgal species and the
results confirmed the general trends found in this study, with a Lowry:Bradford ratio varying from
1.50 to 3.20 (Barbarino and Lourenço, 2005). Actually, in this study, the Lowry:Bradford ratio is
found to be slightly higher (4.80), which might be due to the procedure followed for sample
preparation. In the Lowry’s method, the biomass was resuspended in lysis buffer containing SDS,
an anionic surfactant/detergent that assists in the dissolution of membranes by disrupting the
hydrophobic-hydrophilic interactions among their molecules (Brown and Audet, 2008).
Nevertheless, the interference of ionic detergents with Coomassie reagent, prevents the use of
SDS in samples prepared for Bradford assay (Marshall and Williams, 2004). In order to ensure
that all the proteins present in macroalgae were quantified, as it is assumed to happen with the
Lowry’s quantification, the biomass was suspended in 0.1 M NaOH, assuming that such alkaline
environment was sufficiently harsh for algal cells as SDS. However, it might not be and cell
membranes were not completely disintegrated, avoiding the release of all the proteins present in
seaweeds. In consequence, the value of total protein measured by Bradford may be even more
underestimated than expected, explaining the higher Lowry:Bradford ratio.
The difference between the alga protein content measured by Bradford assay and Lowry’s
method could be also explained by the interaction of respective reagents with proteins. The
development of colour in Bradford protein assay is associated with the binding of Coomassie dye
to basic and aromatic amino acids (Compton and Jones, 1985). Most of seaweeds show relatively
low concentrations of basic amino acids, such as lysine and histidine, as well as aromatic amino
acids, tyrosine and tryptophan (Bleakley and Hayes, 2017). Therefore, the binding with proteins
occurs mainly with arginine and phenylalanine residues, a fact that may contribute to an
underestimation of protein content. On the other hand, the Folin-Ciocalteu reagent used in the
Lowry’s method interacts with all peptide bonds and the colour development can still be enhanced
51
by the presence of certain amino acids (e.g. tyrosine, tryptophan, cysteine and asparagine)
(Legler et al., 1985). Therefore, the quantification of protein tends to be greater.
According to Barbarino and Lourenço, the Lowry’s method is the most reliable way to
determine the protein content in algae since they found out that results generated with Lowry’s
method were more similar to the data obtained from the sum of the amino acid residues
(Barbarino and Lourenço, 2005). In this sense, it is assumed that the total protein content in Ulva
lactuca is 17.81±0.79% dry wt., the value determined by Lowry’s method. The total protein content
is comparable with certain values reported in literature (Table 5.2). In particular, with the value
determined by Khairy and El-Shafay (Khairy and El-Shafay, 2013), which results from the analysis
of U. lactuca collected during the summer, as the samples used in this study.
The Bradford analysis is nevertheless needed for the determination of the protein recovery
yield when using ionic liquids. This is because of the interference between Lowry’s reagents and
ionic liquids, which prevents the use of this method in IL-based experiments. Since the fraction of
protein released by each extraction procedure is calculated using the total protein content in
macroalga (Equation 4.10), for accurate and reliable results, the quantification of protein in the
extracts should be performed with the same method used for total protein quantification.
The composition of the algal batch under study was also evaluated by thermogravimetric
analysis4 and the results are presented in Table 5.3. The protein content is similar to the one
obtained by the Lowry’s method. The amount of carbohydrates determined by thermogravimetric
analysis is slightly lower than the one determined by the Dubois method. However, besides the
inherent differences between the principles of each method and the absence of thermogravimetric
analysis replicates to validate the results, one must consider that Dubois’ quantification has a
large SD associated, which leads to concerns about the reliability of results. The lipid and ash
contents also present some variation. Replicates of thermogravimetric analysis are recommended
to confirm the results obtained.
Table 5.3 | Ulva lactuca composition determined by thermogravimetric analysis.
Component % (w/w) dry wt.
Ash 19.36
Protein 16.00
Lipid 7.60
Carbohydrate 53.80
To conclude, it is important to note that, with the percentages obtained for ashes, proteins,
carbohydrates and lipids, it is possible to close the mass balance of U. lactuca composition,
suggesting the reliability and accuracy of the experiments performed for biomass
characterization.
4 Experiment carried out at Instituto Superior Técnico, Department of Chemical Engineering, by Dr. Ana Paula Vieira Soares Pereira Dias (Ferreira et al., 2016).
52
5.2 Reference Cases
As mentioned in Materials and Methods (section 4.3), three conventional methods used for
macroalga protein extraction were replicated in this study. Extractions by means of aqueous and
alkaline solutions (sequential), high-shear force and aqueous biphasic system (PEG/Na2CO3)
were used as reference cases because, among the protein extraction techniques reported in
literature, these methods have led to higher yields of protein recovery in Ulva species (Table 2.1).
Aqueous and alkaline extraction (sequential) showed better performance compared to
high-shear force and ATPS extractions (Figure 5.1).
Figure 5.1 | Percentage of protein recovery by high-shear force, aqueous and alkaline extraction (sequential)
and aqueous biphasic system PEG/Na2CO3 (sum of protein extracted into top and bottom phases). Protein
yield expressed as % of total protein (Protein extracted/Total proteins x 100). Results are the means of
triplicate determination for aqueous and alkaline extraction and duplicate determinations for high-shear force
extraction and aqueous biphasic system ±SD; (*) - means are statistically equal (p>0.05).
