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1
Microwave-Assisted Extraction (MAE) of
Secoisolariciresinol Diglucoside (SDG) from Flaxseed
Nemes Simona Mihaela
Bioresource Engineering Department, McGill University, Montreal
A thesis submitted to McGill University in partial fulfilment of the requirements
of the degree of M.Sc.
© Nemes Simona Mihaela, 2007
II
ABSTRACT
Nemes Simona Mihaela – MSc Bioresource Engineering
Microwave-Assisted Extraction (MAE) of Secoisolariciresinol
Diglucoside (SDG) from Flaxseed
Secoisolariciresinol diglucoside (SDG) is the main lignan from flaxseed,
and it is the main exogenous precursor of the mammalian lignans enterolactone
and enterodiol. SDG is a recognised nutraceutical which presents several health
benefits; it has strong antioxidant activity, cardioprotective effects, reduces the
blood levels of low-density lipoprotein cholesterol, and reduces the risk of
hormone related cancer. SDG is present in flaxseed in a complex polymeric form,
in which it is ester linked to 3-hydroxy-3-methyl-glutaryl (HMG). SDG can be
released from the polymeric structure by alkaline hydrolysis of the ester bonds.
A new analytical method was developed for fast and reliable
quantification of SDG in flaxseed. The method used microwave energy to enhance
the extraction diffusion processes and to release the SDG from the flaxseed
matrix. Statistical screening designs were used to determine the experimental
domain, and to identify the factors that governed the MAE of SDG from flaxseed.
The MAE method was optimised by use of a response surface methodology study.
The results showed that the extraction yield of SDG was maximised for the
following combinations: 1 mg defatted flaxseed hydrolysed with 50 ml NaOH
0.5M, for 3 min, at 135 W, with the microwave power applied intermittently (30 s
on/off). The efficiency of the optimised MAE method was evaluated by comparing
it with a conventional hydrolysis method and a microwave-less control method.
The MAE method achieved the highest extraction yield, 21.45 mg SDG/g defatted
meal, as opposed to 20.22 and 19.45 mg SDG/g defatted meal obtained with the
conventional hydrolysis and microwave-less control methods respectively. The
optimised MAE method is recommended for fast and reliable preparation of
flaxseed lignan extracts for chromatography.
III
RÉSUMÉ
Nemes Simona Mihaela – MSc Génie des Bioressources
Extraction Micro-onde du Secoisolariciresinol Diglucoside (SDG)
de la graine de lin
Le secoisolariciresinol diglucoside (SDG) est le lignane le plus important
provenant de la graine de lin, et il est le plus important précuseur exogène des
lignanes mammifères entérolactone et entérodiol. Le SDG est un ingrédient
alimentaire fonctionnel reconnu pouvant offrir plusieurs bénéfices santé; il
possède une importante activité antioxydante, des effets cardio-protecteurs, il
peut réduire le niveau sanguin de cholestérol à lipoprotéines à basse densité, et
réduire le risque de cancers hormonaux. Le SDG est présent dans la graine de lin
sous une forme polymérique complexe qui lie son ester au 3-hydroxy-3-methyl-
glutaryl (HMG). Le SDG peut être libéré de la structure polymérique par une
hydrolyse alkaline de ses liens estériques.
Une nouvelle méthode d’analyse a été développée pour la quantification
rapide et fiable du SDG de la graine de lin. La méthode utilise l’énergie micro-
onde afin de favoriser le processus de diffusion lors de l’extraction du SDG de la
graine de lin. Un plan de criblage statistique a été utilisé pour déterminer le
domaine expérimental et pour identifier les facteurs qui gouvernent l’extraction
micro-onde du SDG de la graine de lin. Le processus d’extraction micro-onde a
été optimisé par l’entremise de la méthodologie de surface de réponse. Les
résultats indiquent que le rendement de l’extraction de SDG est optimal pour les
conditions suivantes : 1 mg de graines de lin dégraissées, hydrolysées avec 50 ml
NaOH 0.5M, pour 3 min, et sujet à 135 W de micro-onde intermittent (30 s
marche/arrêt). L’efficacité de l’extraction micro-onde optimisée a été comparée
avec la méthode traditionnelle d’hydrolyse et l’extraction témoin sans micro-
onde. L’extraction micro-onde a réalisé le meilleur rendement avec 21.45 mg
SDG/g de lin dégraissé, comparé à 20.22 et 19.45 mg SDG/g lin dégraissé
IV
obtenus par hydrolyse conventionnelle et extraction témoin sans micro-onde
respectivement. La méthode optimisée d’extraction micro-onde est recommandée
pour la préparation rapide et fiable d’extraits de lignanes de lin pour analyse
chromatographique.
V
ACKNOWLEDGEMENTS
I would like to express my deepest gratitude to my supervisor, Dr. G. S.
Vijaya Raghavan, James McGill Professor, Bioresource Engineering Department,
McGill University, for he is always there for me; teaching me, guiding me,
helping me, encouraging me, inspiring me, and believing in me. Thank you!
I am deeply grateful to Dr. Valérie Orsat for she gave me kindness, moral
support, and invaluable scientific advice during my studies. I would also like to
thank Dr. Orsat for helping me with the translation of the abstract in French.
Many heartfelt thanks to Dr. Robert Kok for providing our laboratory
with an analytical instrument most necessary for my experimental work.
I would like to thank Mr. Yvan Garièpy, for helping me and guiding me
during the experimental projects in the post-harvest field, and for advice and
support during the experimental work for my thesis.
I would like to express my deepest appreciation to Ms. Susan Gregus, Ms.
Trish Singleton, Ms. Abida Subhan, Mr. Scott Manktelow, Dr. Sam Sotocinal, and
Dr. Venkatesh Sosle for their kindness and support. I am also grateful to Dr.
Sotocinal and Dr. Sosle for helping with reparations and providing materials for
the lab.
Many thanks to my friends Sivakumar, Liette, Arun, Satya, Aynur, Viboon,
Jagadeesh, Rajkumar, Tanya, Akinbode, Laura, Mariana, Vidisha, Shadi, Samira,
Yaqiong, Jianbo, Susan, and Charlotte, for making me feel welcome in the
Bioresource Engineering “family”.
Above all, I would like to thank my husband Marius Talos, for his
understanding, encouragements and support.
Thank you!
VI
TABLE OF CONTENTS
CHAPTER I.................................................................................... 1 1. INTRODUCTION............................................................................ 1 1.1. Hypotheses....................................................................................... 2 1.2. Objectives......................................................................................... 3 CHAPTER II.................................................................................. 5 2. LITERATURE REVIEW................................................................ 5 2.1. Linum usitatissimum – General Introduction.................................. 5 2.1.1. The role of flaxseed in the food industry......................................... 5 2.1.2. Chemical composition of flaxseed................................................... 6 2.1.2.1. Flaxseed lignans............................................................................... 7 2.1.3. Methods of extraction of SDG from flaxseed.................................. 13 2.1.3.1. Conventional extraction methods..................................................... 13 2.1.3.2. Microwave-assisted extraction (MAE)............................................ 16 2.1.4. Claimed health benefits of SDG...................................................... 19 2.1.4.1. Cancer related studied...................................................................... 20 2.1.4.2. Antioxidant properties of SDG........................................................ 21 2.1.5. Flaxseed lignans metabolism........................................................... 24 2.1.6. Conclusions...................................................................................... 25 2.2. Microwave radiation – General theory............................................ 28 2.2.1. Microwave application regulations.................................................. 30 2.2.2. Microwave technology..................................................................... 31 2.2.2.1. Microwave ovens............................................................................. 31 2.2.2.2. The magnetron................................................................................. 31 2.2.2.3. The waveguide................................................................................. 33 2.2.2.4. The resonant cavity.......................................................................... 33 2.2.2.5. Microwave systems for chemistry................................................... 34 2.2.3. Microwave heating attributes........................................................... 35 2.2.4. Microwave heating theory................................................................ 36 2.2.4.1. Microwave heating – a form of dielectric heating........................... 37
2.2.4.2. Microwave heating explained by analogy with dielectric heating in a condenser...................................................................................
38
VII
2.2.4.3. Microwave interaction with dielectric materials.............................. 43 2.2.4.3.a. Mechanism of interaction................................................................. 46 2.2.5. Microwave effects............................................................................ 48 2.2.6. Microwave-assisted extraction (MAE)............................................ 48 2.2.6.1. MAE of compounds from plant matrices......................................... 49 2.2.6.1.a. Pioneering work in MAE................................................................. 50
2.2.6.1.b. MAE of natural compounds using dedicated laboratory microwave systems..........................................................................
51
2.2.6.1.b.a. MAE using open-vessel microwave systems................................... 52 2.2.6.1.b.b. MAE using closed-vessel microwave systems................................ 55 2.2.6.2. MAE of natural compounds – General observations....................... 57 2.2.7. Conclusions...................................................................................... 58 CHAPTER III................................................................................ 61 3. THEORETICAL ASPECTS OF STATISTICAL DESIGN............ 61 3.1. The design of experiment................................................................. 61 3.2. Two-level factorial and fractional factorial designs......................... 61 3.2.1. Unreplicated two-level factorial designs.......................................... 61 3.2.2. Two-level unreplicated fractional factorial designs......................... 64 3.3. Response surface methodology........................................................ 67 3.4. Conclusions...................................................................................... 69 CHAPTER IV................................................................................ 70 4. MATERIALS AND METHODS..................................................... 70 4.1. Flaxseed............................................................................................ 70 4.2. Operating procedure......................................................................... 70 4.3. Microwave-assisted extraction (MAE) of SDG............................... 71 4.3.1. MAE preliminary studies................................................................. 72 4.3.1.1. Determination of solvent to sample ratio......................................... 72 4.3.1.2. Determination of the experimental domain..................................... 73 4.3.2. MAE experiments within the established experimental domain..... 77
4.3.3. Evaluation of the MAE method as opposed to control methods............................................................................................
78
4.4. HPLC analysis.................................................................................. 78
VIII
CHAPTER V.................................................................................. 81 5. RESULTS AND DISCUSSION...................................................... 81
5.1. Screening of the factors within the established experimental domain..............................................................................................
81
5.2. Temperature profiles for the screening experiment......................... 94 5.3. Response surface study.................................................................... 97 5.4. Temperature profiles for the axial and center points........................ 1045.5. Evaluation of the efficiency of the optimised MAE........................ 1055.6. Temperature profiles for the replicated MAE.................................. 1105.7. Conclusions...................................................................................... 111 CAPTER VI.................................................................................... 1146. SUMMARY AND GENERAL CONCLUSIONS........................... 1147. REFERENCES................................................................................. 117
IX
LIST OF FIGURES
Figure 2.1. Chemical structure of secoisolariciresinol diglucoside enantiomers, (+) SDG, (-) SDG, and the mammalian lignans enterodiol (ED) and enterolactone (EL)................................ 8
Figure 2.2. Biochemical pathways leading to the formation of (-) secoisolariciresinol (A), and (+) secoisolariciresinol (B) ............ 9
Figure 2.3. Biosynthetic pathway to the formation of the SDG containing polymers....................................................................................... 10
Figure 2.4. Polymer biosynthesised through the ester-linkage of HMG-SDG mono- and di-mers with 3-hydroxy-3-methyl-glutaryl (HMG).......................................................................................... 11
Figure 2.5. Electromagnetic spectrum............................................................ 28
Figure 2.6. The wave of electromagnetic radiation........................................ 30
Figure 2.7. Schematic representation of a magnetron and the motion of electrons inside............................................................................. 32
Figure 2.8. Inverted temperature (degrees Kelvin) gradients in microwave (left) versus oil-bath heating (right)............................................. 36
Figure 2.9. Current and voltage in a condenser with capacitance C0............. 39
Figure 2.10. Condenser with a dielectric load placed between two electrode plates............................................................................................. 40
Figure 2.11. Charging current and loss current in a condenser containing a dielectric material......................................................................... 41
Figure 2.12. The charging current density and the loss current density in a condenser containing a dielectric................................................. 43
Figure 3.13. Three-factor combination points represented at the corners of a cube............................................................................................. 63
Figure 3.14. Central composite design with two factor variables..................... 68
Figure 4.15. Schematic representation of the focused MAE system................ 72
Figure 4.16. Main effects and interactions plot for the 1st screening study...... 75
Figure 4.17. Main effects and interactions plot for the 2st screening study...... 76
Figure 4.18. Chromatogram of SDG standard.................................................. 80
X
Figure 4.19. Typical chromatogram of flaxseed extract................................... 80
Figure 5.20. Pareto plot..................................................................................... 83
Figure 5.21. Half-normal plot........................................................................... 83
Figure 5.22. Main effects and interactions plot................................................ 84
Figure 5.23. Prediction profiler........................................................................ 87
Figure 5.24. Response surface plot for the screening design............................ 88
Figure 5.25. The Box Cox optimal transformation of the response.................. 91
Figure 5.26 Main effects and interaction plot of the full factorial design....... 91
Figure 5.27. The response surface for the full factorial design........................ 92
Figure 5.28. Temperature profiles for the Screening design............................ 94
Figure 5.29. Maximum temperatures for the screening design........................ 95
Figure 5.30. The Box Cox optimal power transformation of the response...... 98
Figure 5.31. The prediction profiler.................................................................. 99
Figure 5.32. The response surface plot for the CCD........................................ 101
Figure 5.33. Temperature profiles for the axial and center points.................... 104
Figure 5.34. Maximum temperatures for the axial and center points............... 105
Figure 5.35. Temperature profiles for the replicated MAE.............................. 111
Figure 5.36. The maximum temperature for the replicated MAE.................... 111
XI
LIST OF TABLES
Table 2.1. Antioxidant capacity of flax lignans.................................. 22
Table 2.2. Concentration–dependent inhibition of PMNL chemoluminescence............................................................ 22
Table 2.3. Microwave photon, Brownian motion and chemical bonds energies.................................................................... 30
Table 3.4. The 23 full factorial design................................................. 62
Table 3.5. The 2 half fraction factorial design of resolution IV.. 66
Table 4.7. ANOVA for the 1st screening study................................... 75
Table 4.8. ANOVA for the 2st screening study................................... 77
Table 5.9. Aliasing structure of two-factor interactions...................... 81
Table 5.10. The factors and the response investigated in the screening design.................................................................................. 82
Table 5.11. The screening design layout............................................... 82
Table 5.12. The effect of the incorrect decision on ANOVA............... 85
Table 5.13. The effect of the incorrect decision on the fit statistics...... 85
Table 5.14. The effect of the correct decision on ANOVA................... 86
Table 5.15. The effect of the correct decision on the fit statistics......... 86
Table 5.16. Predictive model for the screening design......................... 87
Table 5.17. Numerical optimisation results for the screening design... 88
Table 5.18. The effect of discarding the factor Time on the ANOVA.. 89
Table 5.19. The effect of discarding the factor Time on the fits statistics.............................................................................. 90
Table 5.20. The ANOVA for the full factorial design.......................... 92
Table 5.21. The fit statistics for the full factorial design....................... 93
Table 5.22. The response surface model and the effects estimates for the full factorial design....................................................... 93
Table 5.23. Numerical optimisation results for the full factorial design................................................................................. 94
XII
Table 5.23. Screening design layout...................................................... 96
Table 5.24. Central composite design layout........................................ 98
Table 5.25. The ANOVA for the central composite design.................. 100
Table 5.26. The fit statistics for the response model............................. 100
Table 5.27. The master model and the RS predictive model................. 101
Table 5.28 The estimates of the regression terms................................ 102
Table 5.29. Canonical analysis – Stationary point for SDG.................. 102
Table 5.30. Canonical analysis – Critical values at the stationary point.................................................................................... 102
Table 5.31. Canonical analysis – Eigen values and Eigen vectors........ 103
Table 5.32. The ridge analysis............................................................... 103
Table 5.33. Numerical optimisation results........................................... 104
Table 5.34. The analysis of variance..................................................... 107
Table 5.35. The multiple comparisons Tukey’s HSD test..................... 108
Table 5.36. The means and descriptive statistics of the three extraction methods............................................................. 110
XIII
NOMENCLATURE
AC Alternative current CL Chemoluminescence co Speed of light (2.997925 ×10-8 m/s) Co Capacitance of air C Capacitance of the condenser containing a dielectric material CoA Coenzyme A Dp Penetration depth (m) DFM Defatted flaxseed meal E Energy of the microwave photon (J) E Electric field strength inside the load (V/m) ED Enterodiol EL Enterolactone G Conductance of the dielectric material h Plank’s constant (6.66256×10-34 Js) HDL High density lipoprotein HMG 3-hydroxy-3-methyl glutaryl HPLC High performance liquid chromatography I Total current Ic Charging current Il Loss current IR Infra red ISM Industrial scientific and medical microwave frequencies J Current density across the condenser LDL Low density lipoprotein M Molar MAE Microwave-assisted extraction mW Milliwatt nM Nanomole/litre PFC Pressed flaxseed cake PMNL Polymorphonuclear leucocytes Pr Reflected energy Pv Power absorbed per unit volume (W/m3)
XIV
RF Radio frequency SDG Secoisolariciresinol diglucoside SECO Secoisolariciresinol Tanδ Loss tangent U Voltage UDP Uridine diphosphate δ Loss angle ε0 Permittivity of free space ε* Complex permittivity ε’ Dielectric constant ε” Dielectric loss factor θ Power factor angle λ Microwave wavelength (m) λ0 Microwave wavelength in free space (m) µ* Complex permeability µM Micromole/litre ν Frequency (Hz) σ Dielectric conductivity (S) τ Relaxation time (s) ω Angular frequency (rad/s)
1
CHAPTER I
1. INTRODUCTION
Flax, Linum usitatissimum, is in production mainly for fibre and oil. In
recent times, flaxseed has attracted a lot of attention from the scientific
community, due to its favourable chemical composition (Westcott and Muir,
2003a). Flaxseed appears to be a key raw material in the nutraceuticals and
functional foods industry, as it is an important source of omega-3 fatty acids,
soluble fibre (mucilage) and lignans (Oomah, 2001). In fact, flaxseed is the richest
food source of the lignan secoisolariciresinol diglucoside (SDG), which is the
main exogenous precursor of the mammalian lignans, enterodiol and
enterolactone. SDG is a recognised nutraceutical; it has been shown to reduce the
levels of LDL-cholesterol (the bad cholesterol) in blood, the risk of diabetes, and
hormone related cancer; it has antioxidant activity, cardioprotective effect, and
improves renal function in lupus nephritis patients (Oomah, 2001; Prasad, 2000b;
Westcott and Muir, 2003a).
SDG is present in flaxseed in the form of complex polymeric structures, in
which is covalently bound via ester linkages to 3-hydroxy-3-methyl glutaryl
(HMG). SDG can be released from these structures by hydrolytic cleavage of the
ester bonds (Davin and Lewis, 2003, 2005; Ford et al., 2001). The traditional
SDG extraction methods usually involve aqueous alcoholic extraction, followed
by alkaline hydrolysis. These methods are time consuming; they can take
anywhere from few hours to 2-3 days. Moreover, they involve extensive sample
manipulations.
Recently there has been an increasing demand for new extraction
techniques that are environmentally friendly, faster, and more efficient than the
traditional extraction methods. Microwave-assisted extraction (MAE) has
emerged as an efficient method for the extraction of nutraceuticals (Kaufmann
2
and Christen, 2002; Wang and Weller, 2006). MAE has been shown to prevent
the denaturation of thermolabile compounds. It is therefore recommended for fast
and reliable sample preparation for chromatography (Ganzler and Salgo, 1987;
Ganzler et al., 1986). Microwave heating, therefore, represents an alternative to
the conventional heating, for transferring thermal energy to the reaction media.
Microwave energy is non-ionising; the energy of the microwave photon is
not high enough to induce constructive or destructive reaction steps (Kappe,
2004). The physical principle behind MAE processes is based on the dielectric
properties of samples and solvents, and their interaction with microwave radiation
(Liao et al., 2003a; Liao et al., 2001a; Liao et al., 2003b; Liao et al., 2001b; Liao
et al., 2002) .
Microwave heating is volumetric in nature. The energy transfer occurs
from the direct coupling of the electromagnetic field with the polar and ionic
components in the materials. Generally, the microwave interactions with materials
are governed by the complex permittivity and complex permeability of the
materials. However, foods contain only trace amount of magnetic components
therefore, they do not interact with the magnetic field. Microwave heating occurs
mainly by two effects, dipolar polarization and ionic conduction (Venkatesh and
Raghavan, 2004).
1.1. Hypotheses
Many studies showed that by applying microwave power it is possible to
reduce the extraction time of natural compounds from plant matrices
tremendously. Moreover, it helps achieve extraction yields higher or at least
comparable with those achieved by using conventional extraction methods (Alfaro
et al., 2003; Brachet et al., 2002; Dai et al., 2001; Ganzler and Salgo, 1987;
Ganzler et al., 1986; Ganzler et al., 1990; Williams et al., 2004). It is therefore
hypothesised that it is possible to reduce the extraction time and maximise the
extraction yield of SDG from flaxseed by using microwave-assisted extraction.
3
If NaOH is used for the MAE extraction of SDG, the mixture of flaxseed
and NaOH will heat under microwave irradiation by ionic conduction and dipolar
polarization. The ions in the NaOH solution are free to move. They will oscillate
under the influence of the alternating electric component of the microwave field
and will collide with other species, therefore causing heating. Flaxseed lignans are
polar molecules; they have the capacity to polarize under the influence of the
electric field. Lignans are mainly found in the seed coat of flaxseed, where they
are ester-linked to HMG residues, and possibly bound to other compounds,
therefore they are not free to move. This will cause their polarization to lag behind
the applied electric field, which in turn will result in heating by dielectric loss. It
seems therefore reasonable to assume that the extraction kinetics will be
accelerated, and the diffusion rates of NaOH into the flaxseed meal and lignans
out of the matrix will be increased as well. It is therefore hypothesised that the
extraction yield of SDG can be maximised by using MAE.
1.2. Objectives
The main objective of this study is to develop an optimised MAE method
for the extraction of SDG from flaxseed. The following sub-objectives are
involved:
1. To investigate the effect of four factors (Power = 60-360 W, Time = 3-9
min, Power mode = 30-60 s power on/min, and Molarity= 0.5-1 M NaOH)
on the MAE of SDG from flaxseed by using a screening design. The scope
of the study is to screen out the factor variables that do not have a
significant influence on the extraction yield of SDG.
2. To maximise the extraction yield of SDG using response surface
methodology, by augmenting the screening design to a central composite
design. The scope of the study is to optimise the combination of factors in
such a way that the extraction yield of SDG will be maximised.
