Article pubs.acs.org/IECR
Solubility of Flavonoids in Pure Solvents Olga Ferreira and Simaõ P. Pinho* LSRE, Laboratory of Separation and Reaction Engineering, Departamento de Tecnologia Química e Biológica, Instituto Politécnico de Bragança, Campus de Santa Apolónia, Apartado 1134, 5301-857 Bragança, Portugal ABSTRACT: The fast and efficient selection of food-approved solvents is required for a growing number of flavonoids that are continuously being tested for their nutraceutical properties. Solid−liquid equilibrium is an essential source of information for the design of extraction, precipitation, or crystallization processes in the food industry. In this context, the nonrandom two-liquid segment activity coefficient thermodynamic model showed to be an appropriate tool to represent the solubility of several key flavonoids (apigenin, genistein, hesperetin, luteolin) in pure solvents, suggesting its ability to predict solubility in solvents not considered during the correlation procedure. For substances with unknown melting properties the reference solvent approach was successfully applied. Additionally, new solubility data of S-hesperetin in the pure solvents acetone, ethanol, ethyl acetate, methanol, and acetonitrile was measured, between 25 and 40 °C, by the shake-flask method.
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INTRODUCTION Among the numerous phenols present in plants, the class of flavonoids is one of the most studied for their biological and pharmacological properties, comprising more than 6000 different compounds.1 Their integration in the food and pharmaceutical industries involves the study of extraction, precipitation, or crystallization processes in order to concentrate, separate, and purify a specific compound or formulation. To support this process design, the systematic study of their solubility in liquid solvents has been growing in the literature.2−11 A very important and preliminary aspect is the extraction of flavonoids from dried plant material where the solvent is selected according to the properties of the target solute. Hence, more polar aglycones or flavonoid glycosides are extracted with pure alcohols or with water−alcohol mixtures, and for less polar flavonoids (isoflavones, flavanones, methylated flavones, and flavonols), the extraction solvents are, usually, chloroform, dichloromethane, diethyl ether, or ethyl acetate.12 These compounds have a complex structure with several functional groups, presenting multiple intra- and intermolecular interactions that makes their description by traditional thermodynamic models very difficult. Four representative flavonoids were selected from the class of flavanones (hesperetin), isoflavones (genistein), and flavones (apigenin and luteolin). Table 1 presents their molecular structure and molecular mass M. A reasonable solubility database is already available in the open literature for the chosen isoflavone and flavones.4,5,8 In the case of hesperetin, however, it was necessary to measure new solubility data that can be calculated in terms of mole fraction units, due to modeling purposes. Five pure solvents were selected and measurements carried out at four different temperatures. The shake-flask method was applied, followed by quantitative analysis, as described in the experimental section of this work. To be used as an engineering tool, the thermodynamic model should be simple and yet have a predictive capacity. Very recently,13 seven activity coefficient models were compared for the calculation of active pharmaceutical ingredients solubility © 2012 American Chemical Society
Table 1. Molecular Structure and Mass (g/mol) of the Flavonoids Studied in This Work
data in alcohols, alkanes, and water. Diedrichs and Gmehling13 have concluded that the correlative nonrandom two-liquid segment activity coefficient model, NRTL-SAC,14,15 provided the best results, despite the need for some experimental information regarding the solubility of the selected solute. This Received: Revised: Accepted: Published: 6586
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Table 2. Experimental S-Hesperetin Solubilities (g/kg of Solvent) and Thermodynamic Properties of Solution at 305.55 K temperature (K)
solution properties
solvent
298.15
303.15
308.15
313.15
ΔHsol (kJ mol−1)
ΔGsol (kJ mol−1)
ΔSsol (J mol−1K−1)
acetone acetonitrile ethanol ethyl acetate methanol
126.42 (0.53) 15.202 (0.052) 29.156 (0.061) 23.881 (0.21) 35.678 (0.27)
133.17 (0.29) 17.548 (0.027) 32.337 (0.049) 26.217 (0.26) 39.830 (0.042)
142.69 (0.33) 20.333 (0.028) 35.770 (0.078) 27.865 (0.10) 43.330 (0.039)
153.44 (0.73) 23.409 (0.046) 41.818 (0.042) 29.400 (0.14) 51.745 (0.068)
9.81 (0.25) 22.3 (0.10) 18.2 (0.48) 10.6 (0.36) 18.5 (0.66)
9.28 (0.018) 15.2 (0.0077) 13.3 (0.041) 12.3 (0.029) 13.8 (0.058)
1.75 (0.74) 23.5 (0.32) 16.0 (1.43) −5.83 (1.10) 15.5 (1.96)