The overall fraction of protein extracted by aqueous and alkaline extraction (sequential)
(49.08±6.66%) seems to be significantly higher than that obtained by the same method for Ulva
rigida (26.80±1.3%) and Ulva rotundata (36.10±1.4%) (Fleurence et al., 1995). Although these
authors used the same procedure to extract the protein fraction from both Ulva species, the
recovery yields varied considerably between species. One can thus conclude that the protein
recovery yield attained using this procedure is a function of the green alga species.
Separately, osmotic shock (aqueous extraction) led to a protein recovery yield of
5.04±0.95%, whereas the alkaline extraction led to 45.42±8.81%, suggesting that the majority of
macroalgal proteins are insoluble in water. At high pH (~12), most of the proteins are negatively
charged due to the deprotonation of amine groups, which enhances the protein-solvent
interaction, thereby increasing the protein solubility (Valenzuela et al., 2013). In addition, since
many water-insoluble polysaccharides are solubilized under basic pH (Hostettmann, 2014; Knill
and Kennedy, 2003), the alkaline conditions may also have aided in cell wall disruption, leading
to a higher protein recovery yield (Zhang et al., 2015).
0%
10%
20%
30%
40%
50%
60%
High-shear forceextraction
Aqueous biphasicsystem
Aqueous & alkalineextraction
Pro
tein
Recovery
*
*
*
*
53
Concerning the extraction by high-shear force, Barbarino and Lourenço extracted protein
from Ulva fasciata with a recovery yield of 7.30±0.84 g protein/100 g dry wt. (Barbarino and
Lourenço, 2005). On the other hand, the same method was used to extract proteins from the red
seaweed Palmaria Palmata, obtaining a recovery yield of 6.92±0.12 g protein/100 g dry wt.
(Harnedy and FitzGerald, 2013). In this study, only 0.25±0.04 g of protein was extracted per 100
g alga dry wt. (6.68±1.09%), which is an extremely low yield compared to the literature. The
procedure used in this study was adapted from both published works (Barbarino and Lourenço,
2005; Harnedy and FitzGerald, 2013), in which the mechanical grinding was followed by
extraction under strong alkaline conditions (pH~12), known to lead to higher recovery yields. In
order to avoid the expected protein denaturation due to the extremely basic pH (Marambe and
Wanasundara, 2017), the procedure followed in this study did not include the strong alkaline
conditions. Alternatively, the algae were previously resuspended in a pH~8 solution, providing
mild alkaline conditions, which could improve the protein solubility without compromising their
functionality, and subsequently processed by mechanical grinding, saving time and costs due to
a single-step process. However, the modified procedure fell short of expectations, displaying
much lower yields, which suggests that the pH was not high enough to solubilize the proteins.
It should also be noted that the high-shear homogenization is supposed to damage the
alga cells inducing the protein release (e.g. by reducing the particle size, enhancing mass transfer
between solvent and algae and/or fractionate large agglomerates), however when comparing the
recovery yield obtained by mechanical grinding (6.68±1.09%) and aqueous extraction
(5.04±0.95%), there is not a significant difference between both procedures (p=0.17). This fact
suggests that only the effect of the solvent was playing a role while Ultra-Turrax homogenizer
worked exclusively as a mixing device. A reasonable explanation for this unexpected result is the
coffee grinder use in alga sample preparation, which may have reduced the algae to the minimum
size, making the Ultra-Turrax action useless.
The overall fraction of protein extracted by ATPS was 10.52±0.35% (sum of protein
quantified in both formed phases (9.11±0.32% in the top phase and 1.42±0.03% in the bottom
phase). The same procedure was used by Fleurence et al. to extract proteins from Ulva rigida
and Ulva rotundata, achieving recovery yields of 19.10±1.1% and 31.56±2.1%, respectively
(Fleurence et al., 1995). In Figure 5.2, it is possible to observe that, in addition to the top and
bottom phases formed, there was also the formation of an interphase in sample tubes. The
interphase was solid and, although it was not analysed for proteins, these molecules may be part
of its composition. In this sense, besides the influence of the species on the protein recovery yield,
the overall fraction of protein extracted may be underestimated because the contribution of
interphase proteins was not considered.
54
Figure 5.2 | Triplicates of positive controls (algae and milliQ water), negative controls (milliQ water, PEG1000
and Na2CO3) and ATPS formed by milliQ water + PEG1000 + Na2CO3 + algae.
In addition to extract proteins from algae, the ATPS can still separate them from other
components, for instance, carbohydrates. The phase separation results from the salting-out of
the hydrophilic polymer PEG and leads to two phases of different hydrophilic character. Protein
partitioning is known to be highly selective in PEG/salt phase systems (i.e. the partition
coefficients are high (K>>1)) (Iqbal et al., 2016). Generally, the proteins concentrate in the PEG-
rich phase, with a more hydrophobic character, while most of the polysaccharides remains in the
salt-rich phase (Jordan and Vilter, 1991). This trend was confirmed in this study, as displayed in
Figure 5.3, which describes the distribution of the extracted proteins and carbohydrates between
the top and bottom phases. Most of the proteins were concentrated in the top phase: i.e. the PEG-
rich phase (K=11.25), whereas the carbohydrates had higher affinity to the bottom phase, the
salt-rich phase.
Figure 5.3 | Distribution of extracted proteins and carbohydrates between the top and bottom phases of the
ATPS composed by PEG1000 + water + Na2CO3.