3. To evaluate the efficiency of the optimised MAE method by comparing it
with a conventional direct hydrolysis method and a microwave-less
4
control method. The microwave-less control method is identical with the
MAE method, with the exception that no microwave power is supplied.
5
CHAPTER II
2. LITERATURE REVIEW
2.1 Linum usitatissimum – General introduction
The common flax cultivars traditionally grown for oil and fibre are part of
the genus Linum, species Linum usitatissimum (Westcott and Muir, 2003a). Flax
is mainly cultivated in the cool climate of western Canadian prairies in Manitoba,
Saskatchewan, and Alberta. Canada is the world leader in the production and
export of flaxseed. Currently, ninety percent of the exported Canadian flax goes to
the E.U. (60%) and U.S. (30%) markets. In 2005, Canada produced 1,082
thousand tonnes of flaxseed (Anonymous, 2007a).
2.1.1. The role of flaxseed in the food industry
Historically, the role of food has been upgraded from hunger satisfaction,
to food security and to functionality at present. Nowadays the food industry is
challenged to produce food products that are convenient, fresh, and offer health
benefits in addition to their nutritional qualities. The trend in food development is
to produce foods that promote a state of health and well-being and have the
potential to reduce the risk of diseases. According to Health Canada, “a functional
food is similar in appearance to, or may be, a conventional food, is consumed as
part of a usual diet, and is demonstrated to have physiological benefit and/or to
reduce the risk of chronic disease beyond basic nutritional functions
(Anonymous, 1998). Such foods can be obtained by concentration, addition, or
removal of certain components (Roberfroid, 1999).
Flaxseed contains Ω-3 fatty acids, lignans, gums (soluble fibres) and high-
quality proteins, all of which make flaxseed a valuable ingredient for functional
food formulations. In addition, some of the claimed health benefits of flaxseed
lignans include reduction of low-density lipoprotein (LDL), bad cholesterol,
6
reduction of the risk of hormone related cancers, cardioprotective effect, improved
renal function in lupus nephritis patients (Oomah, 2001), antioxidant activity
(Prasad, 2000a), and reduction of the risk of diabetes type I and II (Prasad,
2000b). Thus, as per the definition of functional foods, a food that contains whole
flaxseed, flaxseed ingredients (oil, gum), or flaxseed extracts (lignans, Ω-3 fatty
acids) is a functional food. Due to their health benefits, flaxseed lignans could be
used as dietary supplements or nutraceuticals. According to Health Canada, “a
nutraceutical is a product isolated from foods that is generally sold in medicinal
forms not usually associated with foods. A nutraceutical is demonstrated to have a
physiological benefit or provide protection against chronic disease”
(Anonymous, 1998).
The benefits of developing functional foods can be double-fold. For
example, flaxseed lignans have antioxidant activity, and their capacity to
scavenge free radicals has been demonstrated (Kitts et al., 1999; Prasad, 2000a).
Andersen et al., (2003) suggested the use of plant compounds as natural
antioxidants to replace synthetic ones. Since oxidation plays an important role in
the loss of food quality, the use of plant antioxidants would, from one standpoint,
scavenge the free radicals and prevent the peroxidation of lipids, and from another
standpoint, confer to the food product the aspect of functionality through potential
health benefits. The development of such food products with an optimised content
of plant antioxidants requires the development of specific methods for extraction
and qualitative and quantitative analysis of plant antioxidants (Andersen et al.,
2003).
2.1.2. Chemical composition of flaxseed
In 2006, Canadian flax contained on average, on a dry weight basis, 23.6%
protein and 45.9% oil, 55.8% of which represented alpha-linolenic acid (ALA or
omega-3 fatty acid) (DeClercq, 2006). Flaxseed also contains an important
amount of polysaccharidic mucilage which is a soluble fibre (6-8% on a dry
weight basis), and phytochemicals. The most important phytochemical in flaxseed
is the lignan secoisolariciresinol diglucoside (SDG). SDG is a nutraceutical with
7
potential applications in the therapy of estrogen related cancers, cardiovascular
disease, diabetes and lupus nephritis (Westcott and Muir, 2003a).
2.1.2.1. Flaxseed lignans
Lignans are a very complex class of plant compounds, which have a role
in the plant’s natural defence mechanism. They are usually formed by C8-C8’
coupling of two conyferil alcohols, and are part of large structures such as dimers,
trimers, or higher oligomers. The main flaxseed lignan, SDG, is present in
enantiomeric forms (Figure 2.1), with the (+) antipode in excess (about 99%) and
the (-) antipode in minor concentration (about 1%). The existence of two SDG
isomers was first reported by (Bambagiotti-Alberti et al., 1994). SDG is part of
polymeric structures, in which it is covalently bound via ester linkages to 3-
hydroxy-3-methyl glutaryl (HMG). SDG can be released by hydrolytic cleavage
of the polymeric structures (Davin and Lewis, 2003, 2005; Ford et al., 2001).
SDG is the main exogenous precursor of mammalian lignans. Upon ingestion,
SDG is converted into the mammalian lignans enterolactone and enterodiol by the
gut microflora (Ford et al., 2001).
Ford et al. (2001) studied the biosynthetic pathways leading to the
formation of SDG, by application of stable and radioisotope precursor and tracer
experiments. Given the existence of the two SDG enantiomers, they proposed two
distinct biochemical pathways leading to the formation of secoisolariciresinol
enantiomers (Figure 2.2). Both pathways involve the C8-C8’ coupling of two
conyferil alcohols with formation of (-) or (+) pinoresinol and lariciresinol, which
are further converted into (-) (Figure 2.2 A) or (+) (Figure 2.2 B)
secoisolariciresinol (SECO), respectively. Both, (-) and (+) SECO are further di-
glucosylated at the position C-9 and C-9’ by the uridine diphosphate (UDP)
diglucosyltransferase enzyme to form (-) and (+) SDG (Figure 2.3). Both, (-) and
(+) SDG undergo esterification by 3-hydroxy-3-methylglutaryl coenzyme A
(HMG CoA).
8
There seems to be some disagreement in the literature regarding the
representation of the chemical structures of SDG enantiomers and its aglycons.
The isomeric structures are sometimes used interchangeably. The (+) SDG
structure presented in Figure 2.2 seem to be in agreement with most literature
examples. However, the structural representation of (-) secoisolariciresinol as
presented by Davin and Lewis (2005) and Ford et al., (2001) is identical with that
of the (+) secoisolariciresinol, instead of its mirror image.
Figure 2.1. Chemical structure of secoisolariciresinol diglucoside enantiomers,
(+) SDG, (-) SDG, and the mammalian lignans enterodiol (ED) and enterolactone
(EL) (Ford et al., 2001).
9
Fi2
2B
ih
il
thl
dit
thf
tif
()
il
ii
il
(A)
d(+
)
Figu
re
2.2.
B
ioch
emic
al
path
way
s le
adin
g to
th
e fo
rmat
ion
of
(-)
seco
isol
aric
iresi
nol
(A),
and
(+)
seco
isol
aric
iresi
nol (
B),
(Dav
in a
nd L
ewis
, 200
5; F
ord
et a
l., 2
001)
.
10
Figu
re 2
.3.
Bio
synt
hetic
pat
hway
to
the
form
atio
n of
the
SD
G
cont
aini
ng p
olym
ers.
(Dav
in a
nd L
ewis
200
5; F
ord
et a
l., 2
001)
.
11
As presented in Figure 2.3, the mono- and di-esterifications yield the
monomer 6a-HMG SDG, and the dimer 6a, 6a’-di-HMG SDG, respectively.
HMG and SDG are ester-linked at the positions C-6a, and C-6a’, of the glucose
molecules. According to Ford et al., (2001) the monomer and dimer could be the
main constituents of the flaxseed polymer (Figure 2.4).
Figure 2.4. Polymer biosynthesised through the ester-linkage of HMG-SDG
mono- and di-mers with 3-hydroxy-3-methyl-glutaryl (HMG), (Ford et al., 2001).
Ford et al., (2001) studied the levels of lignans at 6 stages of development
of Linum usitatissimum. The seeds were segregated into 5 stages based on their
dimensions. Stage 1 included the smallest newly developed seeds, and stage 5
contained the largest seeds. Seeds from stages 5 and 6 had the same dimensions;
their segregation was based on the seed coat colour. Seeds from stage 5 had a
translucent vanilla colour, and the mature seeds from stage 6 were brown. The
12
authors extracted the lignans as follows. Samples of 100 mg of pulverised freeze-
dried seeds were extracted with 2 ml of ethanol:water (40:60, v/v) for 4 hours at
room temperature under constant rotation (250 rpm). The solution was filtered
and hydrolysed at room temperature with 0.5 ml 10 M NaOH, for 2 hours under
constant rotation (250 rpm). The hydrolysate was then extracted successively with
hexane, ethyl acetate, and trichloromethane (1 ml each). The resulting aqueous
solubles were freeze-dried for 24 hours, and then dissolved in methanol:water
(30:70, v/v) and analyzed. Non-hydrolysed aqueous ethanolic extracts were also
analysed in order to find out the compounds that were released by hydrolysis. The
results showed that the alcoholic and hydrolysate extracts from all developmental
stages did not contain the following lignans: pinoresinol, isolariciresinol,
matairesinol, and secoisolariciresinol. The ethanolic non-hydrolysed extracts
contained small amounts of the SDG enantiomers and 6a-HMG SDG (monomer),
an important amount of 6a-6a’-HMG SDG (dimer), and very large amounts of an
unknown ester-linked SDG polymer that appeared as a broad high peak in the
chromatograms. Upon hydrolysis of the extract the amount of (+) SDG increased
tremendously and the amount of (-) SDG increased slightly. Other phenolics
found were conyferil alcohol, p-coumaric acid, p-coumaric acid glucoside and
ferulic acid glucoside. The broad peak of unknown polymer was no longer present
after hydrolysis. This suggested the cleavage of the complex structure with release
of high amounts of SDG. The amounts of monomer slightly increased, and the
amounts of dimer marked an important decrease (Ford et al., 2001). Based on
these results the authors suggested that the flaxseed polymer is actually a mixture
of various dimers, trimers, tetramers, and so forth, containing ester-linked SDG-
HMG. It is worth noting that the extraction-hydrolysis method used by Ford et al.,
(2001) did not lead to a complete cleavage of the ester links between the SDG and
HMG. Therefore, an unknown quantity of SDG remained “trapped” in the
monomer 6a-HMG SDG and the dimer 6a, 6a’di-HMG SDG, which peaked after
(-) SDG. Using this method will not lead to an exact quantification of the SDG
content in flaxseed.
13
2.1.3. Methods of extraction of SDG from flaxseed
2.1.3.1. Conventional extraction methods
There is no standard operating procedure for extracting lignans from
flaxseed. Generally, SDG is extracted from defatted matrices such as defatted flax
meal (DFM) or defatted flax hull (DFH). It is recommended to extract first the
lipids from the matrix with a non-polar organic solvent (hexane, petroleum ether,
or dichloromethane) as they might interfere with the analysis of lignans (Willfor
et al., 2006). The extraction methods usually include an extraction step with
aqueous alcohol of the lignan polymers, followed by the cleavage of the
polymeric structures by alkaline or acid hydrolysis. Generally, these methods are
very time consuming (duration can vary from several hours up to 2 or 3 days) and
require multiple manipulations of the extracts. However, for analytical purposes,
the alcoholic extraction step can be eliminated. According to Eliasson et al.,
(2003) the direct hydrolysis of DFM can result in a shorter extraction time, and
better SDG extraction yields as opposed to the lengthier alcoholic extraction
methods followed by hydrolysis. SDG concentrations can range from 9 to 30
mg/g DFM depending on the cultivar, year, and location of growth (Eliasson et
al., 2003; Westcott and Muir, 2003a). SDG is mainly concentrated in the seed coat
of flaxseeds (Madhusudhan et al., 2000).
Meagher et al. (1999) presented a conventional method of extraction,
which involved the extraction of lignans from defatted flaxseed meal (DFM) with
an aqueous organic solvent, followed by the acid hydrolysis of lignan glucosides
in order to release secoisolariciresinol (SECO), the aglycon of SDG. The authors
were the first to report the presence of isolariciresinol and pinoresinol in flaxseed
extracts. The extraction method involved the following steps. Four grams DFM
were extracted with 50 ml methanol/water (80:20, v/v), for 4 hours at 55˚C in a
shaker water bath. The obtained extract was filtered and evaporated at 35˚C until
the volume reached 10 ml. The concentrated aqueous extract was then hydrolysed
with 0.8 ml 1 M HCl for 1 hour at 100˚C. The acid hydrolysate was diluted with
10 ml water and extracted twice with 20 ml ethyl acetate:hexane (1:1, v/v). A dry
14
flaxseed extract was then obtained, which was redisolved in methanol and filtered
for HPLC analysis. It was observed that the acid hydrolysis dehydrates a small
amount of SECO with the formation of the artefact anhydro-secoisolariciresinol, a
compound that is not naturally present in flaxseed (Meagher et al., 1999). Besides
SECO, the authors reported the presence of isolariciresinol, pinoresinol, and
matairesinol compounds that were not found later by Ford et al., (2001).
Matairesinol might have resulted from SECO degradation. In other plant sources
matairesinol results from the downstream conversion of SECO (Davin and Lewis,
2003).
Johnsson et al. (2000) extracted samples of 0.5 g DFM with 10 ml mixture
of 1,4-dioxane / 95% ethanol (1:1, v/v) at 60˚C for 16 hours in a shaker water
bath. The solution was centrifuged at 2000 rpm for 20 min, and then evaporated to
dry state in vacuum at 40˚C. The dry extract was hydrolysed with 10 ml 0.3M
NaOH for two days under constant rotation. The hydrolysate was then acidified to
pH=3 with 2 M H2SO4. The acidification of extracts is necessary in order to avoid
the ionization of the carboxylic and phenolic groups. However, this leads to the
formation of important amounts of salt, which have to be removed prior to HPLC
analysis by solid-phase extraction (SPE) on C18 reversed SPE columns. The
authors used this method to analyse the content of SDG in 14 Swedish and 15
Danish flaxseed cultivars. The concentration of SDG varied between 11.7 and
24.1 mg/g DFM on a dry weight basis. The method used by Johnsson et al. (2000)
was later re-applied with slight modifications to the extraction of SDG from DFM
by Johnsson et al. (2002) and Kamal-Eldin et al. (2001). Besides SDG, Johnsson
et al. (2002) also reported the presence of p-coumaric acid glucoside and ferulic
acid glucoside as noted by Ford et al. (2001). Kamal-Eldin et al. (2001) identified
an oligomer composed of 5 SDG residues interconnected by 4 HMG residues,
which was assigned as the main SDG containing lignan structure in flaxseeds.
This is in accordance with the suggestion of Ford et al. (2001) that SDG is part of
polymeric structures in which it is covalently bound to HMG via ester linkages.
These polymeric structures might be mixtures of di-, tri-, tetra-, and higher
oligomers.
15
Fritsche et al. (2002) identified the two isomers of SDG by on-line LC-
NMR-MS analyses of DFM extracts obtained with the following method. Samples
of 100 g DFM were extracted with methanol:water (75:25, v/v) (unspecified
volume) for 24 hours at 65°C with continuous stirring. The solution was filtered
and concentrated by vacuum evaporation. A hydrolysate was obtained by addition
of 0.3M NaOH in proportion of 1:1 (w/w) (unspecified duration), which was then
acidified to pH=6 by addition of glacial acetic acid. The acidified hydrolysate was
filtered and further concentrated in view of analysis (Fritsche et al., 2002). It is
now well known that SDG is found in enantiomeric forms in the following
proportion (+)-SDG about 99%, and (-)-SDG about 1% (Davin and Lewis, 2003,
2005; Ford et al., 2001).
Eliasson et al. (2003) demonstrated that the alcoholic extraction step can
be skipped thus allowing for a faster sample preparation in view of HPLC analysis
of lignans from flaxseed. The method involves a direct hydrolysis step with alkali
followed by precipitation of sugars and proteins with alcohol. The authors
investigated the effect of temperature (20, 30 and 40°C), NaOH concentration
(0.3, 1 and 1.7 M), and hydrolysis time (0.5, 4, 16, and 48 hours, only at 20°C) on
the extraction yield of flaxseed lignans. The results showed that the temperature
and concentration of alkali had little influence on the extraction yields of lignans.
For practical reasons, the authors preferred 20°C and 1M NaOH, to further
investigate the effect of time. The results showed that very high extraction yields
were achieved in only 30 min. The concentration of alcohols used for sugar and
protein precipitation however had a significant effect. Concentrations of 60%
aqueous ethanol and 90% aqueous methanol gave similar results, but lower
ethanol concentrations resulted in extra (unidentified) peaks in the
chromatograms. The authors applied the extraction method previously used by
Johnsson et al. (2000) for comparison purposes in order to estimate the efficiency
of the direct hydrolysis method. The yield of (+) SDG obtained with direct
hydrolysis method was 22.3 mg/g DFM, but the yield obtained with the alcoholic
extraction followed by hydrolysis was only 19.1 mg/g DFM. The repeatability of
the direct hydrolysis method was reported in terms of relative standard deviation
16
(RSD) of 6 extractions per day carried out for 3 days by two researchers. The
RSD values for (+)SDG were 6.1% and 3.4%. This method was used for
analysing lignan and hydroxycinnamic acid glucoside levels in flaxseed grown at
various locations in Sweden. The following results were obtained (+)SDG = 11.9
– 25.9 mg/g, (-)SDG = 2.2 – 5.0 mg/g, p-coumaric acid glucoside = 1.2 – 8.5
mg/g, and ferulic acid glucoside = 1.6 – 5.0 mg/g (Eliasson et al., 2003). The
authors recommended the following combination for the direct hydrolysis of
lignans from defatted flaxseed; hydrolysis with 1M NaOH for 1 hour at 20°C,
followed by acidification (pH=3) in order to keep the extract stable, and
precipitation of sugars and proteins with alcohol for at least 10 min. This method
is faster than the methods presented above and offers good repeatability.
The reason for which it was possible to enhance the extraction yield by
using direct hydrolysis method might be explained by the incomplete cleavage of
the ester link between the SDG and HMD with the longer extraction-hydrolysis
method. In fact, it is possible that regardless of the method used some unknown
quantity of SDG will remain trapped in the monomeric and dimeric structures
reported by Ford et al. (2001). Moreover, the typical chromatograms of SDG
extracts presented in the literature showed some unidentified compounds that
eluted immediately after SDG. These unknown compounds could be the 6a-HMG
SDG monomer and 6a, 6a’di-HMG SDG dimer.
2.1.3.2. Microwave-assisted extraction (MAE)
Note: The two MAE papers presented below were published on-line in
April 2007 by two groups of researchers, one from China, and the other from
France. The preliminary studies, the experimental design, and the optimisation of
the MAE method that constitute the subject of this thesis were carried out in the
period August 2006 – February 2007. The experimental work was finalized in
early March 2007. Therefore, the MAE method developed by the author of this
thesis had no relation and had not been influenced in any way by those two recent
publications.
17
Zhang and Xu (2007) reported a microwave-assisted method for SDG
extraction from defatted flaxseed hull (DFH). The authors used a microwave oven
that operated at 2450 MHz with emission of microwave power at five levels (50,
130, 220, 315, and 390 W). There are no specifications regarding the type of
microwave cavity (mono-mode, multi-mode, open, or closed), also there is no
specification regarding the presence of any temperature sensor. It is possible that
the oven was a domestic microwave multi-mode cavity. The extraction method
included the following steps, mucilage removal, dehulling, aqueous ethanol
extraction and alkaline hydrolysis. The mucilage was removed from the whole
seeds by soaking, leaching, and washing with water at 70°C (unspecified
duration). The seeds were dehulled in a mill, and the hulls were separated in a
water cyclone. The hulls were dried, ground, and defatted with hexane for 1 hour.
The obtained DFH flour was used for extraction experiments. The authors
observed that pre-soaking the samples in solvent for 5 min, enhanced the
extraction yields of SDG. The MAE method was optimized by using a central
composite design with 15 factor combinations including 3 center points. All
combinations were run in triplicates. It is worth noting that the extraction yield
was maximized only in relation to the ethanol concentration (35-45%), solvent to
sample ratio (15:1 – 25:1 ml/g), and total irradiation time (75 -105 s, in on/off
sequences of 10s). Moreover, the microwave power level was not included in the
optimisation design. It is worth noting that the effect of power level (50, 130, 220,
315, and 390 W) was investigated prior to the optimisation design by extraction of
1 g of pre-soaked sample (as mentioned above) with 20 ml 40% aqueous ethanol.
Subsequently, the power level 130W was chosen, and was used in the
optimization experiment. The authors presented a complete second order
polynomial equation specifying that all the factors (main, interaction and
quadratic) were significant. However, the overall level of significance of the
model was not presented. The authors estimated the adequacy of the model based
on the significant lack of fit (indicator of how well the quadratic terms represent
the surface) and the R2=0.97. R-square is usually high when the model involves
many factors and replications. The authors proposed the following MAE method:
18
irradiation time 90.5 s (on/off sequences of 10 s) at 130 W for a solvent to sample
ratio of 21.9: 1 (ml/g), with pre-soaking the sample in solvent for 5 min prior to
extraction. All samples were re-extracted, centrifuged, and the supernatants
combined and dehydrated in vacuum. The dry extracts were hydrolysed for 2
hours at room temperature by addition of 0.25M NaOH (unspecified volume).
After hydrolysis the extracts were acidified to pH=4 with 4 M HCl. The
hydrolysed extracts were further dehydrated in vacuum, and the dry extracts were
dissolved in 50 ml methanol in view of HPLC analysis. The efficiency of MAE
was assessed by comparison with conventional stirring extraction with 50%
ethanol for 3 hours and Soxhlet extraction with 75% ethanol for 2.5 hours. The
results showed that MAE was more efficient with 11.7 mg SDG/g DFM, as
opposed to stirring extraction 10 mg SDG/g DFM, and Soxhlet 7.6 mg SDG /g
DFM (Zhang and Xu, 2007). Overall, this method involved many long
manipulation steps, of re-extraction and drying, followed by 2 hours of hydrolysis
and drying. Moreover, some of the lignan polymers might have been washed off
during the initial steps of mucilage removal and dehulling. When investigating the
effect of power and alcohol concentration it would have been useful if the authors
had included both factors in the same design in order to evaluate their interaction.