methodology can be used for solvent screening, first by identifying the molecular parameters of the flavonoid from a reduced set of solubility data and then by predicting the solubility behavior in other solvents.16 One should also consider that pure solute properties, such as melting temperature and enthalpy, are often unknown or unreliable for flavonoids. For those cases, the very effective reference solvent approach, RSA,17−19 can be combined with the NRTL-SAC model. To the best of our knowledge, these methodologies have not been systematically applied to flavonoid containing systems. In fact, most solubility data considered in this work was simply correlated using nonpredictive empirical models such as the Apelblat equation or similar approaches.4,5,8−10
with the temperature increasing. The experimental data obtained in this work shows high consistency when compared with already published data.9 To better understand the dissolution process, the thermodynamic properties of solution were calculated from the solubility data measured in terms of mole fractions (xh). According to the work by Krug et al.,20 from the slope and the intercept of the plot of the ln xh against (1/T − 1/Thm), it is possible to calculate the enthalpy of solution (ΔHsol) and Gibbs free energy of solution (ΔGsol), respectively. The harmonic mean of the experimental temperature in the studied range is Thm = 305.55 K. The modified van’t Hoff plots thus obtained were all linear (r2 between 0.97 and 1.00), being also possible to obtain the entropy of solution (ΔSsol) from
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MATERIALS AND METHODS 1. Chemicals. S-Hesperetin 98.0% purity, supplied by Cayman, was kept in a dehydrator with silica gel to avoid water contamination. Methanol (Merck), ethanol (Panreac), acetone, ethyl acetate, and acetonitrile (Fischer Scientific) were highperformance liquid chromatography grade with 99.8% minimum purity and used as received. 2. Experimental Procedure. The solubility experiments were carried out using the analytical isothermal shake-flask method. Saturated solutions were prepared mixing a small excess of solid solute with about 80 cm3 of solvent. To reach equilibrium, the solution is continuously stirred for 40 h, and later, the solution is allowed to settle at least 12 h before sampling. In this process temperature was monitored with 4wire platinum resistance probes (Pt-104, Pico-Technology) placed in direct contact with the solutions. This temperature measuring system was previously calibrated being possible to ensure the solution temperature is within ±0.1 K to the set temperature. Samples (5 cm3) of the saturated liquid phase were after collected using plastic syringes coupled with polypropylene filters (0.45 μm), previously heated, in order to avoid any precipitation. The gravimetric method was chosen for the quantitative analysis. Therefore, the samples were placed into preweighed glass vessels and immediately weighed (±0.1 mg). The next step is to evaporate all the solvent and dry the crystals completely in a drying stove at 343.15 K for 3 days. Finally, the glass vessels are cooled in a dehydrator with silica gel for one day and weighed. The process is regularly repeated until a constant mass value is achieved. 3. Experimental Results. The solubilities were measured at four different temperatures. Each solubility value is an average of at least three different measurements, which are presented in Table 2, simultaneously with the standard deviation (between brackets), presenting a maximum coefficient of variation of 0.99%. The solubility of hesperetin follows the order acetone > methanol > ethanol > ethyl acetate > acetonitrile and, in the temperature range studied, increases
ΔSsol =
ΔHsol − ΔGsol Thm
(1)
Table 2 also compiles the solution properties obtained at Thm, presenting also their standard deviations calculated from the regression analysis. From that data, it is evident that the molar heat of solution is unfavorable since they present always a positive value. Naturally, their magnitude gives the indication about the temperature effect on the solubility, which is of the same level in the solvents acetonitrile, ethanol, and methanol, higher than in acetone or ethyl acetate. These two last solvents show very close enthalpies of solution, but the solubility values are very different, since the negative entropy of solution in ethyl acetate hinders the dissolution process possibly due to a more considerable ordering of the solvent molecules. In acetonitrile, ethanol, and methanol the entropies of solution are of the same level of magnitude, favoring the dissolution process. Finally, it is important to mention that the relative contribution from the enthalpy term to the free energy is (in percentage) much larger than the entropy term; the minimum value was 75.6% in acetonitrile. Thus, the energetic factor is the most important for the solubility changes of hesperetin.