The proteins were quantified in the supernatant of positive controls. The protein recovery
yield of 9.98±0.83% means that the presence of PEG or salt in the solution did not significantly
increase the percentage of protein released, since the overall recovery yield in the ATPS was
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Proteins Carbohydrates
Bottom phase Top phase
55
10.52±0.35% (p=0.46). In this perspective, the importance of the PEG/Na2CO3 ATPS lies mainly
in its ability to separate the proteins from carbohydrates rather than in its use for protein extraction.
To evaluate how the conditions used in high-shear force extraction and aqueous and
alkaline extraction affect the protein conformation, a Native-PAGE electrophoresis was
performed, and the results are presented in Figure 5.4.
Figure 5.4 | Electrophoresis gel of protein extracts under non-denaturing conditions. Lane 1: Protein ladder;
Lane 2: Aqueous extract (positive control); Lane 3: Alkaline extract; Lane 4: Mechanical grinding extract.
In the native-PAGE electrophoresis, the proteins are separated according to the net
charge, size, and shape of their native structure. The electrophoretic migration occurs because
most proteins carry a net negative charge in alkaline running buffers (the higher the negative
charge density, the faster a protein will migrate). At the same time, the gel matrix acts as a
molecular sieve, regulating the movement of proteins according to their size and three-
dimensional shape (small proteins face only a small frictional force, migrating more, while higher
proteins face a large frictional force, migrating less) (Nelson and Cox, 2008).
Considering the protein pattern of aqueous extract (lane 2) as a positive control (performed
under mild conditions in order to ensure protein conformation), it is possible to draw conclusions
about how the extraction under strong alkaline conditions (pH~12) and the mechanical grinding
under mild alkaline conditions (pH~8) can affect the algal proteins conformation. The protein
pattern of alkaline extract (lane 3) is clearly different from the positive control, suggesting that
proteins lose their native structure during the extraction. Although the alkaline extraction leads to
the highest recovery yields (~45%), there are strong evidences that the functionality of proteins
may be compromised, making this method unfeasible for applications in which the native structure
of proteins is needed.
On the other hand, the mild alkaline conditions used in mechanical grinding did not
compromise the native structure of the proteins since the protein pattern (lane 4) is similar to
positive control. However, this method leads to low yields of protein recovery (~7%) as previously
discussed.
56
5.3 Experiments with ionic liquids
5.3.1 Ionic liquid selection
The selection of the most suitable ionic liquid, among those available to test, comprised
three steps. Table 5.4 shows the output of the first selection, in which the 10 ILs selected and the
respective anion hydrogen bond basicity are enumerated.
Table 5.4 | Ionic liquids selected and respective anion hydrogen bond basicity.
Ionic Liquid Anion hydrogen bond basicity
[Ch][Ac] 4.84
[Bmim][Ac] 4.84
[Emim][dbp] 4.36
[Bmim][dbp] 4.36
[Bmim][Cl] 4.25
[Ch][Cl] 4.25
[6,6,6,14-P][Cl] 4.25
[1,4,4,4-P][MSO4] 2.81
[Bmim][dca] 1.84
[6,6,6,14-P][dca] 1.84
The second selection included the qualitative analysis of the IL-alga interaction, the cost
and toxicity of each ionic liquid. As described in Materials and Methods (section 4.4.1), solutions
of 40% of ionic liquid were prepared for the experiments because the use of pure ILs is only viable
when they are liquid at room temperature and have low viscosities. Otherwise, the extraction
efficiencies will be low due to the limited mass transfer coefficients and the poor penetration of
ILs into the biomass structure (Passos et al., 2014). Although high temperatures could be applied
when working with pure ILs, either to overcome their melting points or to reduce their viscosities,
they might lead to the thermal degradation of target molecules as well as represent further
economic concerns. Therefore, to overcome these issues, the ionic liquids were diluted in water,
reducing their viscosity and allowing the extraction process to occur at room temperature. Besides
the lower energy consumption, there is also a reduction on the overall solvent cost.
The suspensions obtained after mixing IL + water + algae are shown in Figure 5.5. After
visual inspection it was possible to infer about the IL-alga interaction. Certain pigments located
inside the cells of Ulva lactuca are responsible for its green colour. Therefore, the development
of such colour in the supernatants suggests the release of those pigments, implying the
destabilization of the cell wall. In this sense, it is possible to conclude that the greener the
supernatant, the larger the pigment release, and the greater the interaction of the IL with the cell
structures. Reminding the first selection in which the ability of the IL anion to act as hydrogen
bond acceptor was used to predict the IL-macroalga interaction, it is expected that ionic liquids
with higher AHBB (Table 5.4) lead to a greener supernatant.
57
Figure 5.5 | Suspensions obtained for each ionic liquid after mixing IL + water + algae. A positive control (+
Ctrl) – algae and water – is placed next to all the samples.
[Choline][Ac] and [Bmim][Ac] are the ionic liquids with the highest AHBB (Table 5.4),
however their supernatants were almost uncoloured. On the other hand, [1,4,4,4-P][MSO4] had
one of the greenest supernatants and has a low AHBB. These evidences contradict what was
expected and, although ILs with higher AHBB are the most effective in cellulose dissolution, it is
not possible to predict their interaction with macroalgae based only on this parameter. However,
there are some relations that can be made based on the data reported in literature and the results
obtained in this study. For instance, it is recognized that the choice of the IL cation also affects
the cellulose solubility and, although data are not always consistent, it is generally accepted that
the length of the cation’s alkyl chains progressively reduces the dissolution of cellulose (Pinker et
al., 2009). The [Emim][dbp] supernatant is greener than the [Bmim][dbp] supernatant, suggesting
a greater interaction of macroalgae and [Emim][dbp]. This behaviour can be easily explained by
the shorter alkyl chain of [Emim] cation in comparison to [Bmim], increasing the ability of the IL to
solubilize cellulose.