In this paper, the power effect was investigated as a function of time at 40%
aqueous ethanol. It is well known that the mixtures of water - alcohol have a
synergistic behaviour at microwave frequencies as presented later in the
microwave section of this literature review.
Beejmohun et al. (2007) used an open-vessel microwave system (Discover
CEM), which operated at 300 W maximum power at 2450 MHz frequency. The
authors extracted SDG, p-coumaric acid glucoside and ferulic acid glucoside from
pressed flaxseed cake (PFC). It is not clear if the flaxseed cake was defatted prior
to extraction. Samples of 500 mg were extracted with a mixture of 20 ml of 70%
aqueous methanol and 0.1 or 1M NaOH (unspecified volume). After the
extraction, the samples were neutralised with acetic acid, and filtered in view of
HPLC analysis. The microwave power (50, 100, and 150W), and extraction times
(1, 3, 5, 7, 10, and 15 min) were investigated. Graphical representation of the data
19
suggested that 3 min extraction with 70 % ethanol and 1M NaOH (unknown
volume), regardless of the microwave power level, resulted in high extraction
yields of SDG (16.1 mg/g PFC), p-coumaric acid glucoside (3.7 mg/g PFC), and
ferulic acid glucoside (4.1 mg/g PFC). The results obtained with MAE were
compared with those obtained by conventional extraction at 60°C with 70%
aqueous ethanol for 3 hours followed by 3 hours hydrolysis with 0.1 or 1M
NaOH. The data showed that MAE was more efficient than the traditional method
(Beejmohun et al., 2007). This method was incompletely reported, and there is no
specification of temperature monitoring. Moreover, no statistical analysis was
presented in this paper.
The first MAE method presented here involved numerous lengthy
preparation steps and the second MAE method was incompletely reported.
Nonetheless, they prove that microwave power can enhance the extraction of
lignans and other phenolic compounds from flax. It is possible that the microwave
radiation interaction with the polar lignan molecules and polar solvents leads to
increased lignans solubilisation and/or enhanced extraction kinetics.
2.1.4. Claimed health benefits of SDG
Westcott and Muir (2003b) conducted a literature review regarding the
capacity of flaxseed lignans to prevent disease and promote health. The authors
concluded that, although the mechanism of action is not always understood, the
overall results showed that flax lignans have beneficial health effects. Although,
there were some negative or non-conclusive studies, it is generally suggested that
flaxseed lignans have a beneficial effect on breast, colon, prostate and thyroid
cancer. Positive results were also obtained with flaxseed lignans studies
concerning lupus and polycystic kidney disease, and diabetes mellitus type I and
II. Flaxseed lignans were also shown to be hepato-protective and to lower the risk
of heart disease.
20
2.1.4.1. Cancer related studies
It is well known that cancer is likely to be affected by dietary factors.
Studies showed that some phenolic compounds from plants have chemoprotective
attributes. However, the phenolics present in the diet might not be in sufficient
quantities to protect against carcinogenic agents. Therefore, a more successful
approach might be offered by the intake of dietary supplements (Fresco et al.,
2006).
Theoretically, three stages of carcinogenesis can be differentiated, namely
the initiation, promotion and progression of cancer tumours. The tumour initiation
stage starts when the exposure to carcinogens leads to DNA damage. The
promotion takes place through clonal expansion, hyper-proliferation, tissue
remodelling and inflammation. Finally, during the progression stage, the pre-
neoplastic cells develop into tumours. The prevention, arrest, or reversal of the
carcinogenesis process at any stage involves chemoprevention by using
pharmacological or dietary agents (Fresco et al., 2006).
Studies on rats using a mammary cancer model at the initiation stage
showed that the intake of 1.5 mg SDG per day resulted in a 47% reduction in the
number of tumours, and 37% reduction in tumour multiplicity. The intake of 1.5
mg SDG per day, at the late promotion – early progression stage, reduced the
volume of existing tumours by 54% and the volume of new tumours by 75%. In
addition, the number of tumours, and the incidence of new tumours were reduced
by 50%, and 27%, respectively. In other studies, SDG and its metabolites
inhibited lung metastasis, and the metastasis and growth of human breast cancer
cells in mice. Studies on pre-menopausal and post-menopausal women revealed
an inverse correlation between the concentration of SDG metabolite,
enterolactone, in blood serum and the risk of breast cancer. Another study showed
that SDG metabolites, enterodiol and enterolactone, had an inhibitory effect on
colon tumour cells (Westcott and Muir, 2003b).
21
2.1.4.2. Antioxidant properties of SDG
Kitts et al. (1999) studied the antioxidant capacity of SDG, enterodiol
(ED) and enterolactone (EL) by using lipid and aqueous in-vitro model systems.
The capacity to scavenge the hydroxyl radical (·OH) was evaluated for
concentrations of 10 and 100 micromole (µM) lignans. SDG and its metabolites
proved to be efficient antioxidants for both systems. However, the metabolites,
ED and EL, were stronger antioxidants. The highest antioxidant activity was
obtained with 100 µM ED and EL for both lipid and aqueous systems (Kitts et al.,
1999).
Prasad (2000a) investigated the antioxidant activity of SDG, SECO, ED
and EL on human blood samples by using zymosan – activated
polymorphonuclear leukocytes (PMNL) chemoluminescence (CL). Treating the
blood with PMNL results in the production of the following free radicals:
superoxide anion (O2-), hydrogen peroxide (H2O2), hydroxyl radical (·OH), and
the singlet oxygen (-O2). The PMNL chemoluminescence is amplified by the
presence of these free radicals, and can be used to quantify the amounts of free
radicals in blood. The chemoluminescence of PMNL treated blood increased 85
fold. It is known that SDG is metabolized in the digestive track into its aglycon
SECO, then into ED and finally into EL. The antioxidant capacities of SDG and
its metabolites were assessed by comparison among them, and as opposed to the
antioxidant capacity of vitamin E for a fixed concentration of 2.5 mg/ml blood.
The results are presented in Table 2.1. SDG metabolites have a very high
antioxidant capacity, more than 4 times higher than that of vitamin E, and more
than 3 times higher than that of their precursor. The capacity of flaxseed lignans
to scavenge free radicals is dependent on their concentration. The results obtained
by Prasad (2000a) are summarised in Table 2.2. The author investigated the effect
of concentration (0.5, 1, 2.5, 5, and 10 mg) of SDG, SECO, ED, EL, and vitamin
E on the inhibition of zymosan- activated PMNL chemoluminescence in human
blood samples.
22
Table 2.1. Antioxidant capacity of flax lignans (Prasad, 2000a).
SDG SECO ED EL
Fold increase in the antioxidant capacity for 2.5mg/ml blood
As opposed to vit. E 1.27 4.86 5.02 4.35
As opposed to SDG - 3.82 3.95 3.43
Table 2.2. Concentration – dependent inhibition of PMNL chemoluminescence
Concentration
mg/ml blood
SDG SECO ED EL VIT. E
Inhibition of chemoluminescence in %
0.5 5 70 72 72 9
1 23 93 82 81 -
2.5 27 99.5 96 86 17
5 45 100 96 83 88
10. - 100 95 - 97
As it can be seen from Table 2.2, SDG metabolites are effective free
radicals scavengers. In fact, they are more effective than the commonly used
antioxidant, vitamin E. The author suggested that in high quantities, flaxseed
lignans scavenge the following free radicals: superoxide anion (O2-), hydrogen
peroxide (H2O2), hydroxyl radical (·OH), and the singlet oxygen (-O2). The
oxygen free radicals are known to play an important role in the development of
hypercholesterolemic atherosclerosis and diabetes mellitus type I and II. Given its
antioxidant activity, it is suggested that SDG, through its metabolites, could be
used in the prevention of diseases such as hypercholesterolemic atherosclerosis,
23
diabetes mellitus, ischemic heart disease, heart failure, and inflammatory diseases
(Prasad, 2000a).
The insulin dependent diabetes mellitus (type I) is characterised by
pancreas cell lesions and significant reduction or absence of insulin secretion. The
chances of developing diabetes are greater than 50% for individuals who have
relatives with type I diabetes. Prasad, (2000b) demonstrated that type I diabetes is
mediated through oxidative stress. The author investigated the effects of SDG on
the development of diabetes in diabetes-prone rats by using the model of human
type I diabetes. The rats were given 22 mg SDG / kg body mass/day in drinking
water. The diabetes incidence was 72.7 % in the rats non-treated with SDG, and
only 21.4 % in the rats that received the SDG treatment. Due to its antioxidant
capacity, SDG prevented the development of diabetes by 71% (Prasad, 2000b).
The author suggested that SDG could be used in diabetes prevention in diabetes-
prone individuals.
Type II diabetes is characterised by increased production of glucose in the
liver, impaired glucose tolerance, insulin resistance and hyperlipidemia. The
production of glucose in the liver is modulated by the enzyme
phosphoenolpyruvate carboxykinase (PEPCK). The drugs used in the treatment of
type II diabetes have antioxidant activity and inhibit the expression of PEPCK.
Prasad, (2002) investigated the hypoglycaemic effect of SDG by using cultures of
rat liver cells treated with 10 nanomole/litre (nM) insulin or 100 micromole/litre
(µM) SDG. The results showed that while 10 nM insulin almost completely
suppressed the PEPCK gene expression, 100 µM SDG suppressed the gene
expression completely. It appears that SDG prevents diabetes due to its
antioxidant activity, and inhibition of PEPCK gene expression in the case of type
II diabetes (Prasad, 2002).
Hypercholesterolemic atherosclerosis is characterized by increased lipid
peroxidation by oxygen free radicals (OFRs) and a decrease in the antioxidant
reserve in the affected tissue. Prasad (1999) studied the effect of SDG on the
development of atherosclerosis in rabbits. The rabbits received high cholesterol
diets with and without SDG treatment (15 mg/ kg body weight/day) for 8 weeks.
24
The high cholesterol diet without SDG treatment increased the serum levels of
total cholesterol (TC) and low-density lipoprotein (LDL), bad cholesterol, and
reduced the levels of high-density lipoprotein (HDL), good cholesterol. The high
cholesterol diet with SDG treatment increased the serum levels of HDL
cholesterol by more than 140%, decreased the TC and LDL cholesterol levels by
33% and 35%, respectively. The hypercholesterolemic atherosclerosis was
reduced by 73%. It appears that SDG effectively reduced the hyper-
cholesterolemic atherosclerosis due to its antioxidant activity, by decreasing the
levels of antioxidant stress and LDL cholesterol and increasing the level of HDL
cholesterol. Therefore, SDG may also reduce the risk of coronary artery disease
(Prasad, 1999).
2.1.5. Flaxseed lignans metabolism
Upon ingestion of flaxseed or flaxseed containing products, SECO is
released from the polymeric structures by the action of hydrochloric acid in the
stomach. The mammalian lignans, ED and enterolactone EL, result from bacterial
degradation of SECO in the colon (Webb and McCullough, 2005). Nesbitt et al.
(1999) investigated the effect of flaxseed intake on 9 pre-menopausal women. The
women followed a seven-day diet supplementation with 5, 15, 25 g of raw
flaxseed, or 25 g of processed flaxseed (bread or muffin). The results showed that
ED was the main lignan present in plasma and urine. EL and SECO were found in
small quantities. The consumed flaxseed quantity was significantly correlated
(r=0.72) with the lignans concentration in urine. The lignans were detected in the
serum 9 h after the initial flaxseed consumption, and were present in the blood
circulation for 24 h. The levels of lignans in plasma and urine were significantly
higher after 1 week of consumption, but they stabilised by the 8th day. It appears
that consuming flaxseed once per day is sufficient to maintain lignans in blood
circulation. Although in this study ED was the main mammalian lignan, other
studies suggest that the final product of flaxseed lignans metabolism varies among
25
individuals. The main lignan could be ED or EL. Cooking the flaxseed did not
affect the plasma and urine concentration of lignans (Nesbitt et al., 1999).
Studies showed that the metabolism of flaxseed lignans and the excretion
of mammalian lignans are inhibited by the administration of antibiotics or germ-
free induced environments (Webb and McCullough, 2005).
Kuijsten et al., (2005) investigated the effect of crushing and grinding of
flaxseed on the bioavailability of mammalian lignans in blood plasma of humans.
Twelve healthy subjects supplemented their diets with 0.3 g of whole, crushed or
ground flaxseed per kg body weight per day. The bioavailability of mammalian
lignans in plasma was as follows, 270 (nM) for ground flaxseed > 122 (nM) for
crushed flaxseed > 85 (nM) whole flaxseed.
2.1.6. Conclusions
According to the Flax Council of Canada, Linum usitatissimum, the
common oil type and fibre type flaxseed, is an important crop for the economy of
Canada, which is the world leader in the production and export of flaxseed
(Anonymous, 2007a).
Due to its chemical composition, flaxseed can become a key
ingredient/raw material for the functional foods and nutraceutical industries
(Oomah, 2001; Westcott and Muir, 2003a; Westcott and Muir, 2003b). Flaxseed
contains high levels of SDG (9-30 mg/g), which is the main exogenous
mammalian lignan precursor (Westcott and Muir, 2003a).
Flaxseed lignans are formed by the C8-C8’ coupling of two conyferil
alcohols. In flaxseed, SDG is part of complex polymeric structures, in which is
covalently bound via ester linkages to 3-hydroxy-3-methyl-glutaryl (HMG), from
which it can be released by alkaline or acid hydrolysis. SDG is found in
enantiomeric form as (+)-SDG (99%) and (-)-SDG (1%) (Ford et al., 2001).
The conventional extraction methods reported in the literature usually
involve the extraction of SDG containing polymers from defatted matrices with
mixtures of alcohol (ethanol, methanol, 1,4-dioxane-ethanol) and water, followed
26
by hydrolysis in order to release the SDG from the polymeric structures (Johnsson
et al., 2000; Johnsson et al., 2002; Kamal-Eldin et al., 2001; Meagher et al.,
1999). These extraction methods are time consuming; they can take from few
hours up to 2-3 days, and involve numerous sample manipulations.
A direct hydrolysis method was also reported (Eliasson et al., 2003). This
method takes about 1h:30 min as it skips the alcoholic extraction step. In fact, this
method demonstrated that for analytical purposes there is no need to first extract
the polymer and then to hydrolyse it in order to release the SDG. Instead, the
matrix is hydrolysed for direct release of SDG. Moreover, the direct hydrolysis
method was reported to give higher extraction yields as compared to the longer
alcohol extraction – alkaline hydrolysis methods.
Two new extraction studies have used microwave-assisted methods to
extract SDG from flaxseed hull and meal (Beejmohun et al., 2007; Zhang and Xu,
2007). It appears that microwave radiation can enhance the extraction yields of
SDG as opposed to conventional methods. The enhanced extraction yields
obtained with MAE could be due to increased lignans solubilisation and/or
enhanced extraction kinetics, due the microwave interaction with the polar lignans
and polar solvents.
The possibility of enhancing the extraction yields of SDG resides in the
fact that SDG is part of polymeric structures and regardless of the method used, it
appears that SDG is not completely released from this structure. An unknown
amount of SDG will remain “trapped” by ester-linkages to HMG in monomeric
and dimeric structures as observed by Ford et al. (2001). However, more of this
trapped SDG could be released by new optimised extraction methods, such as the
direct hydrolysis method reported by Eliasson et al. (2003) and the MAE methods
reported by Beejmohun et al. (2007) and Zhang and Xu (2007). For as long as the
SDG is not completely released from the complex structure, it is expected that
various extraction methods would give different results.
Studies showed that SDG is a nutraceutical that offers several health
benefits. SDG has antioxidant capacity (Kitts et al., 1999; Prasad, 2000a), reduces
the risk of hormone-related cancer (Oomah, 2001; Westcott and Muir, 2003b),
27
reduces the risk of diabetes mellitus type I and II, reduces the levels of LDL
cholesterol and has cardioprotective effect (Prasad, 2000b). Flaxseed lignans are
stable during cooking; therefore, flaxseed can be consumed in raw or cooked
forms. The concentration of lignans in blood serum and urine are dose-dependent.
It appears that one daily dose (5-25g) of flaxseed is sufficient in order to maintain
the lignans in the blood circulation (Nesbitt et al., 1999). The bioavailability of
mammalian lignans in blood plasma is significantly enhanced by grinding the
flaxseeds (Kuijsten et al., 2005). However, the metabolism of mammalian lignans,
and therefore their bioavailability is inhibited by the administration of antibiotics
and induced germ-free environments (Webb and McCullough, 2005).
30
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29
Plank’s constant (h=6.6256 ×10-34 Js). Generally, the energy of the microwave
photon is given in electron-volt (eV) in literature; 1eV=1.6×10-19 J.
(1)
Where E = energy of the microwave photon [J]
h = Plank’s constant [Js]
ν = frequency [Hz]
C0 = speed of light [m/s]
λ = wavelength [m]
The energy carried by the electromagnetic radiation is inversely
proportional to its wavelength. At frequencies between 300 MHz and 300 GHz
the energy of the microwave photon is roughly located between 1.2 ×10-6 eV and
1.2×10-3 eV (Gardiol, 1984; Rosenthal, 1992); at 2.45 GHz it is about 1×10-5eV
(Will et al., 2004).
Electromagnetic radiation has dual nature. James Clerk Maxwell
demonstrated in 1860s the association between the oscillating magnetic (H) and
electric (E) fields. These fields operate in planes, which are perpendicular to each
other and to the direction of propagation. The wave of electromagnetic radiation is
schematically represented in Figure 2.6. According to Maxwell’s equations,
microwave radiation propagate across space at the speed of light
(co=2.997925×108 m/s). The wave of electromagnetic radiation is characterized by
wavelength, which is the distance between two adjacent peaks. The wavelength of
a microwave signal, (λ), defined by the ratio co/ν, is located between 1m at 300
MHz, and 1mm at 300 GHz (Gardiol, 1984; Rosenthal, 1992).
Microwave energy is non-ionizing; the energy of the microwave photon is
not high enough to break a chemical link. It is, in fact, smaller by several orders of
magnitude than the energy of a molecular bond. As presented in Table 2.3, the
30
energy of the microwave photon is smaller than the energies of Brownian motion
and chemical bonds, and therefore cannot initialize constructive or destructive
reaction steps (Gardiol, 1984; Kappe, 2004; Rosenthal, 1992; Stuerga and
Delmotte, 2002; Will et al., 2004).
Figure 2.6. The wave of electromagnetic radiation
Table 2.3. Microwave photon, Brownian motion and chemical bonds energies
Microwave photon Brownian motion
Hydrogen bonds
Covalent bonds
Ionic bonds
Energy (eV)
1.2 ×10-6 to 1.2×10-3 (*)
1 ×10-5 (**) 0.025 0.04 - 0.44 5.0 7.6
Adapted from Will et al. (2004). *Approximate energy range of the microwave photon for the frequency range 300 MHz – 300 GHz (Gardiol, 1984; Rosenthal, 1992). **Energy of the microwave photon at 2.45 GHz(Will et al., 2004).
2.2.1. Microwave application regulations
Microwave radiation is mostly used in radio-communications, mobile
telephones, radar transmissions and heating. In order to avoid interference
between these applications the 915 MHz and 2.45 GHz bands were reserved for
31
industrial, scientific, and medical (ISM) purposes. While domestic microwave
ovens and laboratory microwave systems operate at 2.45 GHz, for industrial
applications the 915 MHz presents certain advantages such as higher penetration
depth (Regier and Schubert, 2005).
The safety regulations for microwave systems operation are concerned
with the maximum microwave exposure or absorption by humans, and the
maximum leakage of microwave equipments. A limit of 1 mW/cm2 for human
exposure is considered safe. The maximum specific absorption rate (SAR) for
humans is 0.4 Watts of incident power per kg body. The maximum microwave
leakage at a distance of 5 cm from the oven should not be greater than 5 mW/cm2.
Microwave leakage is non-focused radiation; its power decreases in proportion to
the inverse square of the distance from the radiation source (Regier and Schubert,
2005).
2.2.2 Microwave technology
2.2.2.1. Microwave ovens
Microwave heating involves microwave generators, waveguide
transmission, resonant cavities, mode stirrers (only for multi-mode cavities), and
power supply. The microwave generators are special oscillator tubes such as
magnetrons and klystrons. They generate the microwaves, which are transported
to the resonant cavities (ovens) by means of waveguides. The mode stirrer ensures
homogeneous heating by distributing the microwave radiation between the
different resonant modes of the cavity (Copson, 1975; Gardiol, 1984; Orsat et al.,
2005; Regier and Schubert, 2005).
2.2.2.2. The magnetron
The magnetron is the most used microwave generator for the industrial
and domestic applications (Regier and Schubert, 2005). The magnetron is a
cylindrical diode. A diode is a vacuum tube with two electrodes, the anode and
the cathode. The anode is a ring of resonant cavities, which are the source for the
32
oscillations of the microwave energy. These cavities are vacuum enclosed. A
magnetic field impinges on the vacuum tube. The cathode is situated in the center
of the vacuum tube, surrounded by the anode. It is a high temperature metal
cylinder coated with electron-emitting material. The cathode has high negative
potential. The interaction between the electric and magnetic fields results in a
space discharge (Figure 2.7). This space discharge resembles a spoked wheel,
with the spokes being electron clouds, as the electrons are accelerated radially by
the high electric field. The orthogonal magnetic field swerves these clouds of
electrons, and causes them to turn in a spiral motion in time with the magnetic
field. The electrons surrender energy to the resonant cavities. All the anode
cavities are coupled, and energy is extracted from all of them. This energy is
radiated by a circular loop antenna through the waveguide towards the resonant
cavity (Copson, 1975; Regier and Schubert, 2005).
Figure 2.7. Schematic representation of a magnetron and the motion of electrons
inside (Anonymous, 2007b)
33
2.2.2.3. The waveguide
The waveguide is used to convey the microwave radiation from the
magnetron to the resonant cavity. A waveguide is a hollow conductor, which has a
rectangular or circular shape and a constant cross-section. The internal dimensions
of the waveguide define the minimal (cut-off) frequency below which the waves
do not propagate (Regier and Schubert, 2005).
2.2.2.4. The resonant cavity
A resonant cavity is also called an applicator. There are multi-mode and
mono-mode cavities. The simpler multi-mode cavity has a rectangular shape.
When microwaves enter a multi-mode cavity, the cavity’s walls reflect the
microwaves and cause them to interfere, thus creating multiple modes of
propagation. Multi-mode cavities are best suited for very lossy (high tanδ values),
large loads that occupy more than 50% of the cavity volume. Non-uniform
heating and hot-spots may occur if the load’s loss is low to medium, and the
volume of the load is less than 20% of the cavity volume (Orsat et al., 2005).