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THERMODYNAMIC MODELING In this work, the NRTL-SAC model will be used to calculate the activity coefficient of each solute in a pure solvent. A complete description of the model and its equations can be found elsewhere.14,15 In summary, the NRTL-SAC model describes each component using four molecular parameters that represent the different surface interaction characteristics: hydrophobic (X), polar attractive or repulsive (Y+ and Y−), and hydrophilic (Z). For 63 different organic solvents, these parameters are already available in the literature.14 Therefore, only four parameters need to be calculated for each solute. The experimental solubility data in a given set of solvents should be chosen to cover the widest range of molecular interactions. 6587
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methodology, the optimal reference solvent obtained for each flavonoid is also indicated. 2. Results and Discussion. The solubility database for hesperetin includes the binary solubility measured in this work and by Liu and Chen,9 in a total of seven pure solvents. Five solvents were selected (acetone, acetonitrile, ethanol, ethyl acetate, and water) in order to represent, as much as possible, different segment characteristics (hydrophobic, hydrophilic, and polar). The calculated molecular parameters of the solute were than used to predict the solubility in the remaining two solvents (1-butanol or methanol). The average root-meansquare (rms) errors in ln(x), rms = {Σi=data[ln x1exp(i) − ln x1calc(i)]2/N}1/2, are presented in Table 4 for hesperetin. For
Two approaches will be followed. First, if the fusion properties are known, the solubility of the solid will be approximated by ΔHf (Tf ) ⎛ 1 1⎞ ⎜ − ⎟ R T⎠ ⎝ Tf
ln x1γ1 =
(2)
where x1 is the solute mole fraction, γ1 is the solute activity coefficient, T is the absolute temperature, Tf and ΔHf are, respectively, the solute temperature and enthalpy of fusion, and R is the ideal gas constant. To avoid the use of thermal properties, often unknown or unreliable for flavonoids, a second approach is proposed where the NRTL-SAC model is combined with the reference solvent approach.17−19 This methodology can be briefly represented by the following equation ln x1i = ln x1j + ln γ1j(T , {xs}j ) − ln γ1i(T , {xs}i )
Table 4. rms Errors in ln(x) for the NRTL-SAC Results Obtained for Hesperetin
where x1j is the solubility of solute 1 in a pure reference solvent j, x1i is the solubility of solute 1 in another solvent i, γ1j and γ1i are the solute activity coefficient in the reference solvent j or solvent i, and xs is the solution composition. The optimal reference solvent j is chosen to obtain the minimum sum of the residuals in accordance to eq 4
∑
minj|
hesperetin + acetone, acetonitrile, ethanol, ethyl acetate, or water9 hesperetin + 1-butanol or methanol9
δ ln x1, ij|
∑
(ln x1i + ln γ1i) − N (ln x1j + ln γ1j)|
(4)
i = data
being x1,ij the mole fraction of component 1 in solvent i, assuming that j is the reference solvent and, finally, N is the number of experimental data in a given set. 1. Parameterization. For the solutes with unknown melting properties, i.e., apigenin, genistein, and luteolin, the NRTL-SAC model was combined with the RSA methodology. For the remaining flavonoid, hesperetin, the thermal properties are available in the literature;7 hence, for comparison purposes, both approaches were applied.
Y+
hesperetin
0.940
0.000 NRTL-SAC + RSA
Y−
Z
0.920
1.059
solute
X
Y+
Y−
Z
reference solvent
apigenin genistein hesperetin luteolin
0.435 0.560 0.886 0.788
0.670 1.721 0.220 0.000
0.705 2.854 1.110 0.000
0.000 0.000 0.841 0.251
ethyl acetate ethanol ethanol ethanol
∑ i = data
[ln x1exp(i) − ln x1calc(i)]2
0.478
prediction 298−313 0.222 (1-butanol) 0.151 (methanol)
0.125 (1-butanol) 0.155 (methanol)
maximum T range (K)
correlation apigenin + acetone, 1-butanol, 288−328 ethanol, ethyl acetate, or water4 280−333 genistein +1-butanol, ethanol, ethyl acetate, or water8 luteolin + acetone, ethanol, hexane, 273−333 methanol, or water5,10 prediction apigenin + methanol or 1-propanol4 288−328 genistein + methanol or 2-propanol8
280−333
luteolin +1-butanol, 1-propanol, 2propanol, or DMSO5
273−333
rms error in ln(x) NRTL-SAC+RSA 0.260 0.232 0.271
0.156 (methanol) 0.178 (1-propanol) 0.165 (methanol) 0.171 (2-propanol) 0.495 (1-butanol) 0.339 (1-propanol) 0.321 (2-propanol) 3.579 (DMSO)
al.,4 and for genistein, solubility data is available in six pure solvents.8 For both flavonoids, satisfactory results are obtained, within the reduced number of systems included in the fit, five for apigenin and four in the case of genistein. Additionally, very good predictions are obtained for the alcohols not included in the optimization step, having a lower rms error than the fit. However, it should be pointed out that, for these flavonoids, there is a lack of solubility data in more hydrophobic solvents (such as aliphatic or aromatic hydrocarbons, halogenated