Comparing two ILs with the same cation and different anions, for instance [Bmim][dbp] and
[Bmim][Cl], it is evident how the AHBB affects the interaction of both ILs with macroalgae.
Although the slight difference between the AHBB of [Bmim][dbp] and [Bmim][Cl] (4.36 and 4.25,
respectively), it is sufficient for the [Bmim][dbp] supernatant to be much greener than [Bmim][Cl]
supernatant.
58
There was phase formation in the mixtures composed by [6,6,6,14-P][Cl] and [6,6,6,14-
P][dca], indicating the highly hydrophobic character of both ionic liquids. Their immiscibility in
water coupled with the high viscosity arising from the long alkyl chain (Zhang et al., 2017), makes
difficult the use of both ionic liquids in macroalga cell disintegration, which may only be achieved
under intensive mixing, for instance, using Ultra-Turrax. Considering an IL-based ATPS, only ILs
miscible in water can be used for its formation because, when dealing with highly hydrophobic
ILs, two phases already exist before the addition of any salting-out agent, and one of the phases
is far from being aqueous due to these ILs’ low solubility in water and vice-versa (Freire et al.,
2007). Moreover, experimental studies have clearly documented the correlation between
increased hydrophobicity and increased cytotoxicity (Ranke et al., 2007). Hydrophobic ionic
liquids have a greater ability to interact with hydrophobic patches on proteins and cell membranes
in living cells, which facilitates the uptake of ILs into organisms (Matzke et al., 2010), and a
consequent inhibition of the enzyme activity (Jing et al., 2016). All the mentioned issues
contributed to the exclusion of both ILs from the list of suitable candidates.
[Bmim][dca] and [Choline][Ac] were excluded due to their high cost (Table 5.5) and lack of
colour development in the supernatants. On the other hand, and although its supernatant was
one of the greenest, [1,4,4,4-P][MSO4] was also excluded because it is recognized that
phosphonium-based ionic liquids are generally more toxic than imidazolium-based ILs (Ventura
et al., 2012).
Table 5.5 | Ionic liquids and estimated costs.
Ionic Liquid Cost (€.g-1) Reference
[Ch][Cl] 0.06 (Sigma Aldrich, 2017a)
[Bmim][Ac] 0.78 (Sigma Aldrich, 2017b)
[Bmim][Cl] 1.31 (Sigma Aldrich, 2017c)
[6,6,6,14-P][Cl] 1.63 (Sigma Aldrich, 2017d)
[1,4,4,4-P][MSO4] 2.15 (Gute chemie abcr, 2017)
[6,6,6,14-P][dca] 3.22 (Sigma Aldrich, 2017e)
[Ch][Ac] 7.04 (Sigma Aldrich, 2017f)
[Emim][dbp] 8.72 (Sigma Aldrich, 2017g)
[Bmim][dbp] 9.20 (Santa Cruz Biotechnology, 2017)
[Bmim][dca] 16.22 (Sigma Aldrich, 2017h)
Therefore, the 5 ILs selected for the following analysis were [Ch][Cl], [Emim][dbp],
[Bmim][dbp], [Bmim][Ac] and [Bmim][Cl]. There was no colour formation in the [Ch][Cl]
supernatant, however it is the cheapest IL and choline-based ILs are considered biodegradable
and non-toxic (Gadilohar and Shankarling, 2017), reasons why it was considered for the following
analysis. The supernatants of [Emim][dcp] and [Bmim][dcp] were the greenest ones, hence they
were considered promising candidates. In turn, the supernatants of [Bmim][Ac] and [Bmim][Cl]
were colourless, but both ILs have already been applied in food and bioproduct industries (Toledo
Hijo et al., 2016). This is an indication of the applicability of these ILs in the food sector, the same
area of application of the proteins that will be extracted from macroalgae to be used as functional
ingredients in food.
59
Table 5.6 | Output of the third selection. Features of the five ionic liquids selected.
[Emim][dbp] [Bmim][Ac] [Bmim][Cl] [Bmim][dbp] [Ch][Cl]
Hydrophilic
Lowest Cost
Non-toxic
Biodegradable
Applied in food industry
Greenest supernatants
The ability of the selected ILs to extract macroalgal proteins was then evaluated. According
to Figure 5.6, [Emim][dbp] appears to be the most suitable candidate, extracting 80.62±4.95% of
the total proteins present in Ulva lactuca.
Figure 5.6 | Percentage of protein recovery by each of the 5 ILs tested (+ control – algae and water). Protein
yield expressed as % of total protein (Protein extracted/Total proteins x 100). Results are the means of
duplicates for each IL and quadruplicates for positive control ±SD. a, b, c – means in columns with the same
letter are significantly equal (p>0.05).
No significant difference is observed in terms of protein recovery yield between [Bmim][Cl],
[Bmim][dbp] and [Emim][dbp]. However, the cost of [Bmim][dbp] is higher than [Emim][dbp] (Table
5.5) and the larger alkyl chains of both [Bmim][Cl] and [Bmim][dbp] increase their toxicity, facts
that strengthen the potential of [Emim][dbp] as the most suitable ionic liquid.