Multi-mode cavities are similar to domestic microwave ovens. A mode stirrer
ensures homogeneous microwave field distribution (Kappe and Stadler, 2005a).
Therefore, some researchers do not recommend multi-mode cavities for research
purposes, as the spatial distribution of the field strength inside the cavity is
unknown. A more efficient cavity, and suitable for research purposes, is the
mono-mode (focused) cavity (Orsat et al., 2005). Here, the microwave radiation is
directed to the reaction vessel through a precise waveguide of circular or
rectangular shape. There is only one mode of propagation and the spatial field
distribution is well defined. A standing wave is created due to the design of the
waveguide and cavity. The reaction vessel is placed at a fixed distance from the
magnetron where the field strength is at its maximum (Kappe and Stadler, 2005a).
Mono-mode cavities can have cylindrical or rectangular shapes (Orsat et al.,
2005).
34
Standing waves occur when the waves entering the cavity are reflected by
the metal walls and travel back toward the source. The wave traveling toward the
wall is called an incident wave, and the wave travelling back to the source is
called a reflected wave. The incident and reflected waves form a standing wave,
which attenuates as it travels along the direction of propagation. A standing wave
has nodes and antinodes. The maximum field amplitude of a standing wave occurs
where the waves travel in phase; this maximum is called an antinode. The
minimum field amplitude of a standing wave occurs when the incident and
reflected waves are opposite in phase; this minimum is called a node (Buffler,
1993; Decareau and Peterson, 1986). A standing wave is a resonant mode, from
where the name of mono-mode and multimode cavities. In a mono-mode cavity,
the reaction vessel is placed at an antinode. In multi-mode cavities, there are
multiple standing waves (resonant modes). It is believed that the cold spots occur
at the nodes and the hot spots occur at the antinodes. This is why the multimode
cavities have rotating turntables to insure homogeneous heating as much as
possible (Buffler, 1993; Decareau and Peterson, 1986; Orsat et al., 2005; Regier
and Schubert, 2005).
2.2.2.5. Microwave systems for chemistry
There are two types of microwave systems commercially available for
microwave-enhanced chemistry applications. These are closed-vessel systems,
operating at controlled temperature and pressure, and open-vessel systems,
operating under atmospheric conditions. It is strongly advised to use such
laboratory systems for chemical reactions, as domestic microwave ovens could
lead to hazardous situations in chemistry applications (Kaufmann and Christen,
2002). Moreover, in domestic microwave ovens the power is usually controlled by
on-off cycles of the magnetron, which makes it almost impossible to monitor the
temperature in a reliable way (Kappe and Stadler, 2005a).
Closed-vessel (multi-mode) microwave systems have Teflon reaction
vessels that are placed on a turntable to ensure homogeneous heating. Such
systems are suitable for digestions or acid mineralization, and extractions under
35
high temperature and pressure conditions. When operating under drastic
conditions, the reaction vessels must be cooled to room temperature before
opening (Kaufmann and Christen, 2002).
The open-vessel microwave systems are focused (mono-mode), they
operate at atmospheric pressure. The reactions take place under reflux conditions
in Teflon or glass-quartz extraction vessels that are fitted with a condenser.
Sample handling is safer in open-vessel than in closed-vessel systems as no
pressure builds up inside the vessel (Kaufmann and Christen, 2002).
2.2.3. Microwave heating attributes
Microwave heating is known to be volumetric in nature; this makes it
attractive by comparison with other heating methods. Conventional heating occurs
by conduction, convection, and radiation. The heat transfer through conduction
and convection is achieved by molecular collisions. The heat transfer through
radiation is achieved by absorption of energy quanta (Copson, 1975; Venkatesh
and Raghavan, 2004). As opposed to conventional heating, microwave heating
occurs by direct energy transfer to the material. This energy transfer is the result
of the interaction between the material and the electric component of the
electromagnetic field. Microwave heating is volumetric and is therefore not
dependent on transfer of heat from the material’s surfaces to the center. In fact, an
inverted temperature gradient occurs. When microwave radiation impinges on a
material in a microwave cavity, the material’s surface is colder than its center, as
the surface is in contact with the microwave-transparent surrounding environment
(air or vessel). This causes an inverted temperature gradient and some heat is lost
to the environment (Gardiol, 1984; Venkatesh and Raghavan, 2004). In
microwave-enhanced chemistry, volumetric heating results from microwave
coupling with the polar components in the reaction mixture. The microwave
reaction vessels are made of microwave-transparent materials such as Teflon or
glass-quartz. Thus, a polar reaction mixture in a non-polar reaction vessel causes
an inverted temperature gradient and some heat will be transferred from the
reaction mixture to the vessel (Figure 2.8) (Kappe, 2004).
36
The key point that makes microwave energy attractive for replacing
conventional heating in various processes requiring heating is exactly the
difference between the two. While conventional heating occurs through thermal
gradients, microwave heating occurs through direct microwave-matter interaction.
Microwave energy transfer is faster, has the potential to reduce processing time,
and may result in enhanced quality products. In general, reduced processing time
is associated with better quality products (Venkatesh and Raghavan, 2004).
Figure 2.8. Inverted temperature (degrees Kelvin) gradients in microwave
(left) versus oil-bath heating (right). The difference in temperature profiles is
shown after 1 min of heating. Source: (Kappe, 2004).
2.2.4. Microwave heating theory
The theory of electric field in not usually taught in chemistry and food
science classes but rather in engineering and physics classes. With the advent of
microwave technology in the laboratory and industry, the need arises for chemists
and food scientists to understand the basic physical principles behind microwave
heating and microwave-matter interaction (Kappe and Stadler, 2005b).
The interactions between electromagnetic radiation and materials occur at
microscopic and macroscopic scales. The microscopic scale interactions are
37
concerned with the energy bands of electrons, and the magnetic moments of the
atoms and molecules. The macroscopic scale interactions are concerned with the
overall response of the materials to the external electromagnetic field. The
macroscopic microwave-matter interactions are described by two complex
parameters: permittivity *, and permeability µ*, which describe material’s
interaction with the electric field, and magnetic field, respectively (Chen et al.,
2004). The permeability aspect is rather negligible in the case of agri-food
materials. Foods contain only traces of magnetic materials such as iron, nickel,
and cobalt. Therefore, they do not interact with the magnetic component of the
electromagnetic field (Mudgett, 1989; Venkatesh and Raghavan, 2004).
The macroscopic scale interactions are of interest when microwave energy
is applied in chemical reactions and food processing. It is important for the
scientist to understand the dielectric properties of materials, and their interaction
with microwave radiation (Gabriel et al., 1998; Kappe, 2004; Kappe and Stadler,
2005b; Kappe and Stadler, 2005c; Meda et al., 2005; Orsat et al., 2005; Venkatesh
and Raghavan, 2004).
Microscopic microwave-matter interaction can be described by Maxwell’s
equations. The material’s response to microwaves is governed by the complex
permittivity *, complex permeability µ*, and conductivity σ. These parameters
also determine the microwave penetration in a material at a given frequency
(Chen et al., 2004).
2.2.4.1. Microwave heating – a form of dielectric heating
Dielectric heating includes induction, capacitive, and microwave heating.
Induction heating produces heat in a conductor by inducing eddy currents
(magnetic coupling), which supply the heat by the resistive component (I2R) of
the inductor. Induction heating takes place in the frequency range 960 Hz – 1
MHz. Capacitive or radio-frequency heating occurs by dipole polarization in poor
electrical conductors in the frequency range 1 MHz – 300 MHz. Capacitive
heating is also called dielectric heating as it occurs through dipole losses in
38
dielectric materials (non-conductors). In practice, capacitive heating occurs
between the plates of a condenser (capacitor). Microwave heating is also dielectric
heating as it occurs by polarization in non-conductors. The difference between the
two types of dielectric heating is that microwave heating takes place in the
frequency range 300 MHz – 300 GHz, and the load is placed in a microwave
cavity (Copson, 1975; Decareau and Mudgett, 1985).
Microwave heating is often explained by analogy with heating in a
condenser (Chen et al., 2004; Copson, 1975). Microwave heating has similarities
with the heating of a dielectric material in a condenser, with the difference that the
load is placed inside a resonant cavity where it directly interacts with microwave
radiation. Microwave heating occurs mainly by absorptive polarization and ohmic
losses. The microwave radiation loses some, or all of its energy to the dielectric
material. From the point of view of electrical engineers, microwave heating is
“energy dissipation in a lossy media” (Copson, 1975). This energy dissipation is
governed by the dielectric properties of the material: the dielectric constant ( ε’),
the dielectric loss (ε”), and the loss tangent (tanδ). The dielectric constant is a
measure of the ability of the dielectric material to be polarized by an external
oscillating electric field. It indicates the resistance opposed by the dielectric to the
movement of polar molecules and ionic species within. The dielectric loss is a
measure of the ability of the dielectric material to convert microwave energy into
heat. The loss tangent is also called the dissipation factor, and is a measure of the
material’s heating ability. The word loss in this terminology refers to the
absorption of radiation within the dielectric material (Meda et al., 2005; Orsat et
al., 2005; Venkatesh and Raghavan, 2004). A dielectric material is an insulator
able to withstand an electric field and to be polarized by it (Copson, 1975).
2.2.4.2. Microwave heating explained by analogy with dielectric heating
in a condenser
A condenser with capacitance C0 is connected to an AC voltage source U,
and has a charge storage Q=C0U. The current flowing in the condenser circuit is
I=jC0ωU, where ω is the angular frequency and j=√ 1. In this case, when no
39
dielectric is present between the electrode plates of the condenser, the current I
leads the voltage U by a phase angle of 90º and no heating occurs (Figure 2.9)
(Chen et al., 2004).
Figure 2.9. Current and voltage in a condenser with capacitance C0,
Source: Chen et al. (2004)
A condenser, with a dielectric material inserted between the electrode
plates is schematically represented in Figure 2.10. Here, the total current I is the
sum of the charging current Ic and loss current Il as presented in Equation 1.
[1]
Where I = total current
Ic = charging current
Il = loss current
j = √ 1
ω = angular frequency
U = voltage
C = capacitance of the condenser containing the dielectric material
G = conductance of the dielectric material
40
Figure 2.10. Condenser with a dielectric load placed between two electrode
plates. The charges within the dielectric load align with the oscillating electric
field. Adapted from Copson (1975).
The material (load) has dielectric strength. It can withstand the potential
difference between the plates without breakdown as the voltage increases. As the
polarity of the electrodes reverses, the dipoles in the load experience a torque as
they realign. Some energy is lost across the electrodes resulting in heating of the
load (Copson, 1975). As presented in Figure 2.11, the loss current Il is in phase
with the voltage source U, and represents an ohmic loss. The charging current Ic
and the loss current Il are measures of the absorptive polarization that occurs in
the condenser, which is the energy transfer from the electric field to the dielectric
material manifested as heat. There are two angles involved; θ is the power factor
angle, and δ is the loss angle (Chen et al., 2004; Copson, 1975).
The charged plates of a condenser affect each other according to the
distance between them, and the amount of charge. The capacitance increases due
to the added effect of close charges as the distance between the plates decreases.
At fixed distance, the capacitance is affected by the material in the condenser.
41
Figure 2.11. Charging current and loss current in a condenser containing a
dielectric material, Source: Chen et al. (2004)
If a dielectric is inserted between the electrode plates, the capacitance
increases from (the capacitance of air) to (Equation 2). The capacitance of
the dielectric material is related to the dielectric constant ’. The relative dielectric
constant influences the amount of charge. Therefore, the complex permittivity
* = ’-j ’’ can be used to describe the behaviour of the dielectric material
under the influence of an electric field (Chen et al., 2004; Copson, 1975). The
complex permittivity has real and imaginary components. The real component is
the permittivity ’, also called the dielectric constant. It describes the ability of a
material to be polarized by the electric field. The imaginary component is the
dielectric loss ”. It measures the lag in dipole orientation behind the electric field.
Therefore, it describes the efficiency with which microwave energy can be
converted into heat (Copson, 1975; Stuerga and Delmotte, 2002).
[2]
Where C = the capacitance of the condenser containing a dielectric
C0 = the capacitance of air
’r = the relative dielectric constant
* = the complex permittivity
42
0 = the permittivity of free space (8.854×10-12F/m)
’ = the dielectric constant
’’ = the dielectric loss
The increased capacitance is explained by the polarization in the dielectric
material. Polarization refers to the orientation of dipoles in the dielectric material
under the influence of an alternating electric field. The dipoles orient with their
positive charges closer to the negative electrode. This leads to an increased flow
of current as the positive ends of dipoles attract a negative charge on the electrode
plate. An excess of electrons is accumulated on the plate which induces a positive
charge on the opposite plate by repelling the electrons (Copson, 1975).
By replacing C= in the equation Ic , the charging
current in a condenser containing a dielectric is obtained.
[3]
Therefore,
[4]
Where, J is the current density across the condenser under the applied field
strength E (Figure 2.12).
According to Figure 2.12, the energy dissipation of a dielectric material is
described by the loss tangent (Equation 5), and the dielectric power factor
(Equation 6) (Chen et al., 2004).
[5]
[6]
Where δ = loss angle
43
θ = power factor angle
The dielectric conductivity accounts for all the
dissipative effects in the dielectric material. It shows that the energy transfer can
occur by ohmic, dielectric constant, or polarization effects (Chen et al., 2004;
Copson, 1975).
Figure 2.12. The charging current density and the loss current density in a
condenser containing a dielectric, Source: Chen et al. (2004)
2.2.4.3. Microwave interaction with dielectric materials
The physical origin of microwave energy conversion into heat resides in
the material’s ability to be polarized by the electric component of the electro-
magnetic field. When polarization lags behind the rapid reversals of the electric
field, energy is lost in the form of heat into the material. The loss tangent,
tanδ= ”/ ’, is a measure of the conversion of microwave energy into heat. The
interactions between microwaves and materials are quantified by the complex
dielectric permittivity * and the complex permeability µ* (Stuerga and Delmotte,
2002). For materials containing only traces of magnetic minerals the interaction
with the magnetic field is negligible (Mudgett, 1989). Therefore, the magnetic
permeability of these materials µ’ is usually taken as 1 in calculations (Meda et
al., 2005).
44
The total energy loss occurring at microwave frequencies includes the
dielectric loss and the conductive (ohmic) loss (Equation 7) (Meda et al., 2005;
Will et al., 2004).
[7]
Where t” = total loss
” = dielectric loss
σ = electric conductivity [S]
ω = angular frequency [rad. /sec.]
0 = free space permittivity
When microwave radiation impinges on a material, part of this radiation is
reflected by the material, some radiation is transmitted through the material, and
only a part of the transmitted energy is absorbed as heat. The amounts of
reflected, transmitted and absorbed energy are quantified in terms of the dielectric
properties of the load (Venkatesh and Raghavan, 2004).
The proportion of reflected energy is a function of the dielectric constant
and the angle of incidence (Equation 8). However, in a multi-mode cavity the
energy impinges from multiple angles. Moreover, the energy reflected by the load
is reflected back by the cavity walls and impinges all over again on the material
thus increasing the amount of absorbed energy (Venkatesh and Raghavan, 2004).
90 √
√ [8]
Where Pr (90) = reflected energy for an angle of 90º
’ = dielectric constant
The power transmitted through the material is Ptrans =(1- Pr) (Venkatesh
and Raghavan, 2004). The power dissipated per unit volume is determined by
45
three factors: one fixed – the frequency, one determined by the load – the loss
factor, and one determined by the microwave cavity – the electric field inside the
load (Equation 9) (Buffler, 1993; Gardiol, 1984; Meda et al., 2005; Venkatesh and
Raghavan, 2004).
2 [9]
Where Pv = power absorbed per unit volume [W/m3]
ν = frequency [Hz]
0 = permittivity of free space
” = dielectric loss factor
E = electric field strength inside the load [V/m]
The electric field strength inside the load is determined by the oven
configuration, the dielectric and the geometric properties of the load. The
interaction between these three factors contributes to the complexity of the
electric field distribution inside the load, which in turn makes it difficult to
determine the microwave heating characteristics of materials (Buffler, 1993).
The volumetric nature of microwave heating is influenced by the
penetration depth, which in turn is influenced by frequency. In general, the
penetration depth increases as the microwave frequency decreases (Liao et al.,
2001b). The penetration depth of microwave is infinite in microwave-transparent
materials, is zero in reflective materials (metals) and has a finite value in lossy
materials. Usually, the penetration depth is calculated for microwaves generated
from one direction only, i.e. a plane wave (Copson, 1975).
The penetration depth (Equation 10) is defined as the depth into a sample
where the incident power drops to 1/e or 36.8% of its initial value (Copson, 1975;
Meda et al., 2005; Venkatesh and Raghavan, 2004).
√ [10]
46
Where Dp = microwave penetration depth into the sample [cm]
λ0 = free space microwave wavelength for a given frequency [cm]
’ = dielectric constant
” = dielectric loss
2.2.4.3.a. Mechanisms of interaction
The interaction between an external oscillating electric field and the
charged molecules within a material result in two major effects: ionic conduction
and dipolar polarization (Meda et al., 2005).
Ionic conduction occurs when the charged particles, usually ions, are free
to travel through the material. They oscillate under the influence of an external
electric field and collide with other species. This motion results in heating through
ohmic losses. The contribution of ionic conduction to the total loss equation is
described by the term . Generally, the ionic conduction increases with the
temperature (Buffler, 1993; Meda et al., 2005).
Dipolar polarization occurs when the charged particles within the material
are not free to move. A dipole has positive and negative centers of charge,
separated by a distance. The strength of separated charges is defined as the dipole
moment, which is a measure of the dielectric constant (Buffler, 1993). When an
external electric field impinges on a dipole, a dipole momentum is induced in it. A
molecule with two atoms has dipole moment only if the atoms are not equal. For
example, the molecules of water, hydrochloric acid, and carbon monoxide have
dipole moments. The molecules of hydrogen, oxygen and chlorine do not have
dipole moments. Some dipoles are permanent, that is, they exist even in the
absence of an external electric field. Water molecules have permanent dipoles.
The shape of a water molecule is that of a symmetrical triangle with an angle of
about 105º between the two oxygen-hydrogen bonds. The oxygen nucleus is the
47
center of negative charge and attracts the hydrogen electrons, thus making the
hydrogen nucleus center of positive charge (Copson, 1975).
A point of maximum polarization occurs when all the dipoles align in the
oscillating electric field. However, in microwave heating of materials, the
maximum polarization is prevented by the simultaneous effects of mobility,
thermal agitation, Brownian movement, and collisions. Polarization is dependent
on the frequency of the electromagnetic radiation, the size of polar molecules, and
the viscosity or solidity of the material. These factors cause the polarization to lag
behind the applied oscillating electromagnetic radiation (Copson, 1975).
The dielectric relaxation time is the delay in dipole response to the
oscillating field. The dielectric relaxation time is the basis for dielectric loss. The
dielectric loss involves the dissipation of energy by elastic distortions,
deformations, and displacements. The relaxation time depends on the viscosity,
which depends on temperature. Therefore, the energy loss is temperature
dependent. The relaxation time τ is in the region 10-6 to 10-15 sec (Copson, 1975).
Polarization is dependent on frequency. The dipole accumulation and the
dipole moment induced by the electromagnetic field determine the dielectric
constant. As the frequency decreases, the dielectric constant remains the same or
decreases, but the absorption passes through a maximum. The maximum
absorption is related to relaxation time and it occurs when the angular frequency
is equal to the inverse of relaxation time (Equation 11). This means that the loss
tangent is at its maximum value.
[11]
Where τ = relaxation time [sec]
ω = 2πν, is the angular frequency [rad/sec]
48
2.2.5. Microwave effects
The issue of microwave effects is very controversial. Some of the
microwave heating attributes such as rapid and uniform heating, inverse
temperature gradients, and selective heating, are mistakenly included in the
category of microwave effects. Only those effects that cannot be predicted or
explained based on the actual understanding of microwave heating should be
called microwave effects (Clark and Sutton, 1996). Most scientists agree today
that the enhanced rates achieved in microwave-assisted chemical reactions are
purely thermal/kinetic effects (Kappe, 2004).
2.2.6. Microwave-assisted extraction (MAE)
There is an increasing demand for new extraction techniques that are
environmentally friendly, faster than the conventional methods, and more efficient
in terms of extraction yield and quality of extracts. MAE techniques have been
developed recently for nutraceutical extraction from plants (Kaufmann and
Christen, 2002; Wang and Weller, 2006). Microwave heating represents an
alternative to conventional heating for inducing energy into reaction media, as it
results in reduced extraction time, and enhanced quality extracts (Orsat et al.,
2005).
The microwave-assisted process (MAPTM) is a relatively new series of
patented technologies related to MAE (Paré and Bélanger, 1997). This technology
can be used for liquid-phase extraction of a wide range of materials by using
microwave radiation. The holders of the patents claimed that MAP supports
sustainable development, as opposed to traditional methods it uses less energy and
smaller volumes of solvents, thus producing smaller volumes of chemical waste.
MAP has potential for application in analytical chemistry, as by comparison with
traditional methods it significantly reduces the sample preparation time, and has
enhanced analytical capabilities. MAP is patented for 2450MHz, and uses
relatively microwave transparent solvents. The physical principle behind its
49
functioning is based on the dielectric properties of samples and solvents, and their
interaction with the microwave radiation (Paré and Bélanger, 1997).
2.2.6.1. MAE of compounds from plant matrices
Two recent review papers, discussing novel techniques for extraction of
nutraceuticals or other natural ingredients from plants, present the MAE as an
efficient extraction technique (Kaufmann and Christen, 2002; Wang and Weller,
2006).
Wang and Weller, (2006) discussed the efficiency, advantages and
disadvantages of the following methods: Soxhlet, sonication, MAE, supercritical
fluid extraction, and accelerated solvent extraction. Soxhlet extraction is a well-
established method, which is usually used for the evaluation of the efficiency of
novel extraction methods. However, it is known to affect negatively the extraction
yields of thermo-labile compounds. It is also very time consuming and uses
considerable volumes of solvents. Sonication uses sound waves with frequencies
above 20 kHz. This technique may promote cell disruption through the expansion
and compression of molecules in the reaction medium. Sonication extraction
generally gives high extraction yields and is suitable for thermo-labile
compounds. However, a number of factors including the sound wave attenuation
influence its performance. Microwave-assisted extraction is a valuable alternative
to traditional extraction techniques. Some of MAE advantages over the latter
include improved extraction yields and reduced extraction times. Supercritical
fluid extraction promotes high mass transfer in the reaction medium by decreasing
the viscosity and surface tension of the solvent, therefore increasing its ability to
dissolve the target components. However, it is very expensive to operate such a
system. Accelerated solvent extraction is performed at high temperature and
pressure levels, usually, between 50 and 200˚C and 10 to 15 MPa. This method
offers an alternative to supercritical solvent extraction. It is environmentally
friendly as it can use carbon dioxide and water as solvents for extraction of polar
compounds (Wang and Weller, 2006).