Table 3 presents the new NRTL-SAC molecular parameters, calculated for the four solutes, which were found by minimizing the following objective function FOBJ =
correlation 298−313 0.455
system
NRTL-SAC X
rms error in ln(x) NRTL-SAC + RSA
Table 5. rms Errors in ln(x) for the NRTL-SAC Results Obtained for Apigenin, Genistein, and Luteolin
Table 3. NRTL-SAC Molecular Parameters for Each Flavonoid solute
rms error in ln(x) NRTL-SAC
comparison purposes, the rms errors were calculated excluding the reference solvent. As can be seen, both approaches give very similar correlation results with a better prediction of the NRTLSAC+RSA for the solubility in pure 1-butanol. The remaining results are presented in Table 5. Solubility data of apigenin in seven pure solvents is reported by Xiao et
i = data
= minj|
T range (K)
system
(3)
(5)
where superscripts (exp) and (calc) represent experimental and calculated solubility, respectively. For the NRTL-SAC+RSA 6588
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hydrocarbons or ethers). In fact, to improve the robustness of the parameters found, proper representative solvents of all the molecular interactions should be present.15 Bouillot et al.21 suggest that in order to get the most reliable regression, solvents should be chosen so that the weight of each segment is the same. That balance, however, is difficult to achieve when the number and diversity of solvents is limited. For luteolin, five of the nine available solvents were chosen as representatives of polar (acetone), hydrophobic (hexane), and hydrophilic solvents (ethanol, methanol, and water). The average rms error in ln(x) for all the systems included in the fit is 0.271, using ethanol as the reference solvent. In particular, for the correlation in the binary system methanol + luteolin, the rms error is 0.358. This value is comparable to the predictions made for the solubility of luteolin in 1-propanol, 2-propanol, and 1-butanol, for which a rms error of 0.339, 0.321, and 0.495, respectively, is obtained. There is, however an outlier, as the model underpredicts the solubility in dimethyl sulfoxide (DMSO) by more than 1 order of magnitude. Including it in the correlation database does not significantly improve the description of that system. This might reflect the difficulty of the NRTL-SAC + RSA formulation to describe the solubility data in very different solvents, using the same set of parameters or, eventually, the existence of a polymorph. In the second case, most thermodynamic models formulation, including NRTLSAC, is not capable to take into consideration solid structures modification that may occur in solution, and more elaborated methodologies must be developed. Nevertheless, the solubility values published in the Cayman Chemical Product Information22 indicate that the solubility of luteolin in ethanol and DMSO has the same order of magnitude, which is in better agreement with the NRTL-SAC predictions. Finally, a global summary of the correlation and prediction results with NRTL-SAC+RSA is presented in Figures 1 and 2, respectively. As can be seen, the model satisfactorily represents the solubility of some representative flavonoids in pure solvents though there is still a lack of experimental data in a larger variety of solutes and solvents to allow an improved model validation. Nevertheless, the deviations obtained are in the order of magnitude usually found for this type of solutes.14,15,23
Figure 2. Experimental data from other sources4,5,8,9 and this work vs NRTL-SAC + RSA predictions for the solubility of flavonoids in pure solvents: (+) apigenin; (Δ) genistein; (○) luteolin; (□) hesperetin.
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CONCLUSION In this work, the NRTL-SAC thermodynamic model was satisfactorily applied to calculate the solubility of several key flavonoids in pure solvents. In combination, the reference solvent approach was successfully applied, and very promising results were obtained. The solubility experimental data for this type of compounds is also still relatively scarce. Hence, new solubility data of Shesperetin in five pure solvents was presented, between 25 and 40 °C, and the correspondent thermodynamic properties of solution were calculated indicating the energetic factor as the most important for the solubility changes of hesperetin.
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AUTHOR INFORMATION
Corresponding Author
*Tel.: +351 273 303 086. Fax: +351 273 313 051. E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work is supported by project PEst-C/EQB/LA0020/2011, financed by FEDER through COMPETEPrograma Operacional Factores de Competitividade and by FCTFundaçaõ para a Ciência e a Tecnologia.