In turn, [Ch][Cl] and [Bmim][Ac] extract almost the same amount of protein as the positive
control – no significant differences were observed between the protein recovery yields of [Ch][Cl]
vs positive control (p=0.99) and [Bmim][Ac] vs positive control (p=0.87) – indicating the
ineffectiveness of these ILs for U. lactuca protein extraction.
Concerning the reference cases, the extraction efficiency obtained by [Emim][dbp] is
significantly higher than that obtained by any of the conventional methods tested. Moreover,
although the effect of the ionic liquid in protein conformation has not been evaluated in this
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
(+) Control [Ch][Cl] [Bmim][Ac] [Bmim][Cl] [Bmim][dbp] [Emim][dbp]
Pro
tein
Recovery
c
c
a a b
a b
b c
60
experimental work, published studies have shown that certain ILs improve the stability and half-
lives of various proteins and enzymes and allow some enzymes to display superactivity (Ventura
et al., 2017), which support the applicability of ionic liquids for the purpose under study.
A recent economic analysis assessment demonstrates that the application of ILs for the
extraction of compounds from biomass is only viable when the concentration of extracted
compounds is considerably higher (<5% dry wt.) or when they are truly value-added compounds
(Passos et al., 2014). In the case under study, the use of ILs seems to outweigh the requirements,
since, in addition to the concentration of extracted components ranges from 3 to 14% dry wt.,
such components are proteins with a high potential for downstream applications.
5.3.2 Extraction and fractioning of protein by IL based-ATPS
The economic and environmental feasibility of the use of ionic liquids to extract proteins as
a single product from seaweeds needs to be assessed. Cascade biorefinery approaches aiming
at the maximization of the inherent value of all the components present in the biomass would
certainly improve the overall economics of the biorefinery. An integrated biorefinery process
where the extracted proteins are the high-value components but also the carbohydrate fraction is
used, for instance, for the production of biofuels, chemicals or materials, might meet the
expectations for using seaweed as a viable feedstock for biorefineries. In this perspective, an
aqueous biphasic system composed by [Emim][dbp], K2HPO4 and water was studied for the
protein-carbohydrate separation in macroalga extracts. K2HPO4 was the inorganic salt selected
as salting-out agent because it is the most used in IL based-ABSs for protein extraction (Desai et
al., 2016).
The binodal curve obtained for the ATPS composed by [Emim][dbp] and K2HPO4 is shown
in Figure 5.7. Although different ATPS compositions could be selected, a low concentration of
ionic liquid (11% (w/w) [Emim][dbp]) and a high concentration of salt (24% (w/w) K2HPO4) were
used for the ATPS formation in the bead-mil experiment because the stock of [Emim][dbp]
available was lower than that of K2HPO4.
Figure 5.7 | Binodal curve of [Emim][dbp] and K2HPO4 at 298.15 K.
0
10
20
30
40
50
60
0 5 10 15 20 25 30
[Em
im] [d
bp]
% (
w/w
)
K2HPO4 % (w/w)
61
As described in Materials and Methods (section 4.4.3), the [Emim][dbp] based-ATPS
creation (Figure 5.8) was a single-step process, in which all the reagents were added at the same
time (alga, ionic liquid, water and salt) and the biomass was processed inside a milling equipment.
Figure 5.8 | ATPS ([Emim][dbp]/K2HPO4) created after biomass processing in bead mill.
In Figure 5.8 it is possible to distinguish two aqueous phases (top phase and bottom phase)
and a solid pellet and interphase. While both top and bottom phases were collected for protein
and carbohydrate analysis, the consistency of the interphase (solid gum) did not allow the
quantification of both biomolecules by the available methods. Therefore, taking the starting
amount of protein and carbohydrates as 100%, the extraction of both molecules in the interphase
was calculated subtracting the mass of protein quantified in the top and bottom phases. On the
other hand, the pellet was also not analysed because it was most probably precipitated salt.
Figure 5.9 | Distribution of extracted proteins and carbohydrates between the bottom, inter- and top phases
of the aqueous system composed by [Emim][dbp] and K2HPO4.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Carbohydrates Proteins
Bottom phase Interphase Top phase
62
In the several published studies in which IL based-ATPSs were applied as fractionating
strategy to separate proteins from polysaccharides, the proteins were preferentially concentrated
in the IL-rich phase, whereas the polysaccharides were accumulated into the bottom phase
(Ventura et al., 2017).
As displayed in Figure 5.9, although the distribution of proteins agrees with literature
(preferentially concentrated in the top phase), most of the carbohydrates, instead of being
concentrated in the bottom phase, were enriched into the interphase.
Like inorganic salts, polysaccharides are also kosmotropic (Wu et al., 2008) and, although
their presence is not sufficient to form an ATPS in macroalga extracts, they assist the K2HPO4 in
the phase separation. The interaction between water and saccharides are strong enough to enrich
the sugars in the bottom phase (Pei et al., 2010), however it has been shown that the extraction
efficiency of carbohydrates into the bottom phase decreases with increasing concentrations of
salt (Tan et al., 2012). This may be attributed to the salting-out effect, in which the increase in salt
concentration, decreases the solubility of carbohydrates due to the competition of salt molecules
for intermolecular hydrogen bonds (McKee and McKee, 1999), meaning less free water available
to dissolve saccharides. This phenomenon may explain the separation of carbohydrates into the
additional interphase, which should hold insoluble material with high water content, but not dense
enough to settle. Instead, the water activity of this material is such that it is trapped between the
water activities of the top and bottom phases (i.e. the water activity in the bottom phase was lower
because the water molecules were bound to the salt molecules – density of bound water is greater
than that of free water (Vaclavik and Christian, 2008)). The formation of the interphase turns the
ATPS into a three-partitioning phase (TPP) system and several TPP systems have already been
reported in literature for the extraction of carbohydrates into the interfacial layer (Coimbra et al.,
2010; Sharma and Gupta, 2002; Tan et al., 2015).