50
Kaufmann and Christen, (2002) reviewed literature on MAE and
accelerated solvent extraction of natural compounds from plants. The authors
classified the literature on MAE in two parts dealing with closed-vessel and open-
vessel methods. However, a good part of the research included in the closed-
vessel category was actually performed using domestic microwave ovens. For
example, the pioneering work done by Ganzler and co-workers in 1986 and 1987
was included in this category. In addition, it is well known that even if the
domestic microwave ovens are multimode cavities, there are some characteristics
that differentiate them from the dedicated laboratory multimode (closed-vessel)
microwave systems. The variations in power in a domestic microwave oven are
achieved by automatically turning the magnetron on and off, by using, the so-
called duty cycles. For example, the power output can be averaged to half the
nominal power by using duty cycles of 15 sec on / 15 sec off. This is an important
aspect to be considered when adapting domestic microwave ovens for chemical
applications. Moreover, when the reaction mixture is present in small amounts or
its capacity to absorb microwaves is poor, the multi-mode cavity is not effective
anymore, and a mono-mode cavity should be used instead (Mingos and Baghurst,
1991). Also, the domestic microwave ovens have low-cost magnetrons which
produce an inhomogeneous microwave field (Kappe and Stadler, 2005a).
2.2.6.1.a. Pioneering work in MAE
Pioneering work in microwave-assisted extraction was done in 1986 and
1987 by Ganzler and co-workers. In spite of the lack of control over the process,
when using a domestic microwave oven, the results testified in the favour of
replacing the conventional heat sources by microwave irradiation.
To acknowledge the undeniable merit of the pioneering work, and its
research value to the field, the MAE methods developed by Ganzler and co-
workers are briefly presented below. The authors used a domestic microwave
oven, with a nominal power of 1140 W operating at 2450 MHz, to study the
applicability of microwave energy for the extraction of compounds from complex
matrices. An intermittent microwave irradiation method was used in order to
51
prevent the boiling of extraction mixtures. Consecutive irradiation (30 sec) and
cooling (2-3 min at room temperature) steps were repeated up to seven times in
order to determine the best conditions for maximum compound recovery. Two
and three irradiation steps were necessary for maximum recovery of vicine, and
convicine respectively, from fava beans. Maximum recovery of gossypol from
cottonseeds was achieved in three irradiation steps. Two irradiation steps gave the
best recovery of organophosphate pesticides from soil samples. The comparison
of results obtained with MAE and traditional methods showed that MAE was
more effective than the traditional methods and reduced the extraction time.
Moreover, with the traditional methods, such as Soxhlet and shaken-flask,
degradation of compounds occurred, most likely due to the increased temperature
and long extraction time profiles. MAE is fast and prevents the denaturation of
thermo-labile components. It is therefore, recommended for fast and reliable
sample preparation for chromatography (Ganzler and Salgo, 1987; Ganzler et al.,
1986).
The same irradiation technique, using a domestic microwave oven, was
used for extraction of sparteine from lupine. By comparison with traditional
methods, MAE achieved a 20% higher extraction yield. Preparation of sample is a
critical step in chemical analysis. Although, MAE is highly efficient, it should be
reproducible, since it was observed that the homogeneity of the microwave field
inside the cavity affects this aspect (Ganzler et al., 1990). Reproducibility along
with reliability and safety aspects are the reasons for which more recent
publications recommend the use of laboratory microwave systems for chemical
reactions (Kappe and Stadler, 2005a; Kaufmann and Christen, 2002) and
especially of mono-mode cavities (Kappe and Stadler, 2005a; Mingos and
Baghurst, 1991; Raghavan et al., 2005).
2.2.6.1.b. MAE of natural compounds using dedicated laboratory
microwave systems
The dedicated laboratory microwave systems suitable for MAE processes
function at 2450 MHz frequency. Two well-known manufacturing companies are
52
the CEM Corporation, based at Matthews North Carolina (www.cem.com), and
Milestone Inc with headquarters at Shelton, Connecticut
(www.milestonesci.com).
All the MAE methods presented below were carried out in commercial
dedicated laboratory microwave systems operating at 2450 MHz emission
frequency.
2.2.6.1.b.a. MAE using open-vessel microwave systems
Azadirachtin-related limonoids Dai et al. (1999) extracted azadirachtin-
related limonoids from ground defatted neem seeds. The MAE experiments were
carried out in a Synthewave 402 (Prolabo), at 150W. The microwave power was
intermittently turned on and off in sequences of 30 seconds. The MAE method
was compared with conventional extraction under reflux conditions and extraction
at room temperature with stirring. All the extractions were carried out for 10 min,
using 2 g sample and 50 ml ethanol. The comparison study showed that the three
extraction methods performed equally well (Dai et al., 1999).
In 2001, Dai and co-workers re-applied the methods presented above with
some modifications. The sample size and solvent volumes were halved, and the
extraction time was doubled (20 min). In addition to the previous study, the
authors studied the effect of matrix on the extraction of azadirachtin-related
limonoids. The interaction microwave-matrix had a significant influence on the
extraction yield. The comparison with the conventional methods revealed that
MAE had the highest limonoids recovery when extracting the seed shell, leaf, and
leaf stem, and the lowest recovery when extracting the seeds. The authors also
investigated the effect of solvent type (methanol, dichloromethane, and petroleum
ether) on the recovery of limonoids. Among the three solvents, methanol was
preferred as it had both good dissolving and microwave absorbing capabilities
(Dai et al., 2001).
Ginger oil Alfaro et al. (2003) used a focused Soxwave-100 (Prolabo) to
extract the essential oil from ground ginger root. The solvent type, applied power
53
and irradiation time along with sample characteristics affected the extraction
yield. Five grams of sample were extracted with 30 ml of solvent (ethanol or
hexane). The MAE was performed at combinations of power-extraction time of
150W with 60 and 120 s and 300 W with 30 and 60 s. The efficiency of MAE
method was assessed by comparison with 2-hours Soxhlet extraction method. The
MAE using ethanol at 300W for 30 s gave higher extraction yield than the Soxhlet
(Alfaro et al., 2003). The authors claimed that the operational parameters of the
MAE methodology were optimized. However, they failed to present any statistical
procedures for optimization, and statistical analysis of the data was missing
throughout the paper. It appears that any inferences drawn were solely based on
simple interpretation of data presented in tables and graphics. Considering all
these, the authors obtained some interesting results. MAE could be successfully
applied for the extraction of oil from ginger root in only 30 s as opposed to 2-
hours extraction with the Soxhlet method.
Cocaine Brachet et al. (2002) used a Soxwave 3.6 operating at a
maximum power of 250W to extract cocaine from coca leaves. The authors
investigated the effect of solvent, sample particle size, use of extraction cartridge,
and sample modifier on the MAE of cocaine. The effect of each factor was
assessed one at a time by using one-way ANOVA analyses. The experiments were
conducted at a fixed combination of power (75W), extraction time (20 min), and
particle size (90-150 µm). The effects of polar and non-polar solvents were
investigated. While the polar solvents were effective, and methanol gave the
highest extraction yield, the non-polar solvents were not suitable for MAE of
cocaine. Particle sizes in the range 90-470 µm were tested. The effect of particle
size significantly affected the extraction yield. The smaller the particle size, the
higher the extraction yield. The authors tried to eliminate the filtration step after
MAE by placing the sample into an extraction cartridge prior to the irradiation.
The results showed that using a cartridge is not recommendable as it retains an
important quantity of extract. Interesting results were obtained by modifying the
dielectric properties of the sample. The addition of 100 µl of water to 100 mg
sample prior to the extraction with methanol further improved the extraction
54
yield. A central composite design was used to find the optimum levels for the
factors microwave power (25 to 225 W), and extraction time (0.5 to 15.5 min).
The main factors and their interaction were found to have a negligible effect on
MAE of cocaine. Therefore, the best extraction of cocaine was obtained for 100
mg of moistened samples extracted with 5 ml of methanol for 30 s at 125 W
(Brachet et al., 2002). In conclusion, the dielectric properties of both sample and
solvent, and the sample particle size were more influential than the applied
microwave power or the irradiation time. However, the optimization study was
necessary as it permitted the reduction of the extraction time to only 30 s.
Although the choice of very simple experimental designs for assessing the effect
of factors one at a time seems correct, it has the main drawback that it does not
allow for assessment of factors interactions. It is possible that a decision based on
a standard central composite design including all factors would be different.
However, the study presented by Brachet and co-workers has the merit of
proposing a fast and attractive method for MAE of cocaine. Nonetheless, the
performance of the new MAE method as opposed to traditional methods is
unknown.
Cocoa fat ElKhori et al. (2007) used a Soxwave 3.6 (Prolabo) to extract
the fat from cocoa powder and cocoa nibs. They used the operating principle
patented as a MAP process in which the solvent is relatively microwave
transparent (hexane) and the sample has higher dielectric properties. The
dielectric properties of the sample were enhanced by addition of 1 ml of water to
5 g of homogenised cocoa powder or cocoa nibs. This resulted in extraction yields
comparable with those obtained by conventional AOAC method. Moreover, the
fatty acids profiles of extracts obtained with both methods were similar. The fat
from cocoa powder was extracted in two stages for a total extraction time of 9.5
min. First, 5g sample moisturized with 1 ml water was extracted with 30 ml
hexane at 250 W for 1 min followed by 2 min power off. Then, 10 ml isopropanol
were added to the reaction mixture, which was shaken for 30 s, then the reaction
mixture was irradiated at 25 W for 6 min (ElKhori et al., 2007). While this
method is short, the method for extracting fat for cocoa nibs is lengthy, with 10
55
min irradiation period followed by drying in the oven over night, and then re-
extraction under microwave radiation. The interpretation of results was done
based on simple assessment of data presented in tables, as no statistical was
reported. However, the results of ElKhori and co-workers showed that using
relatively microwave transparent solvents during MAE gives good results for
samples that strongly absorb microwave radiation.
Capsaicinoids Williams et al. (2004) used Synthewave 402 to extract
capsaicinoids from Thai red chilli peppers. The effects of various factors on the
extraction yields were investigated one at a time by extracting 2 g sample with 20
ml acetone at 150 W for 3 min. Comparison of extraction yields obtained with
hexane, methanol, acetone, and methylene chloride showed that acetone is
suitable for MAE of capsaicinoids. The effect of chopping the peppers in smaller
or bigger pieces was investigated by extraction with acetone and it was found
negligible. The comparison of capsaicinoids yields obtained by MAE (25 min),
shaken flask (24 h), and reflux (2 h) methods showed that MAE (0.48mg/g
pepper) performed slightly better than the shaken flask method (0.31mg/g
pepper), and significantly better than the reflux method (0.22 mg/g pepper)
(Williams et al., 2004). The authors stated that 7 min of irradiation at 30 W were
the optimum operation parameters.
2.2.6.1.b.b. MAE using closed-vessel microwave systems
Ginsenosides Yang et al. (2004) used a MSP-100 (Beijing Rayme
SciTech Co. Ltd.) with a maximum power output of 850 W, to extract
ginsenosides from ginseng roots. The extraction yields obtained with MAE,
Soxhlet and ultrasonic extraction methods were comparable, but it took only 3
min with MAE, while Soxhlet needed 3 hours and ultrasonic extraction needed 1
hour for maximum compound recovery. The authors observed that a mixture of
ethanol-water (50%, v/v) gives better extraction yields than pure ethanol when
using MAE. In addition, the higher the volume of solvent (investigated range 10
to 50 ml) for 1 g sample, the higher the extraction yield (Yang et al., 2004). The
enhancing effects of alcohol-water mixtures observed by Yang and co-workers
56
are confirmed by earlier findings showing that at microwave frequencies, the
mixtures of water and alcohol show synergistic loss behaviour. That is, the
dielectric loss of the mixture is significantly higher than the dielectric loss of
either pure water or alcohol. This synergistic behaviour is attributed to the
stabilization effect of the hydrogen bonding on the mixture structure (Decareau
and Mudgett, 1985). It appears that the evaluation of MAE efficiency was based
on simple assessment of data from tables and graphics, as no statistical analysis
was presented.
Coumarin and its related compounds Martino et al. (2006) used a
focused (mono-mode) Discover Labmate (CEM) to extract coumarins and its
related compounds from dried flowers of sweet yellow clover herb. Samples of
0.2 g were extracted with 4 ml of 50% aqueous ethanol at 100 W. The authors
used a temperature control program, for 50 and 110˚C. The reaction mixtures
were irradiated in one or two sequences of 5 min. power on, followed by a cooling
step (time unspecified). The extraction yields in mg/g sample of coumarin, o-
coumaric acid, and melilotic acid were compared for MAE, ultra-sound assisted
extraction and Soxhlet extraction. The highest extraction yields were obtained
with MAE in two sequences of irradiation (coumarin 3.98 mg/g, o-coumaric acid
1.26 mg/g, melilotic acid 9.05 mg/g). The results obtained with MAE were
comparable with those obtained by ultrasound-assisted extraction, and were
significantly higher than those obtained by Soxhlet extraction (Martino et al.,
2006). The authors reported that the best results were obtained at 50˚C in 2
irradiation sequences of 5 min followed by cooling steps. However, there is no
mention about the duration of the cooling steps; therefore, a total extraction time
cannot be estimated.
Taxanes Mattina et al. (1997) used a MES-1000 (CEM) to extract
taxanes from Taxus biomass. The authors reported 90% taxanes recovery with
MAE. Samples of 5 g fresh Taxus needles were pre-soaked with 5ml water, and
then extracted with 10 ml of 95% aqueous ethanol for 9 min:10 sec at 85˚C at
100% power (950W). The efficiency of the MAE methods was comparable with
57
that of traditional methods (Mattina et al., 1997). The reported extraction time of
9 min: 10 sec represents only the fraction of time for which the microwave system
was programmed to maintain the target temperature. The system was programmed
as follows: 2 min:20 sec ramp to temperature, 6 min hold, 30 sec ramp, and 3 min:
10 sec hold, which gives in fact a total of 12 min. Prior to the MAE the sample
was soaked in solvent for 10 min, which brings the extraction time up to 22 min.
Moreover, in the case of closed-vessel systems the vessels have to cool down
before opening, therefore a more realistic total extraction time cannot be estimated
here.
2.2.6.2. MAE of natural compounds - General Observations
The technique of microwave-assisted extraction is relatively new,
considering that the first reports on its application for the extraction of a variety of
matrices date only to 1986-1987. Consequently, since then, researchers have been
applying microwave power to a variety of analytical processes, causing the
microwave-assisted chemistry to reach a relative maturity. The application of
microwave power in the extraction of natural compounds has a great potential in
reducing the sample preparation time, improving the quality of extracts, and
enhancing the extraction yields. Therefore, the MAE technique appears as an
attractive alternative to the time-consuming traditional (conventional) extraction
methods.
There were some negative aspects encountered in the literature. It was
observed that some researchers have a strong preference in reporting methods
development and optimization when in fact they hardly used any experimental
designs to support such claims. Often experiments were carried out in a random
fashion, and statistical analysis of data was missing. This makes the interpretation
of results difficult and gives place to unsupported claims. Very often, the MAE
methods are not completely reported. When crucial information is missing, it is
quite difficult to recreate in the laboratory the so-called successful method
reported in the literature. Another problem encountered is the mitigation of time.
This happens when complicated extraction procedures are used, or the microwave
58
oven is programmed to work with power on / power off sequences, or at different
temperature or power levels.
2.2.7. Conclusions
Microwave is electromagnetic radiation with a frequency range of 300
MHz to 300 GHz (Gardiol, 1984). The transfer of energy from microwave
radiation to materials occurs in discrete units called quanta or photons (Gardiol,
1984; Rosenthal, 1992). The energy of the microwave photon at 2450 MHz is
about 1×10-5eV (Will et al., 2004). Therefore, it is not high enough to initialize
constructive or destructive reaction steps (Gardiol, 1984; Kappe, 2004; Rosenthal,
1992; Stuerga and Delmotte, 2002; Will et al., 2004). The industrial, scientific and
medical (ISM) microwave systems operate at 915 MHz and 2450 MHz emission
frequencies (Orsat et al., 2005; Raghavan et al., 2005; Venkatesh and Raghavan,
2004). The limit of exposure to microwave radiation for the individuals operating
microwave systems is 1 mW/cm2 of body surface (Regier and Schubert, 2005).
Microwave applications are carried out in microwave systems composed
of resonant cavity (oven), magnetron, wave-guide transmission and in some cases
mode stirrers (Orsat et al., 2005). The laboratory dedicated microwave systems
can be classified in mono-mode cavities (open-vessel and closed-vessel), and
multi-mode cavities (closed-vessel).
Microwave heating is volumetric in nature. It occurs by direct energy
transfer from the microwave radiation to the materials (Copson, 1975; Venkatesh
and Raghavan, 2004). The energy transfer results from the coupling of the
electrical component of the electromagnetic field with the polar components in the
material. As opposed to conventional heating, microwave heating is faster, has the
potential to reduce the processing time, and may result in enhanced quality
products (Orsat et al., 2005; Venkatesh and Raghavan, 2004).
Generally, the interactions between microwave and materials are described
by the complex permittivity ε*=ε’-jε”, and complex permeability µ* of materials
(Chen et al., 2004). However, agri-foods materials contain only traces of magnetic
59
minerals (Mudgett, 1989). Therefore, they do not interact with the magnetic field,
and their complex permeability µ* is usually taken as 1 in calculations (Meda et
al., 2005; Venkatesh and Raghavan, 2004).
Microwave heating is dielectric heating as it occurs by polarization in non-
conductors (Copson, 1975; Decareau and Mudgett, 1985). It is “energy
dissipation in a lossy media”(Copson, 1975) that occurs mainly by absorptive
(dipolar) polarization and ohmic losses (ionic conduction). Microwave heating is
governed by the dielectric properties of the material, namely, the dielectric
constant ε’, the dielectric loss ε”, and the loss tangent tanδ. The dielectric constant
is the real component of the complex dielectric permittivity, is a measure of the
dielectric material’s ability to be polarised by an oscillating electric field. It
indicates the resistance opposed by the dielectric to the movement of polar
molecules and ionic species within. The dielectric loss is the imaginary
component of the complex dielectric permittivity; it measures the lag in the dipole
orientation behind the electric field. Therefore, it describes the efficiency with
which the microwave energy can be converted into heat. The loss tangent,
tanδ=ε”/ε’, also called the dissipation factor, is a measure of the material’s heating
ability. The word loss in this terminology refers to the absorption of microwave
energy within the dielectric material (Copson, 1975; Meda et al., 2005; Orsat et
al., 2005; Stuerga and Delmotte, 2002; Venkatesh and Raghavan, 2004).
Microwave heating is often explained by analogy with heating in a condenser
(Chen et al., 2004; Copson, 1975). The total energy loss occurring at microwave
frequencies includes the dielectric loss and the conductive (ohmic) loss (Meda et
al., 2005; Will et al., 2004). When microwave radiation impinges on a dielectric
material, a fraction of the incident energy is reflected and another fraction is
transmitted through the material, and finally part of the transmitted energy is
absorbed as heat. The volumetric nature of the microwave heating is influenced
by the penetration depth, which is a function of frequency. In general, the lower
the frequency, the higher the penetration depth (Venkatesh and Raghavan, 2004).
60
There is an increased demand for new extraction techniques that are
environmentally friendly, faster, and more efficient than the conventional
extraction methods (Kaufmann and Christen, 2002; Wang and Weller, 2006).
Microwave heating represents an alternative to conventional heating for inducing
energy into reaction media, as it has the potential to reduce the extraction times,
and to maintain the quality of bioactive compounds in the extracts (Orsat et al.,
2005). The physical principle behind MAE’s functioning is based on the dielectric
properties of samples and solvents and their interaction with the microwave
energy (Paré and Bélanger, 1997). MAE has been reported to be efficient for the
extraction of nutraceuticals (Kaufmann and Christen, 2002; Wang and Weller,
2006). MAE minimizes the denaturation of thermolabile compounds, and is
recommended as a fast and reliable sample preparation for chromatography
(Ganzler and Salgo, 1987; Ganzler et al., 1986). MAE has been successfully
applied for the extraction of azadirachtin related limonoids (Dai et al., 2001),
ginger oil (Alfaro et al., 2003), cocaine (Brachet et al., 2002), capsaicinoids
(Williams et al., 2004), ginsenosides (Yang et al., 2004), coumarins (Martino et
al., 2006), and taxanes (Mattina et al., 1997).
61
CHAPTER III
3. THEORETICAL ASPECTS OF STATISTICAL DESIGN
3.1. The design of experiment
“Statistics is the science of problem-solving in the presence of variability.”
In scientific experimentation, it is recommended to use statistical designs to
ensure the validity of inferences. Statistics are necessary from the early steps of
problem formulation to the final step of drawing the conclusions (Mason et al.,
2003c). Planning an experiment includes stating the problem, defining the
response variable that has to be measured, selecting the variables that are likely to
have an effect on the response, and selecting the appropriate design of experiment
(Lundstedt et al., 1998).
3.2. Two-level factorial and fractional factorial designs
Two-level factorial designs (2k) have k factors that are investigated at two
levels. The levels are called “low” and “high” and are denoted by (-1) and (+1),
respectively. The 2k designs are used in the screening of factors. They provide
information about the magnitude and the direction of factor effects. These designs
are very useful as they can be augmented to central composite designs (CCD) by
addition of axial runs and centre points (Myers and Montgomery, 2002a).
3.2.1. Unreplicated two-level factorial designs
The analysis of data obtained from replicated two-level designs is done in
a classical manner by performing the factor effects estimation, and the analysis of
variance ANOVA. If the two-level design is not replicated, the design is
saturated; there are not enough degrees of freedom in the model to allow for error
estimation. Therefore, the principle of effects sparsity, also known as the Pareto’s
principle, is used. This principle states that about 20% of the effects are active,
and the other 80% of effects are inactive as they are normal random variables with
62
the mean equal to zero. The inactive effects will fall along a straight line in
normal and half normal probability plots (Myers and Montgomery, 2002a). The
probability plots along with Pareto plots are used to determine the active effects.