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REFERENCES
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Figure 1. Experimental data from other sources4,5,8−10 and from this work vs NRTL-SAC+RSA correlation results for the solubility of flavonoids in pure solvents: (+) apigenin; (Δ) genistein; (○) luteolin; (□) hesperetin. 6589
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(5) Peng, B.; Zi, J. Q.; Yan, W. D. Measurement and Correlation of Solubilities of Luteolin in Organic Solvents at Different Temperatures. J. Chem. Eng. Data 2006, 51, 2038. (6) Bogel-Lukasik, R.; Goncalves, L. M. N.; Bogel-Lukasik, E. Phase Equilibrium Phenomena in Solutions Involving Tannins, Flavonoids and Ionic Liquids. Green Chem. 2010, 12, 1947. (7) Chebil, L.; Humeau, C.; Anthoni, J.; Dehez, F.; Engasser, J. M.; Ghoul, M. Solubility of Flavonoids in Organic Solvents. J. Chem. Eng. Data 2007, 52, 1552. (8) Wu, J. G.; Ge, J. A.; Zhang, Y. P.; Yu, Y.; Zhang, X. Y. Solubility of Genistein in Water, Methanol, Ethanol, Propan-2-ol, 1-Butanol, and Ethyl Acetate from (280 to 333) K. J. Chem. Eng. Data 2010, 55, 5286. (9) Liu, L. X.; Chen, J. Solubility of Hesperetin in Various Solvents from (288.2 to 323.2) K. J. Chem. Eng. Data 2008, 53, 1649. (10) Peng, B.; Yan, W. D. Solubility of Luteolin in Ethanol Plus Water Mixed Solvents at Different Temperatures. J. Chem. Eng. Data 2010, 55, 583. (11) Brand, W.; Shao, J.; Hoek-van den Hil, E. F.; van Elkr, K. N.; Spenkelink, B.; de Haan, L. H. J.; Rein, M. J.; Dionisi, F.; Williamson, G.; van Bladeren, P. J.; Rietjens, I. M. C. M. Stereoselective Conjugation, Transport and Bioactivity of S- and R-Hesperetin Enantiomers in Vitro. J. Agric. Food Chem. 2010, 58, 6119. (12) Marston, A.; Hostettmann, K. Separation and Quantification of Flavonoids. In Flavonoids: Chemistry, Biochemistry, and Applications, Andersen, Ø. M.; Markham, K. R., Eds. Taylor & Francis: Boca Raton, FL, 2006; pp 1. (13) Diedrichs, A.; Gmehling, J. Solubility Calculation of Active Pharmaceutical Ingredients in Alkanes, Alcohols, Water and Their Mixtures Using Various Activity Coefficient Models. Ind. Eng. Chem. Res. 2011, 50, 1757. (14) Chen, C. C.; Crafts, P. A. Correlation and Prediction of Drug Molecule Solubility in Mixed Solvent Systems with the Nonrandom Two-Liquid Segment Activity Coefficient (Nrtl-Sac) Model. Ind. Eng. Chem. Res. 2006, 45, 4816. (15) Chen, C. C.; Song, Y. H. Solubility Modeling with a Nonrandom Two-Liquid Segment Activity Coefficient Model. Ind. Eng. Chem. Res. 2004, 43, 8354. (16) Kokitkar, P. B.; Plocharczyk, E.; Chen, C. C. Modeling Drug Molecule Solubility to Identify Optimal Solvent Systems for Crystallization. Org. Process Res. Dev. 2008, 12, 249. (17) Abildskov, J.; O’Connell, J. P. Predicting the Solubilities of Complex Chemicals I. Solutes in Different Solvents. Ind. Eng. Chem. Res. 2003, 42, 5622. (18) Abildskov, J.; O’Connell, J. P. Prediction of Solubilities of Complex Medium-Sized Chemicals. II. Solutes in Mixed Solvents. Mol. Simulat. 2004, 30, 367. (19) Abildskov, J.; O’Connell, J. P. Thermodynamic Method for Obtaining the Solubilities of Complex Medium-Sized Chemicals in Pure and Mixed Solvents. Fluid Phase Equilib. 2005, 228, 395. (20) Krug, R. R.; Hunter, W. G.; Grieger, R. A. Enthalpy-Entropy Compensation. 2. Separation of Chemical from Statistical Effect. J. Phys. Chem. 1976, 80, 2341. (21) Bouillot, B.; Teychene, S.; Biscans, B. An Evaluation of Thermodynamic Models for the Prediction of Drug and Drug-Like Molecule Solubility in Organic Solvents. Fluid Phase Equilib. 2011, 309, 36. (22) Chemical, C. Product Information - Luteolin. http://www. caymanchem.com/pdfs/10004161.pdf (24/01/2012). (23) Mota, F. L.; Carneiro, A. R.; Queimada, A. J.; Pinho, S. P.; Macedo, E. A. Temperature and Solvent Effects in the Solubility of Some Pharmaceutical Compounds: Measurements and Modeling. Eur. J. Pharm. Sci. 2009, 37, 499.
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