The ATPS negative control was used as blank in protein and carbohydrate quantification
to avoid interferences from the top and bottom phases, however, concerns about the
quantification of such molecules in the interphase arise from its unknown composition in terms of
ionic liquid and salt. Therefore, sophisticated analytical methods for the quantification of protein
and carbohydrates in the three-phases formed should be attempted to a more accurate and
reliable results. Moreover, replicates of the system should be performed to validate the
experiment.
It is also important to keep in mind that the stability of proteins in this specific ionic liquid is
a prime requirement to ensure the viability of the proposed process. Techniques such as infrared
spectroscopy, NMR spectroscopy, dynamic light scattering, and circular dichroism yield important
information on protein stability, which makes them suitable to evaluate the effect of the IL based-
ATPS in the extracted proteins.
Once the aforementioned issues are assessed, the optimization of separation conditions is
crucial to increase the extraction efficiency of protein in the top phase. Parameters such as the
concentration of phase-forming components, pH and temperature affect the partition coefficient
of protein in IL based-systems (Pei et al., 2010; Yan et al., 2014). Thus, a screening of the suitable
63
conditions for this particular separation process should be carried out. The biomass loading has
important repercussions for the throughput and economics of the process as there would be cost
saving if less of the solvent can be used and more biomass processed per batch. In this sense,
the maximum amount of biomass that can be processed without compromising the protein
extraction efficiency should be evaluated.
5.4 Ionic liquid recovery
As planned, the recovery of IL was further studied by ultrafiltration. The aim was to recover
the IL in the filtrate, while molecules, such as proteins and carbohydrates, would be rejected by
the membrane. Retentate and filtrate mass balances allowed the evaluation of each membrane
performance in terms of [Emim][dbp] recovery. The results showed that both membranes are
permeable to IL, allowing the recovery of 80-85% [Emim][dbp] (Figure 5.10).
Figure 5.10 | Recovery of [Emim][dbp] in filtrates and retentates, after ultrafiltration through stirred
ultrafiltration cell and centrifugal filter unit. Results are the means of duplicates ±SD.
On the other hand, the separation efficiency of proteins/carbohydrates from the IL solution
was analysed by the rejection coefficient. As it is shown in Table 5.7, the rejection coefficients for
both filtration setups were close to 1, meaning that the proteins and the carbohydrates were
effectively retained by the membranes.
Table 5.7 | Rejection coefficients of proteins and carbohydrates for each ultrafiltration setup. Results are
means of duplicates ±SD.
Ultrafiltration setup Protein rejection coefficient Carbohydrate rejection coefficient
Stirred ultrafiltration cell 0.91±0.04 0.89±0.01
Centrifugal filter unit 0.99±0.01 0.99±0.02
Based on these results, one should conclude that ultrafiltration can effectively separate
ionic liquids from other compounds in mixture, as far as the selection of the membrane considers
the size of the target molecule. Improved recoveries can be achieved by optimizing the operation
parameters (e.g. flow rate, concentration ratio, temperature) (Liu and Wu, 1998). Since the
0%
20%
40%
60%
80%
100%
Stirred ultrafiltration cell Centrifugal filtration unit
IL r
ecovery
Retentate Filtrate
64
efficiency of IL recovery by both ultrafiltration setups was not significantly different (p=0.36),
considering the possible scale-up of the process, stirred ultrafiltration cell setup might be more
suitable in terms of equipment requirements and time-consumption.
65
6 6. Conclusions and future
prospects
66
67
The commercialization of macroalgal proteins depends on the development of new
techniques for the extraction and separation of these biomolecules, under mild and stabilizing
conditions. The work developed in this thesis aimed at using ionic liquids to mediate the extraction
of macroalgal proteins. After screening twenty-five different ILs, [Emim][dbp] was selected as the
most suitable for the disintegration of U. lactuca cells and subsequent protein extraction. A protein
extraction efficiency of 80.62±4.95% was obtained which is significantly higher than that obtained
by reference cases: extraction by high-shear force under alkaline conditions, ATPS
(PEG/Na2CO3) and sequential aqueous and alkaline extraction, with protein recovery yields of
6.68±1.09%, 10.52±0.35% and 49.08±6.66%, respectively.
Aiming at an integrated biorefinery approach and the valorisation of the different algal
components, a strategy was proposed that involved the single-step processing of algae under
mechanical grinding and in the presence of an aqueous solution of [Emim][dbp] and a certain
amount of K2HPO4 capable to induce phase formation. A TPP system was created, allowing the
selective separation of proteins and polysaccharides, which were mainly concentrated in the top
phase (IL-rich phase) and interfacial layer, respectively. Despite this, only ~50% of the extracted
proteins were enriched into the top phase, meaning that, although [Emim][dbp] extracts ~80% of
the total protein content of U. lactuca, the addition of an aqueous solution of K2HPO4 permitted
the recovery of only ~40% of the total proteins. Therefore, in order to improve the performance of
the system under study, it is of utmost importance to increase the extraction efficiency of the
proteins into the top phase by optimizing the conditions used for the TPP system formation.