The effects found to be inactive are discarded from the statistical model. In this
way, the design becomes unsaturated and the analysis of variance can be
performed (Myers and Montgomery, 2002a; SAS, 2003).
The general 23 full factorial design has 8 runs, which correspond to the 8
factor combinations represented at the corners of a cube. The lower case letters
are used to designate the factors investigated, and the capital letters designate the
effect of the respective factors. In a 23 factorial design, there are 3 main factors, 3
two-factor interactions and 1 three-factor interaction. The combination of low and
high factor levels, as well as the 8 combination points are presented in Table 3.4.
Once the response observations are recorded, such a table can be used for manual
computation of the effects of factors and regression coefficients. The combination
point denoted by “1” corresponds to the combination of the factors a, b, and c at
the low (-1) level. The other combination points are obtained by using the lower
case letters corresponding to the high level (+1) factor combinations. For
example, the combination point denoted by “a” corresponds to the following
factors combination a (+1), b (-1), and c (-1); the combination point denoted by
“ab” corresponds to the combination of factors a (+1), b (+1), and c (-1), and so
on (Table 3.4 and Figure 3.13).
Table 3.4. The 23 full factorial design.
Run Main effects Interactions Combination
points Response A B C AB AC BC ABC
a b c ab ac bc abc y 1 - - - + + + - 1 ... 2 + - - - - + + a ... 3 - + - - + - + b ... 4 + + - + - - - ab ... 5 - - + + - - + c ... 6 + - + - + - - ac ... 7 - + + - - + - bc ... 8 + + + + + + + abc ...
F
ch
th
av
po
co
fo
ne
fa
O
ea
fo
Figure 3.13.
The m
hange in resp
he levels of t
verage effec
oints where
ombination p
It can
ormula for c
eeds to mul
actor with th
Once the data
asily compu
or each effec
Three-factor
main effect, a
sponse produ
the other fac
ct of a, is th
e a is at th
points where
n be observe
calculating t
ltiply the si
he correspon
a from a 23
ute the effect
ct estimate i
r combinatio
also called th
uced by a ch
ctors” (Myer
he difference
he high lev
e a is at the l
ed from equa
the effect o
igns under t
nding combin
full factorial
ts of factors
is obtained b
63
on points rep
he average e
hange in the
rs and Montg
e in the aver
vel (+1), an
low level (-1
ations 13 an
f a factor, o
the heading
nation points
l design is o
s and interac
by dividing
presented at
effect of a fac
level of that
gomery, 200
rage respons
nd the aver
1) (Equation
[12]
[13]
[14]
nd 14, that i
or a combin
correspond
s, and then d
organised in
ctions. The r
the value of
the corners o
ctor is define
t factor avera
02a). For exa
se of the com
rage respons
12).
in order to o
nation of fac
ding to the r
divide their s
such a table
regression c
f the respect
of a cube.
ed as “the
aged over
ample the
mbination
se of the
obtain the
ctors, one
respective
sum by 4.
e, one can
coefficient
tive effect
64
by 2. The intercept is the grand average of all response observations. The
regression model of a 23 full factorial (Equation 15) can be used to predict the
response at any point within the experimental range of the factors in the design
(Myers and Montgomery, 2002a).
[15]
As specified above, the significance of effects in the model is assessed by
using probability plots, Pareto plots, and factorial plots. The magnitude of the
regression coefficients gives a hint about the significance of the effects. If a
regression coefficient is very close to zero, this is a good indication that the
respective effect might be negligible or not significant. If the regression
coefficients have a large either positive or negative value, then the corresponding
effect has a large influence on the response. In two-level factorial designs, the
variables (factors) are normalised to vary between -1 and +1. Therefore, the
relative change in a factor is directly related to the magnitude of its regression
coefficient (Lundstedt et al., 1998).
3.2.2. Two-level unreplicated fractional factorial designs
Fractional designs are special saturated two-level factorial designs in
which only a fraction (1/2, 1/4, 1/8, 1/16, ..., 1/2p) of the full factorial is
investigated. A fractional factorial design is denoted by 2k-p. For example, 24-1
refers to the one-half fraction of a two-level four-factor design. Fractional designs
have a resolution, which is an indicator of the order of confounding of main
effects and interactions. The resolution is denoted by capital Roman
letters 2 . In resolution IV designs main effects are not aliased (confounded)
with other main effects or with two-factor interactions, but the two-factor
interactions are aliased with each other (Table 2.5) (Lundstedt et al., 1998; Mason
et al., 2003b; Myers and Montgomery, 2002b).
Some general rules given in SAS documentation (SAS, 2003) for the
interpretation of resolution are:
65
• The resolution of a design is equal to the smallest order (e) of the
interactions that are confounded with zero. For example, for a resolution
IV, the four-factor interaction is considered equal to zero.
• If the resolution is IV, then two-factor interactions are confounded with
each other and three-factor interactions are confounded with main effects.
• If the resolution (r) is an odd number, then the effects of order e that are
equal or smaller than (r-2)/2 are not aliased with each other, but some of
them will be aliased with effects of order e+1. The designs of odd
resolution are recommended when the order of effects of interest is equal
or smaller than e.
• If the resolution is even, then the effects of order e that are equal or
smaller than (r-2)/2 are not aliased with each other or with effects of order
e+1. The designs with even resolution are recommended when the order of
effects of interest is equal or smaller than e, the effects of order e+1 are
not negligible, and the effects of order e+2 or higher are negligible.
• In general the higher the resolution the larger the design.
All saturated fractional designs invoke the validity of the effects sparsity
principle, and are analysed in the same way as the saturated two-level full
factorial designs, as presented above. That is, the decision of deeming one or
more effects inactive is based on interpretation of various plots, among which
probability plots and Pareto plots are recommended by several publications
(Lundstedt et al., 1998; Mason et al., 2003b; Myers and Montgomery, 2002a, b;
SAS, 2003). The analysis of variance can be performed if at least one effect is
deemed inactive.
According to Myers and Montgomery (2002a), the fractional factorial designs
are among the most used designs in industrial research. They are used in
screening experiments for selecting the factors that have a large influence on the
response. Screening designs are performed at the beginning of response surface
studies. First, the important factors are identified; second, the inactive factors are
66
discarded from the statistical model, thus projecting the fractional design into a
stronger design, which can be then augmented into a response surface design.
Generation and analysis of standard two-level designs, full factorial and
fractional, as well as response surface designs can be achieved with the statistical
analysis software SAS, by using the ADX interface. This interface is specially
conceived for researchers and engineers. Here no programming steps are required,
therefore avoiding any potential programming errors. With the SAS ADX
interface, screening designs are easily analysed, and projected into full factorial
designs or response surface designs (SAS, 2003).
Table 3.5. The 2 half fraction factorial design of resolution IV.
Estimable effects Non-estimable
effects A B C D
a b c d ab ac ad
Run Aliases Combination points Response
ID bcd acd abd abc cd bd bc abcd y 1 + - - - - + + + + 1 ... 2 + + - - + - - + + ad ... 3 + - + - + - + - + bd ... 4 + + + - - + - - + ab ... 5 + - - + + + - - + cd ... 6 + + - + - - + - + ac ... 7 + - + + - - - + + bc ... 8 + + + + + + + + + abcd ...
The one half-fraction of the two-level four-factor design of resolution IV
is presented in Table 3.5. In the case of resolution IV designs the third order and
higher order interactions are considered inactive; this is an appeal to the sparsity
of effects principle (Lundstedt et al., 1998; Mason et al., 2003b; Myers and
Montgomery, 2002b; SAS, 2003). In fact, the abcd effect is considered equal to
zero (SAS, 2003). The main effects are estimable since they are aliased with the
third order effects that are considered inactive. The second order interactions are
67
aliased with each other. If they appear to have an influence, the researcher should
decide to augment the design in order to break the aliasing structures. The ID is
the identity column, and is always positive for the first half fraction. The ID and
the abcd columns are obtained by multiplying the signs of the four factors.
Therefore, ID = abcd is called the defining relation of the design. The defining
relation for the second half fraction is obtained by multiplying the column abcd
with (-1), therefore, the second half fraction has ID = -adbc. The two half
fractions form a full factorial 24 design. In practice, it doesn’t matter which half is
run (Myers and Montgomery, 2002b). The computer programs usually generate
the first half fraction (SAS, 2003).
3.3. Response surface methodology
Response surface (RS) methodologies are used in process development for
optimisation of one or more response variables as a function of several
quantitative factors. A quadratic RS model is estimated by least square regression.
The model describes the RS within the experimental range. Therefore, it can be
used to determine the factor levels that optimise the response variable. RS studies
often start with two-level factorial or fractional factorial designs to identify the
factors that significantly influence the response. Then, the designs are augmented
to central composite designs by addition of center points and axial points (Figure
3.14). The levels of the factors are coded (-1, 0, +1) denoting the low, central, and
high levels respectively. For the axial points, each factor is set in turn, at a
distance of α from the center (level 0) while the other factors are kept at the
center. In the case of face centered central composite designs (CCD), α=1. For the
center points, all factors are kept at their central level (Lundstedt et al., 1998;
Mason et al., 2003a; Myers and Montgomery, 2002b; SAS, 2003).
The response surface model for a CCD with two factors includes linear,
quadratic and bilinear (cross products) terms as presented in Equation 16.
[16]
68
Figure 3.14. Central composite design with two factor variables.
Model parameters are estimated by least square regression. The
information about the statistical fit of the model, the contribution of each factor
variable to the statistical fit; the residual error and lack-of-fit are given by the
analysis of variance. The lack-of-fit test gives information about the adequacy of
the quadratic RS model, by comparing the variation around the model with the
“pure” variation within the replicated observations at the center points. The lack-
of-fit test decomposes the residual error in “pure” error and “bias” error. The
“pure” error is obtained from the variation of the center points replicates around
their mean value. The “bias” error is obtained from the variation of mean values
around the model prediction. If the model is adequate, the lack-of-fit test is not
significant (SAS, 2003).
The canonical analysis gives information about the shape of the RS and
determines the stationary point (maximum, minimum, or saddle point). If the
stationary is a saddle point, there is no unique optimum. The shape of the RS is
characterized by the Eigen values and Eigen vectors of the second-order
parameters. The Eigen vectors indicate the direction of the principle orientation of
the surface. The signs and magnitudes of the Eigen values indicate the type of
curvature in the given directions; positive values indicate upward curvature and
negative values indicate downward curvature. The larger the absolute Eigen
value, the more pronounced the curvature (SAS, 2003).
69
If the estimated surface has a simple optimum (maximum or minimum)
situated within the range of experimentation, the ridge analysis is not necessary. If
the stationary point is a saddle point or is situated outside the range of
experimentation, the ridge analysis has to be performed. This analysis calculates
the ridge of optimum response for radii increasing from 0 (center of design) to 1
(SAS, 2003).
3.4. Conclusions
Sound statistical designs are necessary to ensure the validity of inferences
in scientific experimentation. The two-level, fractional and full factorial designs,
are the most used designs in screening studies prior to response surface
experimentation. Fractional designs are largely used in industrial research, as they
are sound, economic and can give information about the influence of factors with
few runs (Lundstedt et al., 1998; Mason et al., 2003a, b, c; Myers and
Montgomery, 2002a, b). The SAS interface ADX is meant for the use of
engineers and researchers, for easy generation and analysis of statistical designs
(SAS, 2003).
70
CHAPTER IV
4. MATERIALS AND METHODS
4.1. Flaxseed
Brown Canadian flaxseed was purchased from the local market (Montreal,
Quebec). The flaxseed was coarsely ground and vacuum-packaged in 425g bags
(Puresource PP, Guelph, Ontario). A sample of 100g flaxseed was defatted,
milled, and kept at 4ºC in view of experimentation. The flaxseed was defatted
with hexane (1:6, g/ml) at 23ºC for 1h under magnetic stirring, then filtered,
washed with hexane (1:3, g/ml), and filtered again. The defatted flaxseed was kept
in the fume hood overnight to allow the hexane to evaporate. The dry defatted
flaxseed was milled in a coffee grinder for 5 min, to obtain a meal with particle
size between 1-0.25 mm. The moisture content (4.88% wwb) was determined in
triplicates by drying 0.5 g defatted flaxseed meal (DFM) at 105ºC for 17 h
(Eliasson et al., 2003).
4.2. Operating procedure
The operating procedure that was used for all experiments involved five
steps. The steps 2 to 5 were identical for all extraction experiments. The step one
was carried out with three extraction methods, namely, microwave-assisted
extraction (MAE), the control method for MAE (called microwave-less control),
and the reference direct hydrolysis method reported by Eliasson et al. (2003).
1) 1g DFM was hydrolysed with 50 ml NaOH. For the MAE
experiments, the parameters Time, Power, Molarity and PMode
were applied according to the experimental designs. For the
reference direct hydrolysis experiments, Time = 1 h and Molarity =
1 M NaOH. The microwave-less control method was used to
determine the magnitude of the effect of microwave power on the
extraction yield of SDG when using the MAE optimised method.
Therefore, the microwave-less method was identical (Time = 3 min
71
and Molarity = 0.5 M NaOH) to the optimised MAE, except for the
fact that no microwave power was applied.
2) The carbohydrates and proteins in the hydrolysate were
precipitated with 100 ml methanol for 10 min, at 23ºC, under
magnetic stirring, followed by 5 min without stirring to allow the
DFM to settle down.
3) The hydrolysate was acidified to pH=3 by titration with 2 M
. The pH was measured with Accumet 25 (Fisher Scientific).
4) 2×10 ml of acidified hydrolysate were transferred into two
centrifuge tubes of 15 ml, and centrifuged for 5 min at 3000 rpm
(Spinette, Needham Heights, USA). After the power was turned
off, the centrifuge needed a few minutes to stop. In order to keep
track of time, all the extracts were left in the centrifuge for 5 min
after the power was turned off.
5) The extracts were filtered through 0.22 µm, 12 ml Cameo syringe
filters (Sigma-Aldrich) into 2 ml capped vials and analysed by high
performance liquid chromatography (HPLC) (Agilent 1100).
4.3. Microwave-assisted extraction (MAE) of SDG
All MAE experiments were carried out with a focused open-vessel
microwave system (Star System 2, CEM, Matthews, USA) operating at 600W
maximum power and 2450 MHz emission frequency. The glass-quartz extraction
vessel with a volume of 250 ml was placed inside the focused microwave cavity,
and fitted with a Graham type condenser as presented in Figure 4.15. Although
this microwave system has a built-in IR temperature sensor, the temperature
evolution during extractions was monitored using a fibre optic temperature sensor
(FISO Technologies, Quebec). The IR sensor measures the temperature at the
surface of the vessel, while it is well-known that inverted temperature gradients
occur during microwave processes (Kappe, 2004).
72
Figure 4.15. Schematic representation of the focused MAE system.
Indeed, it was observed during the experiments that the IR sensor read
significantly lower values as compared to the fibre optic sensor, which was
introduced inside the extraction vessel. Therefore, it is suggested that the readings
of the process temperature given by the fibre optic sensor were more accurate that
those given by the IR sensor.
4.3.1. MAE preliminary studies
4.3.1.1. Determination of solvent to sample ratio
Note: The two studies on MAE of SDG reported by Zhang and Xu (2007),
and Beejmohun et al. (2007) (presented in the section 2.1.3.2. Microwave-assisted
extraction (MAE)) had not been published when the experimental procedure for
this study had been planned.
Initial preliminary studies were carried out at 120 W for 20 min in order to
establish the solvent to sample ratio (ml:g) that would facilitate the preparation of
extracts for chromatography. Samples of 1 g DFM were used for all the
extractions. According to the literature, it is possible to reduce the volume of
solvents when using MAE as opposed to traditional extraction methods (Dai et al.,
2001; Kaufmann and Christen, 2002; Wang and Weller, 2006; Williams et al.,
2004). However, it was observed during the preliminary studies that it was
73
difficult to prepare flaxseed extracts for chromatography when the solvent to
sample ratios were lower than 50:1 (ml:g). Flaxseed contains about 8% (wwb) of
polysaccharidic mucilage, which forms a gel in aqueous solutions. The viscosity
of the mucilage gel is affected by the pH of the aqueous solution; it is maximum
at pH=6-9, and decreases as the pH decreases from 6 to 2 (Chen et al., 2006;
Mazza and Biliaderis, 1989). The polysaccharidic mucilage from extracts can be
precipitated by addition of alcohol (Eliasson et al., 2003; Mazza and Biliaderis,
1989). Furthermore, the viscosity of the extracts can be reduced by acidification
to pH=3 as the polysaccharidic structure brakes down to monosaccharides (Mazza
and Biliaderis, 1989), and precipitates in an acid environment. It was observed
that when the MAE was performed using solvent to sample ratios of 10:1, 20:1,
30:1, and 40:1 (ml:g) the clear hydrolysates were still viscous after the
precipitation with methanol, acidification to pH=3 with 2M , and
centrifugation. This led to the clogging of the 0.22 µm syringe filters, which
prevented the injection of extracts into HPLC. Moreover, when lower solvent to
sample ratios were used, the DFM adhered to the extraction vessel. This was not
desirable as it could lead to losses of SDG and increased variability of the results.
Therefore, the 50:1 (ml:g) solvent to sample ratio was used for all subsequent
extractions as it facilitated the extracts preparation for chromatography.
4.3.1.2. Determination of the experimental domain
Screening preliminary studies were carried out in order to establish the
experimental domain, that is, the low and high factor levels. Fractional factorial
screening designs can be used at the beginning of response surface studies in
order to identify the experimental domain (Myers and Montgomery, 2002a, b).
Four factors were anticipated to have an influence on the extraction yield of SDG
were identified. These were the microwave power level (Power, Watts), the time
of residence in the microwave cavity (Time, min), the concentration of NaOH
(Molarity, M NaOH), and the mode of microwave power application (PMode,
intermittent or continuous). The experimental domain was determined by using
two-level, four-factor, half fraction factorial designs of resolution IV (2 . The
74
designs were generated and analysed with SAS using the ADX interface for
design and analysis of experiments (Copyright 2002-2003, SAS Institute Inc,
Carry, NC, USA). These screening designs are saturated; there are not enough
degrees of freedom (DF) in the model to allow for error estimation. Therefore,
they were analysed following the rules presented in the section 2.3.1. Two-level
factorial and fractional factorial designs. Probability, Pareto, main effects and
interactions plots were used to identify the interaction effect that had the least
influence on the response, which was then discarded from the model, thus
allowing for error estimation. (Note: The model containing all the terms is called
the Master Model, and it is presented in the left-hand side of the ANOVA table.
The Master Model has 7DF, which are enough for the estimation of the sums of
squares (SS) and mean squares (MS) of the main effects, but not enough for the
estimation of the interactions, F-tests, and significance values. The number zero is
used to denote the values that could not be estimated. One DF is gained by
discarding one term from the Master Model. The new model that is used as
Predictive Model has 7 DF, from which 1 DF is attributed to the error term. All
SS, MS, F-test, and significance values in the Predictive model can be calculated.)
The first screening preliminary study was carried out for the following
factors combination, Power = 30 and 120 W, Time = 1 and 5 min, Molarity = 0.25
and 1 M NaOH, and PMode = power on, 30 and 60 s/min. (Note: The PMode
factor refers to the application of microwave power in continuous or intermittent
mode. For the continuous mode (power on 60 s/min), the microwave power was
supplied without interruption during the time of residence in the microwave
cavity. For the intermittent mode (power on 30 s/min), the microwave system was
programmed to turn the power off after 30 s of application, and then to turn the
power on again after 30 s of power off. This notation was maintained throughout
this thesis).
Overall, the model of the first screening design was not significant at
α=0.05 (Table 4.7). The mean value of SDG was 19.54 mg/g dfm, with a standard
deviation of 0.94. The interpretation of the main effects and interactions plot
75
(Figure 4.16) suggests that the extraction yield of SDG might be increased at
levels of Power and Time above 120 W and 5 min, respectively. Moreover, the
fact that the model is not significant indicates that further experimentation is
necessary in the indicated direction in order to identify the experimental domain
that would lead to a significant model.
Figure 4.16. Main effects and interactions plot for the 1st screening study
Table 4.7. ANOVA for the 1st screening study
76
The second screening study was carried out for the following factors
combination, Power = 120 and 360 W, Time = 5 and 25 min, Molarity = 0.5 and 1
M NaOH, and PMode = power on, 30 and 60 s/min. The mean value of SDG was
20.37 (higher than that obtained with the previous design) with a standard
deviation of 0.56. Overall, the significance values, presented in Table 4.8 under
the Pr>F heading of the Predictive Model, are lower than those obtained with the
previous design, but still not significant of α=0.05.
Table 4.8. ANOVA for the 2st screening study
The interpretation of the main effects and interactions plot (Figure 4.17)
suggests that the extraction yield of SDG might be maximised within an
experimental domain situated around Time=5 min, PMode=power on 30 s/min,
Molarity=0.5 M NaOH, and Power=360W. The power level of 360 W was too
high for the volume of the extraction mixture (50 ml NaOH + 1 g dfm). At this
power level, the extraction mixtures foamed excessively, boiled in about 1 min –
1 min:30 sec, and reached into the condenser. Therefore, it does not seem
reasonable to investigate the effect of power levels higher than 360 W.
77
Figure 4.17. Main effects and interactions plot for the 2st screening study
The experimental domain for the subsequent (screening and response
surface) studies was determined based on the results of the two preliminary
screening studies presented above. The low and high levels of the factors have to
be chosen in such a way that one would expect a significant difference between
the effects of that factor at the low and high levels. Moreover, the response (SDG)
maximum would be expected to occur within the experimental domain. Based on
the results of the screening preliminary studies the experimental domain was
chosen as follows, Power=60 and 360W, Time=3 and 9 min, PMode= power on
30 and 60 s/min, and Molarity=0.5 and 1 M NaOH. This choice is justified by the
fact that the response maximum is expected to occur between 120 and 360 W, for
the following combination of the other factors Time=5 min, PMode= power on 30
s/min, and Molarity=0.5 M NaOH.
4.3.2. MAE experiments within the established experimental domain
The MAE experiments were carried out for the established solvent to
sample ratio of 50:1 (ml NaOH:1g dfm), and experimental domain as follows:
1) Screening of the following factors by using a two-level, four-
factor, half fraction factorial design of resolution IV (2 :
a. Microwave power level, Power= 60 and 360 W
78
b. Time of residence inside the microwave cavity, Time=3
and 9 min
c. Mode of microwave power application (intermittent and
continuous), PMode= power on 30 and 60 s/min
d. Concentration of NaOH, Molarity=0.5-1 M NaOH
2) The augmentation of the screening design into a face-centered
central composite design (CCD) was done within the experimental
domain mentioned above. The CCD design was used to maximise
the extraction yield of SDG.
The analysis and interpretation of results for these studies are presented in
the Chapter V – Results and discussion.
4.3.3. Evaluation of the MAE method as opposed to control methods
The optimized MAE method was replicated 5 times and the results were
compared (one-way ANOVA) with the microwave-less control method, which
was identical to the optimised MAE except for the microwave power which was
zero, and the reference direct hydrolysis method reported by Eliasson et al.
(2003).
The direct hydrolysis method reported by Eliasson et al. (2003) is
presented in section 2.1.3.1 Conventional extraction methods of the literature
review. Briefly, the method involved the hydrolysis of defatted flaxseed meal with
1 M NaOH, for 1h, at room temperature. The method was reported to give higher
extraction yields than the commonly used alcoholic extraction methods followed
by alkaline hydrolysis (Eliasson et al., 2003). The analysis and interpretation of
results for this study are presented in the Chapter V – Results and discussion.
4.4. HPLC analysis
The HPLC analysis was performed in five replicates per sample with an
Agilent 1100 series instrument, equipped with a variable wavelength detector.
The Chemstation software was used for chromatographic data analysis. The
79
chromatographic method developed by Eliasson et al. (2003) for analysis of direct
hydrolysis extracts was used throughout this experiment, since the optimised
MAE method is by nature a direct hydrolysis method. The separation was carried
out at 25ºC on a reversed phase Discovery column (Supelco) RP C18 (5 µm, 25
cm×4.6 mm), fitted with a Supelguard cartridge (Discovery), C18 (5 µm, 2 cm×4
mm). The injection volume was 10 µl, and the flow-rate 1 ml/min. The separation
was carried out using the gradient of two solvents, as follows:
• Solvent A consisted of 5% acetonitrile (HPLC grade, Fisher
Scientific) in 0.01 M phosphate buffer with pH=2.8
• Solvent B was acetonitrile
• Gradient of A- B: 0 min (100:0, v/v), 30 min (70:30, v/v), 32 min
(30:70, v/v) (Eliasson et al., 2003).
The calibration curves were obtained by using concentrations of 20, 30,
40, 50, 70, 100, and 200 µg/ml of pure SDG standard (Chromadex, Santa Ana,
California, USA). The detector was set at 280 nm. The concentration of SDG in
the extracts was calculated using peak areas and the calibration curve
(R2=0.99996, Area=3.8289*Amount-2.6156). The typical chromatogram of (+)
SDG (ca. 18.6 min) standard is presented in Figure 4.18, and a typical
chromatogram of flaxseed extract is presented in Figure 4.19.
The peak number 1 in the chromatogram (Figure 4.19), eluted at ca.18.6
min and was identified as (+) SDG, the peaks number 2 and 3 were tentatively
identified as ferulic acid glucoside and p-coumaric acid glucoside respectively,
based on resemblance with chromatograms presented by Eliasson et al. (2003)
and Johnsson et al. (2000).
The extraction yields of SDG were expressed in mg SDG/g DFM (the
DFM had a moisture content of 4.88 % on a wet weight basis).
Figure
Figure 4
e 4.18. Chrom
4.19. Typica
80
matogram of
al chromatog
f SDG stand
gram of flaxs
dard
seed extract
81
CHAPTER V
5. RESULTS AND DISCUSSION
5.1. Screening of the factors within the established
experimental domain
As mentioned earlier in the section 4.3.2. MAE experiments within the
established experimental domain, a new screening experiment had to be carried
out in order to identify the significant factors, which would be then investigated
by using response surface methodology. The screening study was necessary, as
there was no meaning in optimising the response (SDG) as a function of non-
significant factor variables.
A two-level four-factor half-fraction screening design of resolution IV
(2 was generated and analysed with SAS ADX (SAS Institute Inc., Cary, NC,
USA). The scope was to identify the factor variables that had a significant
influence on the response (y=SDG mg/g DFM). Fractional factorial designs are
based on the principle of sparsity of effects, which states that about 20% of effects
are active and 80% of effects have negligible or no influence on the response
variable. According to this principle, the three-factor and higher interactions are
inactive. Therefore, the main effects are confounded with three-factor effects
without affecting the estimation of main effects. The two-factor interactions are
aliased with each other (Table 5.9).
Table 5.9. Aliasing structure of two-factor interactions.
82
Table 5.10 shows the investigated factors at the low and high levels, and
the response variable. The design layout and the measured response variable are
presented in Table 5.11.
Table 5.10. The factors and the response investigated in the screening design.
Table 5.11. The screening design layout.
The design is saturated, it has only 7 degrees of freedom (DF), which are
enough to estimate the main effects and the overall mean. There is no DF left to
calculate an estimate of the error variance. At this step, it has to be decided which
effect appears to have the least effect on the response by using Pareto, half –
normal, and effects plots. The length of the bar in the Pareto plot reflects the
absolute values of the effect estimates. In the half-normal plot, the absolute values
of the effect estimates are plotted against their cumulative normal probabilities.
The non-significant effects tend to fall along the identity line which passes
through the origin. As it can be observed in the Pareto plot (Figure 5.20) the
interaction Power*Time that is alliased with PMode*Molarity appears to be the
least significant, followed by the Time and PMode effects. The most influential
ef
T
M
pl
in
di
su
w
in
ffects appea
Time*PMode
Molarity and
lot (Figure
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Figure 5
Figure 5.21
83
ion Power*
ty and Pow
e only effect
contradictory
the Pareto p
the half-nor
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y not signifi
lot (Figure 5
5.20. Pareto
1. Half-norm
*Molarity, t
wer. It is p
ts that are ac
y informatio
plot (Figure
rmal plot (
this point, it
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5.22).
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t be found
84
The interpretation of main effects and interaction plot (Figure 5.22) comes
in agreement with that of the Pareto plot (Figure 5.20). The main effects are
presented on the diagonal. It appears that the main effects Power and Molarity are
the most influential and the main effects Time and PMode are the least influential,
possibly inactive. It appears that the interaction Power*Time is not significant.
However, since this interaction is aliased with PMode*Molarity, their effects are
calculated in the same way. The decision to discard the Power*Time interaction
seems to be subjective. Therefore, the decision to discard the Power*Time
interaction has to be supported by reasonable hypotheses.
Figure 5.22. Main effects and interactions plot.
It is hypothesised that a maximum amount of SDG is extracted in 3 min at
60W, and further increasing the extraction time, or the microwave power is not
going to increase the extraction yield. The effect of Power is much stronger than
the effect of Time. Moreover, Time seems to have no significant effect, as about
the same result can be obtained regardless of the extraction time. It is very easy to
confirm this choice by simply using the ADX interface of SAS to select in turn
each of the two interactions and then to perform the analysis of variance. The
results will show if the decision to discard the Power*Time interaction is the
correct one. First, the interaction Power*PMode is discarded incorrectly, and the
analysis of variance is performed (Tables 5.12, 5.13).
85
Table 5.12. The effect of the incorrect decision on ANOVA
Table 5.13. The effect of the incorrect decision on the fit statistics
By discarding the Power*PMode interaction, the model becomes not
significant. Moreover, none of the three effects (Power, Molarity and
Power*Molarity) expected to be significant are not significant. In addition, the
value of the adjusted R-square is lower than 50% (Table 5.13). It is obvious that
the decision to discard the Power*Mode interaction is incorrect.
Next the effect of discarding the Power*Time interaction will be
investigated. The ANOVA (Table 5.14) and the fit statistics (Table 5.15) show
that the decision was correct. The overall model is significant at α≈0.01, the three
effects expected to be active, namely, Power*Molarity, Power and Molarity, are
all significant at α<0.01. The R-square =100% and the Adjusted R-square
=99.97%, which means that 100% of the variation in the response is explained by
86
the model. However, the main effect, Time, is not significant at α=0.05.
Considering that it is not recommended to accept a level greater than 5% (α=0.05)
for taking the risk of making a type I error (reject the null hypothesis when it is
true), the Time factor should be discarded.
Table 5.14. The effect of the correct decision on ANOVA
Table 5.15. The effect of the correct decision on the fit statistics
However, before rejecting the Time factor, one should take advantage of
the powerful regression model and its prediction capabilities to investigate the
response surface, and the optimisation calculations. The regression model for the
coded and uncoded factor levels is presented in Table 5.16. It is describing how
the factor variables and their effects influence the response. It seems that any
increase in the level of the main factors will result in a decrease in the response;
however, an increase in the response will result from the two-factor interactions.
87
The most active factor in the model is the Power*Molarity. This was expected
considering the way microwave interacts with the dielectric materials. It is
expected that the interaction between microwave radiation and NaOH will
enhance the extraction kinetics and diffusion processes leading to accelerated
hydrolysis of the matrix. However, one cannot expect to increase the NaOH
concentration to obtain a maximum effect due to the heat produced from ohmic
losses, which might in fact lead to thermal degradation of the SDG. It should be
kept in mind that, at this step, the interactions have aliases.
Table 5.16. Predictive model for the screening design
“A 95% prediction interval for an unknown quantity is the region which has a 95% chance of containing the true value of the quantity” (SAS, 2003). The red lines represent the main effects, and the blue lines delimit the prediction interval.
Figure 5.23. Prediction profiler
88
Table 5.17. Numerical optimisation results for the screening design
Figure 5.24. Response surface plot for the screening design
The prediction profiler (Figure 5.23) shows that the model has 95%
chances of predicting the true value of the response as a function of the four
factors investigated, Power, Time, PMode, and Molarity. It is interesting to
observe that the effect of each main factor on the response is maximised when the
factors are kept at the low level. The numerical optimisation results (Table 5.17)
also suggest that the extraction yield can be optimised when the factors are set at
the low level, namely, Power=60 W, Time=3 min, PMode=power on 30 s/min,
and Molarity=0.5 M NaOH. The same trend is observed in the response surface
plot (Figure 5.24). For plotting the factor Power against Molarity, the other two
factors were kept at the low level, as suggested by the numerical optimiser. The
89
curvature in the response surface is induced by the effects of two-factor
interactions. The curvature is most likely due to the Power*Molarity effect, since
it is the most significant term in the model. The curvature and the effect of the
Power*Molarity interaction will be investigated in depth with the response
surface optimization study.
By analysing this screening design, one gained understanding of the
effects of factors and interactions on the response, and the direction and
magnitude of factors effects. It is obvious that the factors Power, Molarity and
their interaction seem to be the key to the optimisation of SDG extraction. One
can now proceed to discard the factor Time since it is not significant at α=0.05.
(Note: The Time factor is not significant, which means that similar results are
obtained for 3 and 9 min of residence in the microwave cavity. Therefore, by
discarding the Time factor from the model one will consider that all extractions
were carried out for 3 min of residence in the microwave.)The effect of discarding
the Time factor is presented in the ANOVA table (Table 5.18) and the fit
statistics table (Table 5.19).
Table 5.18. The effect of discarding the factor Time on the ANOVA
90
Table 5.19. The effect of discarding the factor Time on the fit statistics
The effect of discarding the Time factor inevitably caused the next least
significant effect, namely PMode, to lose its importance in the model. The
optimisation recommendations remained unchanged, that is, in order to maximise
the response, all factors should be kept at the low level. The natural conclusion
now is to discard the PMode factor as well. (Note: The PMode factor is not
significant, which means that similar results are obtained for the intermittent and
continuous power application. Therefore, by discarding the PMode factor from
the model one will consider that all extractions were carried out in intermittent
mode, that is, in sequences of 30s of power on/off.) Interestingly, all the
investigation steps performed from the beginning until now reveal that in spite of
its saturated nature, the screening design is very powerful. One could have taken
the decision to discard the factors Time and PMode and subsequently their
interaction, by simply choosing to trust the main effects and interaction plot and
the Pareto plot. However, it is safer to perform an in-depth investigation for taking
well-informed decisions.
The screening design has accomplished its role; now, one can project the
unsaturated screening design (2 into a (22) full factorial design. This is
achieved by discarding from the predictive model the two non-significant factors,
Time and PMode. The (22) full factorial design can then be augmented to a central
composite design (CCD) by addition of 4 axial and 3 center points.
The full factorial design was analysed with the ADX interface of SAS.
ADX automatically checks to identify if any assumptions underlying the analysis
have been violated (SAS, 2003). No outliers were identified, but the Box-Cox
response transformation (Figure 5.25) was suggested by the assumptions check
91
test. ADX uses a likelihood ratio test to determine if the estimated optimal power
transformation is significantly different from the null transformation (SAS, 2003).
ADX recommended the optimal transformation 1/SDG-2 for the response variable.
Figure 5.25. The Box Cox optimal transformation of the response.
Figure 5.26. Main effects and interaction plot of the full factorial design.
The main effects, Power and Molarity, and their interaction
Power*Molarity, are presented in the Figure 5.26. This plot offers one more proof
92
in the favour of the validity of the screening design. It appears that the principle of
sparsity of effects held very well. The main effects were confounded with the
three-factor interactions, which were considered inactive. The proof that the three-
factor interactions were inactive is offered in this plot, the effects are identical
with those observed earlier in the Figure 5.22 showing the main effects and
interactions for the screening design.
The response surface (Figure 5.27) obtained with the full factorial
response surface model is almost identical with the response surface (Figure
5.24) generated with the response surface model of the screening designs which
contained aliases. The ANOVA, the fit statistics and the response surface model
are presented in Tables 5.20, 5.21, and 5.22, respectively.
Figure 5.27. The response surface for the full factorial design.
Table 5.20. The ANOVA for the full factorial design.
93
Table 5.21. The fit statistics for the full factorial design
The model is significant at α=0.05. The values of the R-square and
Adjusted R-square (Table 5.21) are a little bit lower than those of the screening
design were (Table 5.19), but they are good. Eighty nine percent of the variability
in the data is explained by the model. It was expected that by discarding the non-
significant effects some of the variability would no longer be explained. The
reduction in the number of main factors to half resulted in less than 11% of
unexplained variability.
Table 5.22. The response surface model and the effects estimates for the full
factorial design.
As expected, the Power*Molarity effect has the most influence on the
response. The regression coefficients (Table 5.22) suggest that increasing the
level of main factors will have a decreasing effect on the response value. The
interaction between microwave radiation and NaOH seems to be the key factor in
maximising the extraction yield of SDG. The regression coefficients represent
half the value of the estimates. Therefore, the interpretation of the effects
94
estimates values give the same information as the interpretation of the regression
coefficients.
Table 5.23. Numerical optimisation results for the full factorial design.
The numerical optimisation results (Table 5.23) are consistent with the
previous results, they suggest that the extraction yield is maximised for the low
levels of the factors.
5.2. Temperature profiles for the screening experiment
The real-time temperature (Figure 5.28) was measured with a fibre optic
sensor, which was placed in the extraction mixture inside the extraction vessel.
The eight runs correspond to the number of the four-factor combinations in the
screening design layout presented in the Table 5.23.
Figure 5.28. Temperature profiles for the Screening design.
0
20
40
60
80
100
120
140
0 100 200 300 400 500 600 700
Tempe
rature (
ºC)
Time (sec)
Run1
Run2
Run3
Run4
Run5
Run6
Run7
Run8
an
in
te
st
te
co
th
ir
te
5.
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ap
re
of
di
The p
nd continuo
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emperature r
table period
emperatures,
ondenser, w
he extractio
rregular tem
emperature (
.23), which
pplication a
mixture to
pplication o
each a maxim
f microwave
ifferent heat
Fig
0
20
40
60
80
100
120
140
Maxim
um te
mpe
rature ºC
patterns of te
us power ap
power app
rose linearly
d of 30 s (
, the extrac
where due to
n vessel ca
mperature pa
(about 115ºC
h had a com
and 0.5 M
reach the
f 360W for
mum tempe
e irradiation
ting behaviou
gure 5.29. M
37.65
8
Run1 R
emperatures
pplication ar
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(power off)
ction mixtur
the cold env
ausing sign
atterns for t
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NaOH. Abo
maximum
9 min and
rature of 10
n, the differe
ur.
Maximum tem
87.4
53.3
un2 Run3
Screeni
95
recorded fo
re clearly d
aused the
0 sec of irra
for temper
res foamed
vironment th
ificant drop
the experim
rded for the R
of 360 W f
out 2 min
temperature
1M NaOH
03ºC. This is
ent salt conc
mperatures f
108.65
3
3 Run4 R
ing experim
or the interm
istinguishab
temperature
adiation foll
ratures belo
excessively
he foam brok
p of temper
ments ran at
Run 6 (Figu
for 3 min o
were suffic
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(run 8) req
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centrations in
for the screen
39.1
114.8
Run5 Run6
ment num
mittent (30 se
ble on the gr
e to fluctu
owed by a
ow 90ºC. Fo
y and rose
ke and fell d
ratures. Thi
360W. Th
ures 5.28, 5.2
of continuou
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ngly, the co
quired about
or identical c
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85
89.451
6 Run7
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raph. The
uate. The
relatively
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into the
down into
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he highest
29, Table
us power
e reaction
ontinuous
4 min to
conditions
on have a
103.45
Run8
96
Table 5.23. Screening design layout.
The highest extraction yields were obtained for the runs 1 and 7, which
had low combinations of power level and concentration of NaOH (Table 5.23).
However, the slightly lower result obtained for run 7 is due to the combinations of
time and power mode at high levels. The limiting factors for the extraction of
SDG appear to be a longer duration of interaction between the extraction mixture
and microwave at high power levels, which might lead to thermal degradation of
SDG. Information is available in the literature regarding the stability of SDG in
baked breads and muffins containing flaxseed (Nesbitt et al., 1999). Therefore, it
could be concluded, that the temperature is a limiting factor only in the presence
of an alkali.
Studies showed that the dissolved salts have two major effects on the
dielectric properties of pure water, namely, they lower the value of the dielectric
constant, and increase the value of the dielectric loss. The behaviour of ionic
aqueous solutions at microwave frequency can be predicted by the Hasted-Debye
models. The total dielectric loss at microwave frequencies occurs by dipolar
polarization and ionic conduction (ohmic loss). The behaviour of ionic solutions
at a given microwave frequency is governed by temperature. The contribution to
the total dielectric loss of dipolar polarization decreases as the temperature
increases, and the contribution of the ionic conduction increases as the
temperature increases. Therefore, the heating behaviour at ionic solutions at a
97
given microwave frequency is dominated at the initial stage by dipolar
polarization, and then as the temperature increases by ionic conduction (Decareau
and Mudgett, 1985).
At this stage, it is not possible to describe the behaviour of aqueous NaOH
at 2450 MHz without measuring the dielectric properties at various temperatures.
The behaviour of NaOH at 2450 MHz with and without flaxseed flour will
constitute the objective of future studies.
5.3. Response surface study
The ADX interface of SAS was used to augment the 22 full factorial
design resulting from the screening study to a face centered central composite
design (CCD) by addition of axial and central points. The CCD design was used
to discover which values of the Power and Molarity factors maximised the
extraction yield of SDG. A quadratic response surface (RS) model was estimated
by least square regression. The steps of data analysis included model fitting and
ANOVA to estimate the model parameters, the canonical analysis to investigate
the shape of the predicted RS, and the ridge analysis to search for the region of
optimum response. The analysis of RS designs was described in the section 3.3.
Response surface methodology.
The CCD design had 15 runs, including 8 factorial points coming from the
22 full factorial design, plus 4 axial and 3 center points. The old runs were
included in block 1 and the new runs in block 2. The design layout is presented in
Table 5.24. The new runs were performed in power mode 30 sec on/30 sec off,
for 3 min based on the interpretation of the screening design.
The data analysis started with assumptions checking to identify if any of
the assumptions underlying the analysis were violated. No outliers were detected,
but the Box Cox power transformation 1/SDG-2 was found to be optimal and it
was recommended by the ADX assumptions checking test (Figure 5.30).
98
Table 5.24. Central composite design layout
Figure 5.30. The Box Cox optimal power transformation of the response.
99
“A 95% prediction interval for an unknown quantity is the region which has a 95% chance of containing the true value of the quantity” (SAS, 2003). The red
lines represent the main effects, and the blue lines delimit the prediction interval. Figure 5.31. The prediction profiler.
The prediction profiler plot (Figure 5.31) shows that blocking does not
affect the response. Curvature is introduced in the response surface by the
quadratic term of the Power factor, which seems significant. This is changing the
perspective of maximisation of the response, which will no longer be maximised
at 60 W, but at 135W. The addition of axial and central points made the design
stronger, and the model was capable of describing the response surface more
accurately. The trend in the effect of Molarity was the same with that observed in
the screening study. This is an indication that the quadratic term of Molarity is not
significant. The results of the analysis of variance ANOVA are presented in Table
5.25. The master model contains all the terms, the main effects, the interaction of
main effects, and the quadratic terms (Table 5.27). As mentioned earlier in the
interpretation of the prediction profiler plot, the quadratic term of Molarity is not
significant. Overall the master model is not significant at α=0.05. The correct
decision at this step is to discard the quadratic effect of Molarity and to re-analyse
the reduced model, which will be called predictive.
100
Table 5.25. The ANOVA for the central composite design
Table 5.26. The fit statistics for the response model
The predictive model (Table 5.25) becomes significant at α=0.02, but the
main effect of Power is significant at α=0.12. This seems to pose a problem, as
the acceptable level for making a type I error is α=0.05. The quadratic effect of
Power and the interaction Power*Molarity are both significant. Moreover, they
seem to be the key for the maximisation of the extraction yield of SDG. The test
for the lack of fit is not significant, which means that the quadratic effect of
Power is describing the RS efficiently. Therefore, the main effect of Power should
not be discarded since it is involved in two significant effects, and the model is
significant. In addition, the response surface model shifted the maximisation of
the response from 60 W to 135 W. According to Myers and Montgomery (2002),
main effects do not have much meaning when they are involved in significant
interactions. The correct decision is to investigate the response surface for
optimisation details. The fit statistics (Table 5.26) shows that about 66% of the
101
variability in the data is explained by the model. This relatively low value could
be due to the effect of Power which is not significant at α=0.05. The R-square
adjusted for the number of parameters in the model and the number of
observations, increased by 5% after discarding the quadratic of Molarity.
Table 5.27. The master model and the RS predictive model
Since the factor Power is present in three terms in the model, it appears that the
system is more influenced by the Power level. By increasing the coded levels of
Power from -1 to 1, the value of the response can be represented on a curve with
the hill (upward curvature) occurring somewhere around the center of the design.