For an economically sustainable process, it is crucial that the IL can be easily separated
from the product and recycled for further use. Ultrafiltration showed great potential for the recovery
of ionic liquids from macroalgal extracts, with IL recovery yields of almost 80% in the filtrate while
allowing the concentration of proteins and carbohydrates in the retentate. In this sense, one
should conclude that, if ultrafiltration would be applied only to the top phase of the proposed TPP
system (rich in IL and proteins), it should lead to the isolation of the desired proteins while making
the IL available for reuse.
The results attained with the TPP system for cell disintegration and protein/carbohydrate
fractionation, along with ultrafiltration for IL recovery, are encouraging for a large-scale
application. However, several items must be addressed in addition to the experimental issues
discussed in section 5.
The purity levels required for biocompounds or refined extracts in cosmetic, food and
mainly pharmaceutical and medical sectors are high. This means that the requirements of industry
and markets are crucial when attempting the design and development of separation and
purification processes based on ILs. The scarce information on the stability and bioactivity of ILs
limits their application in such target industries, so further studies in this area are needed.
The development of reliable models that predict the partitioning behaviour of proteins or
polysaccharides in TPP systems would be a major breakthrough for the rational design of these
separation processes and would help the establishment of TPP systems as a standard and large-
68
scale unit operation. In addition, special attention should also be paid to the environmental
concerns that could arise from phase-forming component waste streams.
Although ILs have been claimed as “green” solvents due to their negligible volatility, there
are several studies reporting their negative impact in water ecosystems (Frade and Afonso, 2010).
Therefore, a detailed life-cycle assessment on [Emim][dbp] is necessary in order to claim its
sustainability from a “green” point of view. However, the integration of an additional ultrafiltration
unit operation is a promising option to recovering and recycling [Emim][dbp], minimizing the
environmental concerns of its disposal. Moreover, the high salt concentrations used in TPP
systems can also create waste disposal concerns, for instance due to enhancing eutrophication
phenomenon in water (Ratanapongleka, 2010). This limitation may be overcome by recycling the
salts, using biodegradable salts (e.g. sodium citrate) or gentler salting-out species, such as amino
acids, carbohydrates and polymers (Freire et al., 2012; Raja et al., 2012). Nevertheless, the ability
of these alternative components to induce the phase-formation in [Emim][dbp]-based ATPSs
needs to be evaluated because they suffer the drawback on only being able to form ATPS with a
limited number of ILs due to their low salting-out ability (carbohydrates, amino acids, and polymers
versus salts) (Desai et al., 2016).
Finally, it is of the utmost importance to ascertain the economic viability of the proposed
biorefinery approach, which was one of the objectives of this research project. However, the
obtained results did not allow the accurate establishment of the process mass and energy
balances. Due to the impossibility of protein quantification in the interphase, the non-optimized
partition conditions in the ATPS, the lack of replicates and the unknown amount of biomass that
can be processed at the same time, a proper economic appraisal of the project was deemed not
feasible.
Upon the optimization of the proposed biorefinery strategy and with reliable data available
for the establishment of mass and energy balances, it will be possible to estimate the costs of raw
materials (biomass, [Emim][dbp] and K2HPO4), as well as utilities (water supply, energy
consumption and nitrogen gas/compressed air). Concerning the required equipment (bead mill,
settler, filtration unit), it should be properly scaled up in order to serve the needs of the bioprocess
maximizing its performance while also managing to do so in the most cost-effective manner.
Once the process equipment and utility requirements are determined, the costs associated
towards the implementation of the projected plant design should be defined. In this sense, one
must look at the benefits and impacts of the process through provisional demonstrations of
expected costs and monetary yield. The economic outlook should be based on parameters such
as capital investment, estimate production costs, provisional balances and ultimately, profitability
analysis. This study should determine the success or downfall of the downstream process under
development.
As final remarks, although not all the objectives of the study proposed in the beginning of
this thesis were fully met, the outcome of the present work presents important insights toward the
valorisation of seaweeds as feedstocks for integrated biorefineries. [Emim][dbp] seems to play a
69
remarkable role in the extraction of macroalgal proteins, which widespread use is envisaged in
the near future to boost the quality of modern society.
70
71
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A-1
A Appendix A Protein,
carbohydrate and ionic liquid quantification
A-2
A-3
A.1. Protein Quantification
The calibration curves used in protein quantification were prepared with BSA for a working
range of 0–1.4 mg.mL-1 for Lowry’s method and 0–2 mg.mL-1 for Bradford assay. Equation A. 1
to Equation A. 8 describe the calibration curves, and respective correlation factors, used in each
experiment for protein determination.