By increasing the coded levels of Molarity at the same time with Power from -1to
1, the values of the response are represented by a curved surface with a decreasing
slope. The response surface predicted by the RS model is presented in the Figure
5.32.
Figure 5.32. The response surface plot for the CCD
102
The effects estimates presented in Table 5.28 are equal to the regression
coefficients for the predictive model using coded factor levels.
Table 5.28. The estimates of the regression terms
The canonical analysis of the overall shape of the surface shows that the
stationary point is a saddle point (Table 5.29), which means that there is no
unique minimum or maximum. The critical coded and uncoded values at the
stationary point are given in Table 5.30. The values of both factors are situated
well outside the domain of experimentation. The fact that the stationary point is a
saddle point, which occurs outside the domain of experimentation, requires ridge
analysis.
Table 5.29. Canonical analysis – Stationary point for SDG
Table 5.30. Canonical analysis – Critical values at the stationary point.
The Eigen values and the Eigen vectors of the matrix of second order
parameters characterise the shape of the response surface (Table 5.31). The Eigen
vectors presented under the headings of the factors Power and Molarity point in
the directions of the principle orientation of the surface. The signs and the
magnitude of the associated Eigen values give the shape of the response surface in
103
the indicated directions. The Eigen vector 0.949 is positive; therefore, it indicates
upward curvature. The Eigen vector -0.314 indicates downward curvature. The
larger the absolute Eigen value associated with these Eigen vectors, the more
pronounced the curvature. The value 0.9111 indicates that the hill of the surface is
more aligned with Power and the valley is more aligned with Molarity.
Table 5.31. Canonical analysis – Eigen values and Eigen vectors
Table 5.32. The ridge analysis
Because the stationary point is a saddle point situated well outside the
domain of experimentation, the ridge analysis is necessary for finding the factor
combination that maximises the response. A ridge of optimum response is
estimated by increasing the radius starting from the center of the design (where
the coded levels of the factors are set to zero). The ridge analysis is showed in
Table 5.32. It appears that the extraction yield is maximised around the coded
levels of about -0.4 for Power, and -1 for Molarity. The numerical optimisation
104
results show the uncoded levels of the factors, for which the response is
maximised (Table 5.33).
Table 5.33. Numerical optimisation results.
The response surface study shows that the extraction yield of SDG is
maximised for a power level of 135W and 0.5 M NaOH. It should be kept in mind
that the study assumed fixed levels for Time (3 min) and PMode (30 sec on/off).
5.4. Temperature profiles for the axial and center points
The temperature profiles and the maximum temperatures recorded for the
axial and center point are shown in the Figures 5.33 and 5.34.
Figure 5.33. Temperature profiles for the axial and center points.
0
20
40
60
80
100
0 100 200 300
Tempe
rature (ºC)
Time (sec)
Run9
Run10
Run11
Run12
Run13
Run14
pr
0
di
co
N
te
co
N
re
fa
by
12
co
A
an
Figure
By co
resented ear
.75M NaOH
ifferent conc
ombination,
NaOH (32ºC
emperature p
omparing Ru
NaOH, Tmax
eaction mixt
actor combin
y comparing
2 (62ºC), wh
5.5. E
The
omparison w
According to
nd it resulte
0
20
40
60
80
100
Tempe
rature (ºC)
e 5.34. Maxi
omparing th
rlier in Figu
H, Tmax=32
centration o
0.5 M NaO
C). Such beh
profiles for
un 2 (360W
x=94ºC). It
ture will rea
nations, for
g the maxim
hich were ca
Evaluation
efficiency
with the dire
o Eliasson et
ed in higher
32.05
0
0
0
0
0
0
Run9
imum tempe
he data for R
ures 5.28, a
2ºC), one ca
f NaOH. Fo
OH reaches
haviour was
the screenin
W, 1M NaOH
appears th
ach a higher
a lower con
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arried out at 2
n of the eff
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t al. (2003)
r extraction
94.15
Run10
Ex
105
eratures for t
Run 1 (60 W
and 5.29, w
an see that t
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H, Tmax=87
hat within t
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72.55
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Power, Tim
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the molarity
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of NaOH. T
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hydrolysis m
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61.85 6
Run12 Ru
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NaOH, Tma
a from Run
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un 10 (360W
y range 0.5
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73ºC), 13 (6
nd 1 M respe
ised MAE
d was eval
Eliasson et a
method was o
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66.9 63.
un13 Run
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ax=38ºC),
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67ºC), and
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uated by
al. (2003).
optimised
e aqueous
15
n14
106
alcoholic extraction followed by alkaline hydrolysis. Therefore, it represents a
good means of evaluating the performance of the MAE method. The contribution
of the microwave radiation to the extraction efficiency was assessed by comparing
the MAE method with a microwave-less control method that was carried out
identically but without microwave radiation.
The extractions were carried out in 5 replicates as explained in the section
4.2. Operating procedure. Briefly, the optimised MAE was carried out with 1 g
DFM and 50 ml 0.5 M NaOH for 3 min at 135 W (30 sec on/30 sec off). The
microwave-less control extraction was carried out with 1 g DFM and 50 ml 0.5 M
NaOH for 3 min. The direct hydrolysis extraction was carried out with 1 g DFM
and 50 ml 1M NaOH, at room temperature for 1 hour. The preparation steps
following the hydrolysis were identical for all methods. The carbohydrates and
proteins were precipitated with 100 ml methanol under magnetic stirring for 10
min, followed by 5 min without stirring to allow the flaxseed residues to settle
down. The liquid phase of the hydrolysate was acidified to pH=3 by titration with
2M H2SO4, centrifuged for 5 minutes and filtered through 0.22 µm syringe filters
in view of HPLC analysis. Every sample was injected into HPLC in five
replicates. The result of each extraction replicate was expressed as the mean of the
5 HPLC injections in mg SDG/g DFM.
The one-way ANOVA (Table 5.34) was computed with the Analyst
application of SAS. The Analyst is a tool intended to provide easy access to basic
statistical analyses. If the analysis of variance provides the information that a
significant difference exists between the compared treatments, then a test for
multiple comparisons is performed to determine how the effects of the
independent variables differ from each other.
The MAE, microwave-less control, and the direct hydrolysis methods are
significantly different from each other at α=0.05. The one-way ANOVA is usually
intended for comparing the different levels of one treatment. In this case, the one-
way ANOVA was used for comparing three different treatments; therefore, the R-
square value does not have much significance.
m
(T
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The b
means of dif
Table 5.36),
The T
means of the
or equality.
alue of all k
tandard erro
pproximatio
rrors by the
ollowing co
Hydrolysis|, a
The T
referred for
pecifying tha
ype II error
Ta
basic statisti
fferent treatm
and the mea
Tukey’s HSD
three extrac
Tukey’s HS
k*(k-1)/2 dif
ors this dis
n of the dis
eir geometr
mparisons a
and |Microw
Tukey’s HD
the multipl
at the Tukey
r rate, mean
able 5.34. Th
ics of intere
ments, are t
ans (Table 5
D test was u
ction method
SD is based
fferences be
stribution is
stribution is
ric means. T
are made |M
wave-less con
DS method
le compariso
y’s test contr
ns that the
107
he analysis o
est that are
the within g
5.35).
used to deter
ds. This test
on the distri
etween k me
s difficult
obtained by
This compa
MAE-Microw
ntrol-Direct H
d is relative
ons of mean
rols the type
test is rela
of variance
important
group (same
rmine the di
compares th
ibution of th
eans. If the
to compute
y replacing t
arison desig
wave-less c
Hydrolysis|.
ely conserv
ns. The not
e I error, but
atively cons
when comp
e treatment)
ifference bet
he least squa
he maximum
means have
e, therefore,
the different
gn has k=3,
control|, |MA
vative, and
te in the Ta
t has a relati
servative. T
paring the
variation
tween the
are means
m absolute
e different
, a good
t standard
, and the
AE-Direct
therefore
able 5.35,
ively high
That is, if
si
to
nu
T
si
ar
m
of
10
ra
no
m
ignificant dif
o the conser
umber of pa
Tukey’s test
ignificance i
T
As sh
re significan
method. In ot
f SDG is si
00%, than t
adiation is 9.
ot very diff
method, whic
fferences are
rvative natur
air-wise com
consists o
is around, bu
Table 5.35. T
hown in the
ntly differen
ther words, t
ignificant. In
the differenc
.36%. The re
ferent from
ch in turn is
e found at th
re of the tes
mparisons. T
of finding tr
ut a little bit
The multiple
Table 5.35
nt from thos
the contribu
n fact, if co
ce in the re
esult obtaine
that obtain
different bu
108
he specified l
st, the level
The risk of
reatments n
higher than
e comparison
5, the results
se obtained
ution of micr
onsidering th
esult attribut
ed with the M
ned with the
ut not very d
level of α=0
of α does n
making a ty
not significa
α=0.05.
ns Tukey’s H
s obtained w
with the mi
rowave radia
he result ach
table to the
MAE method
e conventio
different for
0.05, it mean
not increase
ype II error
ant if their
HSD test.
with the MAE
icrowave-les
ation to the e
hieved with
effect of m
d is also diff
nal direct h
rm that obta
ns that due
e with the
by using
level of
E method
ss control
extraction
h MAE as
microwave
ferent, but
hydrolysis
ained with
109
the microwave-less control method. MAE achieved a 5.73% higher extraction
yield as opposed to the direct hydrolysis method.
The main conclusion of this study is that the optimisation (maximisation)
of the MAE of SDG was successful. As opposed to the conventional method, the
MAE method can extract a maximum amount of SDG from flaxseed that is closer
to the real content. The goal of every extraction method is to extract the whole
amount of the compound of interest existent in a given matrix. The more accurate
the estimation of the compound of interest the more preferred the extraction
method that achieves it. Moreover, the optimised MAE method has the merit of
being fast, simple, not requiring extensive manipulations of the sample, and
reducing the hydrolysis time by 57 minutes as compared to the direct hydrolysis
method.
The fact that the result obtained with the microwave-less control method is
not very different from that obtained with the direct hydrolysis method, reveals
that an important amount of SDG is extracted in the first 3 min with a reduced
concentration of alkali. It is possible that the extraction rate reaches a maximum
in the first 3 min and then falls to minimum. The art of optimising the extraction
rate seems to be related to the period between the beginning of the extraction and
the beginning of the falling rate. It appears that the optimised MAE method does
not need a long falling rate period to extract a maximum of SDG.
Interestingly, the result obtained by replicating 5 times the optimised MAE
method (21.45 mg SDG/g DFM) is situated between the predictions given by the
RS model (21.57 mg SDG/g DFM) and the screening model (21.38 mg SDG/g
DFM). The fact that the MAE method did not achieve the predicted yield of 21.57
mg SDG, might be due to a somehow reduced capacity of the RS model to predict
the exact value. This reduced capacity could be attributed to the reduced R-square
value, and the low significance of the main factor Power. However, the difference
between the predicted and the achieved extraction yield is only 0.12 mg SDG,
which seems rather negligible.
T
m
m
M
ex
st
m
m
ex
H
te
si
st
de
m
po
re
Table 5.36.
The s
methods are
mean estimat
MAE to extr
xperiments,
tandard error
5.6. T
The te
method are p
maximum rec
xplained by
However, si
emperatures
ignificantly
tandard dev
eviation is
microwave p
ower levels
eliability and
The means a
standard dev
comparable
tes the popu
ract SDG fr
there is ab
rs from the p
Temperatu
emperatures
presented in
corded temp
slight varia
nce for th
are lower
affect the e
iation for th
0.5. It is t
power outpu
s (135W / 5
d repeatabilit
and descripti
viation (Tab
e. The stand
ulation mean
from the sam
bout 95% c
predicted val
ure profiles
recorded fo
the Figures
peratures (sta
ations in the
he optimised
than 50ºC
extraction y
he replicate
therefore co
ut for short
50 ml NaO
ty of the opt
110
ive statistics
ble 5.36) of
dard error sh
n. It can be
me batch of
hance that
lue (21.45 ±
s for the re
or the five r
s 5.35 and 5
andard devia
microwave
d combinat
C it is unli
yield. Indeed
d MAE is
oncluded th
irradiation
OH+1 g DF
timized MAE
s of the three
f the MAE a
hows how a
said that, by
f DFM that
the results
± 0.5).
eplicated M
replicates of
.37. There a
ation 3), wh
power outp
tion of fac
ikely that
d, as shown
about 1, an
at such sm
times (3 m
M) are not
E method.
e extraction m
and direct h
accurately th
y using the o
t was used
will fall w
MAE
f the optimi
are fluctuatio
ich could be
put of the m
ctors, the m
such variat
n in Table
nd the stand
mall variation
min) at relati
t likely to a
methods.
hydrolysis
he sample
optimised
for these
within two
sed MAE
ons in the
e possibly
magnetron.
maximum
tions will
5.36, the
dard error
ns in the
ively low
affect the
an
ir
en
io
F
Figu
5.7
Accor
nd enhance
rradiation. T
nhanced the
onic conduct
1
2
3
4
5
6
Tempe
rature ( ºC)
Tempe
rature( ºC)
Figure 5.35.
ure 5.36. Th
7. Conclusi
rding to the
the extractio
The microwa
e diffusion p
tion induced
0
10
20
30
40
50
60
0
41.8
38
40
42
44
46
48
50
MAE
Temperatur
e maximum
ions
hypothesis,
on yield of
ave environm
processes thr
d by microwa
100
Tim
8
47.75
E1 MAE2
M
111
re profiles fo
temperature
it was possi
SDG from
ment accele
rough the ef
ave irradiatio
200
me (sec)
546.25
2 MAE3
MAE replic
or the replica
e for the repl
ible to reduc
flaxseed by
rated the ex
ffects of dip
on in the ext
0
547.45
3 MAE4
cate
ated MAE
licated MAE
ce the extrac
applying m
xtraction kin
polar polariz
traction med
300
MAE
MAE
MAE
MAE
MAE
41.75
MAE5
E
ction time
microwave
netics and
zation and
dia.
E1
E2
E3
E4
E5
112
The factors identified to have potential effects on the extraction yield of
SDG were the microwave power level (Power = 60 and 360 W), time of residence
in the microwave cavity (Time = 3 and 9 min), the mode of microwave irradiation
(intermittent or continuous PMode = power on 30 and 60 s/min), and the
concentration of NaOH (Molarity = 0.5 and 1M). A screening experiment was
conducted with the objective of screening out the factors that did not have a
significant influence on the response variable (extraction yield of SDG). The
study showed that the factors Time and PMode did not have a significant
influence; therefore, they were discarded from the model of the screening design,
and the subsequent experiments were carried out for 3 min, with the power
applied in intermittent mode, 30 s on/off. This resulted into the projection of the
two-level half-fraction four-factor screening design of resolution IV, into a full
factorial design with two factors. The full factorial design, containing the factors
Power and Molarity was augmented to a face centered central composite design
(CCD) by addition of axial and center points. The CCD was used to investigate
the effects of Power and Molarity on the response (extraction yield of SDG). The
objective of this response surface study was to optimise the effects of the two
factors in such a way that the extraction yield will be maximised. The results
showed that the extraction yield was maximised for the following combination of
factors Power = 135 W, Molarity = 0.5 M, for a period of 3 min with the
microwave power applied intermittently (30 s on/off). The predicted value of the
response for this combination was 21.57 mg SDG / g DFM. The real value of the
response was 21.45 mg SDG/g dfm, and it was obtained by replicating the
extraction procedure five times for the optimised factors combination.
The efficiency and repeatability of the optimised MAE method was
evaluated by using a comparative study. The extraction yield obtained with the
MAE method was compared with those obtained by direct hydrolysis and
microwave-less control methods. The direct hydrolysis method developed by
Eliasson et al. (2003) was reported to perform better than a traditional extraction
method involving aqueous alcoholic extraction followed by alkaline hydrolysis.
The microwave-less control method was carried out identically with the optimised
113
MAE method except that no microwave power was applied. The results showed
that the MAE method has a good repeatability, similar to that of the direct
hydrolysis method. Statistically, at a significance level of α=0.05, the extraction
yield difference is not very significant between the MAE and the direct hydrolysis
method, and between the direct hydrolysis method and the microwave-less control
method, but it is significant between MAE and microwave-less control method.
The MAE method achieved the highest extraction yield, 5.73 % higher than that
obtained with the direct hydrolysis method, and 9.36% higher than that obtained
with the microwave-less control method. It was therefore suggested that for 3 min
of extraction with 0.5 M NaOH, the contribution of microwave irradiation (135
W, applied intermittently 30 s on/off) to the extraction yield of SDG is 9.36%.
The optimised MAE method was found to be fast, simple, and reliable.
The reliability was based on a standard deviation of about 1, and standard error of
0.5, of the 5 replicates of MAE. It is predicted that 21.45±0.5 mg SDG/g DFM,
can be extracted with the optimised MAE by using samples of DFM from the
same batch of material that was used for experimentation.
The enhanced extraction yield achieved with MAE is attributable to the
interaction between microwave radiation and the extraction mixture. It is possible
that the extraction kinetics and the diffusion processes are enhanced by the
interaction of microwave radiation with the extraction mixture. Probably the most
important interaction occurs between microwave radiation and NaOH. Within the
experimental domain, it was observed that lower concentrations of NaOH
exhibited a stronger heating capacity. The dissolved salts in the aqueous NaOH
solution have the capacity to depress the value of the dielectric constant, and
increase the dielectric loss of pure water.
114
CHAPTER VI
6. SUMMARY AND GENERAL CONCLUSIONS
Flax belongs to the genus Linum species Linum usitatissimum; it is
generally cultivated for fibre and oil. Flax is an economically important crop for
Canada, which is the world leader in the production and export of flaxseed.
Recently, flax attracted a lot of interest within the research community, due to its
high content in omega-3 fatty acids, mucilage, high quality proteins and lignans. It
represents a key ingredient for the functional foods and nutraceutical industries.
Flaxseed contains important quantities of the lignan secoisolariciresinol
diglucoside (SDG), which is the main exogenous precursor of the mammalian
lignans enterodiol and enterolactone. SDG is a recognised nutraceutical with
several claimed health benefits. Studies showed that SDG has antioxidant capacity
(Kitts et al., 1999; Prasad, 2000a), reduces the risk of hormone related cancers
(Oomah, 2001; Westcott and Muir, 2003b), reduces the risk of diabetes, reduces
blood levels of LDL cholesterol, and has cardio-protective effects (Prasad,
2000b).
SDG is found in flaxseed in the form of a complex polymeric structure,
from which it can be released by hydrolysis (Davin and Lewis, 2003, 2005; Ford
et al., 2001). The conventional extraction methods usually involve the extraction
of SDG from flaxseed with aqueous alcoholic mixtures followed by hydrolysis.
These methods are time consuming, they can take anywhere from several hours
up to 2-3 days, and involve extensive sample manipulations. Regardless of the
extraction method used, it seems that some amount of SDG will remain trapped in
polymeric residues. This gives place for developing new optimised extraction
methods that result in an almost complete cleavage of the polymeric structures,
therefore enhancing the extraction yields of SDG.
115
It was hypothesised that microwave radiation might help in reducing the
extraction time, and increasing the extraction yield of SDG from flaxseed.
Preliminary studies were conducted in order to determine the optimal solvent to
sample ratio (50:1, ml:g), and the experimental domain. Four factors were
identified, which were likely to have an influence on the MAE of SDG. These
factors were the level of microwave power (60 and 360 W), residence time in the
microwave cavity (3 and 9 min), mode of irradiation (power on 30 and 60 s/min),
and the concentration of NaOH (0.5 and 1 M). A screening design was used to
investigate the effects of these factors on the extraction yield of SDG, and to
screen out those factors that did not have a significant effect. The results showed
that only the level of applied power and the concentration of alkali had significant
effects on the response variable SDG. The screening design was projected in a full
factorial design, which was then augmented to a face centered composite design.
The central composite study was conducted to optimise the combination of factors
Power and Molarity that maximised the response of SDG. The results showed that
the extraction yield was maximised for the combination of Power = 135 W, and
Molarity = 0.5 M, for an extraction time of 3 min with the microwave power
applied intermittently (30 s on/off).
The MAE method was found to be fast, reliable, and to give a 5.73%
higher extraction yield (21.45 mg SDG/g DFM) than the direct hydrolysis method
(20.22 mg SDG/g DFM). The comparison with the microwave-less control
method showed that by applying microwave irradiation the extraction yield was
increased by 9.36%. Statistically, at a significance level of α=0.05, the extraction
yield difference is not very significant between the MAE and the direct hydrolysis
method, and between the direct hydrolysis method and the microwave-less control
method, but it is significant between MAE and microwave-less control method.
The optimised MAE method takes only about 30 min from the beginning of
extraction until injection into HPLC, as opposed to the direct hydrolysis method
that takes about 1h:30 min.
116
The main conclusion of this thesis is that microwave energy can be
successfully applied for enhancing the extraction yield of SDG and, at the same
time, reducing the total sample preparation time for chromatography. By applying
microwave energy, a new analytical method was successfully developed and
optimised for the extraction of SDG from defatted flaxseed meal. This new MAE
method is faster than any other optimised methods reported in the literature, and it
requires very few sample manipulation steps. Moreover, the new MAE is reliable
and has good repeatability; it is therefore recommended for fast and reliable
preparation of flaxseed lignan extracts for chromatography. Some potential areas
of application for the new optimised MAE of SDG include:
• The flaxseed industry - for quantification of the content of lignan in
flaxseed, flaxseed cake (resulted after oil extraction), flaxseed hull and
flaxseed flour.
• The general food and functional food industry - for quantification of the
lignan content in breads, pastries, pasta, and other food items containing
flaxseed, during product development and quality control.
• The nutraceutical industry - for precise quantification of lignan during
product development and for quality control.
• Nutritional research - where the precise amount of lignan has to be known
prior to feeding studies using flaxseed or foods containing flaxseed.
• Generally, the new optimised MAE of SDG is an analytical method
recommended for fast and reliable sample preparation for chromatography
in any field (research and industry) requiring precise quantification of
flaxseed lignan in matrices containing flaxseed ingredients.
Note: In order to obtain reliable results when applying this optimised MAE
method, the following steps should be adhered to:
• Use a laboratory dedicated focused microwave system functioning at
2450 MHz
• Use defatted samples
117
• Use a solvent to sample ratio of 50:1 (ml:g)
• Use the specified MAE parameters of 135W, for 3 min with the power
applied intermittently in sequences of 30 s on/off
• Use the preparatory steps for chromatography as presented in this thesis,
and the HPLC method reported by Eliasson et al. (2003)
118
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