Total protein content
Lowry’s method
Abs750𝑛𝑚 = 0.20[𝑃𝑟𝑜𝑡𝑒𝑖𝑛]𝑚𝑔. 𝑚𝐿−1 − 1.47 × 10−2 (𝑟2 = 0.9938)
Equation A. 1
Bradford assay
[𝑃𝑟𝑜𝑡𝑒𝑖𝑛]𝑚𝑔. 𝑚𝐿−1 = 1.50𝐴𝑏𝑠595𝑛𝑚2 + 0.61𝐴𝑏𝑠595𝑛𝑚 + 0.04 (𝑟2 = 0.9870)
Equation A. 2
Aqueous and alkaline extraction
Bradford assay
[𝑃𝑟𝑜𝑡𝑒𝑖𝑛]𝑚𝑔. 𝑚𝐿−1 = 1.83𝐴𝑏𝑠595𝑛𝑚2 − 1.05𝐴𝑏𝑠595𝑛𝑚 + 0.16 (𝑟2 = 0.9898)
Equation A. 3
Extraction by high-shear force
Bradford assay
[𝑃𝑟𝑜𝑡𝑒𝑖𝑛]𝑚𝑔. 𝑚𝐿−1 = 1.83𝐴𝑏𝑠595𝑛𝑚2 − 1.05𝐴𝑏𝑠595𝑛𝑚 + 0.16 (𝑟2 = 0.9898)
Equation A. 4
Aqueous biphasic system PEG/Na2CO3
Lowry’s method
Abs750𝑛𝑚 = 0.17[𝑃𝑟𝑜𝑡𝑒𝑖𝑛]𝑚𝑔. 𝑚𝐿−1 + 0.14 (𝑟2 = 0.9831)
Equation A. 5
Ionic liquid selection
Bradford assay
[𝑃𝑟𝑜𝑡𝑒𝑖𝑛]𝑚𝑔. 𝑚𝐿−1 = 1.50𝐴𝑏𝑠595𝑛𝑚2 + 0.61𝐴𝑏𝑠595𝑛𝑚 + 0.04 (𝑟2 = 0.9870)
Equation A. 6
Aqueous three-phase partitioning system [Emim][dbp]/K2HPO4
Bradford assay
[𝑃𝑟𝑜𝑡𝑒𝑖𝑛]𝑚𝑔. 𝑚𝐿−1 = −0.20𝐴𝑏𝑠595𝑛𝑚2 + 0.84𝐴𝑏𝑠595𝑛𝑚 + 0.02 (𝑟2 = 0.9966)
Equation A. 7
A-4
Ultrafiltration
Bradford assay
[𝑃𝑟𝑜𝑡𝑒𝑖𝑛]𝑚𝑔. 𝑚𝐿−1 = 1.82 𝐴𝑏𝑠595𝑛𝑚2 − 0.39𝐴𝑏𝑠595𝑛𝑚 + 0.04 (𝑟2 = 0.9873)
Equation A. 8
A.2. Carbohydrates Quantification
The calibration curves used in carbohydrate quantification were prepared with glucose for
a working range of 0–0.1 mg.mL-1. Equation A. 9 to Equation A. 12 describe the calibration curves,
and respective correlation factors, used in each experiment for carbohydrate determination.
Total carbohydrate content
Abs483𝑛𝑚 = 4.37[𝐶𝑎𝑟𝑏𝑜ℎ𝑦𝑑𝑟𝑎𝑡𝑒]𝑚𝑔. 𝑚𝐿−1 + 2.48 × 10−3 (𝑟2 = 0.9703)
Equation A. 9
Aqueous biphasic system PEG/Na2CO3
Abs483𝑛𝑚 = 4.37[𝐶𝑎𝑟𝑏𝑜ℎ𝑦𝑑𝑟𝑎𝑡𝑒]𝑚𝑔. 𝑚𝐿−1 + 6.33 × 10−2 (𝑟2 = 0.9703)
Equation A. 10
Aqueous three-phase partitioning system [Emim][dbp]/K2HPO4
Abs483𝑛𝑚 = 7.02[𝐶𝑎𝑟𝑏𝑜ℎ𝑦𝑑𝑟𝑎𝑡𝑒]𝑚𝑔. 𝑚𝐿−1 + 7.21 × 10−2 (𝑟2 = 0.9981)
Equation A. 11
Ultrafiltration
Abs483𝑛𝑚 = 6.32[𝐶𝑎𝑟𝑏𝑜ℎ𝑦𝑑𝑟𝑎𝑡𝑒]𝑚𝑔. 𝑚𝐿−1 + 6.38 × 10−3 (𝑟2 = 0.9985)
Equation A. 12
A.3. Ionic liquid Quantification
The calibration curve and respective correlation factor used for ionic liquid quantification,
in ultrafiltration experiment, was prepared with [Emim][dbp] for a working range of 100–400
mg.mL-1 (Equation A. 13).
𝐴𝑟𝑒𝑎𝑝𝑒𝑎𝑘 = 3.54 × 102[𝐼𝐿]𝑚𝑔. 𝑚𝐿−1 − 1.56 × 103 (𝑟2 = 0.9977)
Equation A. 13
B-1
B Appendix B Binodal Curve
B-2
B-3
B.1. Binodal curve correlation
The binodal experimental data obtained for the ATPS composed by [Emim][dbp] an
K2HPO4 and respective correlation factor are described in Equation B. 1. The r-squared statistical
measure is quite far from 1, suggesting that the model does not fit the experimental data so as
one should not use it for the determination of component concentrations. Due to time concerns
the experimental procedure was not repeated and the concentrations of K2HPO4 and IL used for
the ATPS formation were estimated by visual observation of the Figure 5.7.
[𝐸𝑚𝑖𝑚][𝑑𝑏𝑝] = 420.25 × exp(−1.32 × [𝐾2𝐻𝑃𝑂4]3) (𝑟2 = 0.7964)
Equation B. 1