Experimental Data and Modelling of the Solubility of High-Carotenoid Paprika Extract in Supercritical Carbon Dioxide

Article Experimental Data and Modelling of the Solubility of High-Carotenoid Paprika Extract in Supercritical Carbon Dioxide

Dorota Kostrzewa * , Agnieszka Dobrzy ´nska-Ingerand August Turczyn Łukasiewicz Research Network—New Chemical Syntheses Institute, Al. Tysi ˛acleciaPa´nstwa Polskiego 13A, 24-110 Puławy, Poland; [email protected] (A.D.-I.); [email protected] (A.T.) * Correspondence: [email protected]; Tel.: (+48)-81-473-17-10

Received: 18 October 2019; Accepted: 16 November 2019; Published: 18 November 2019

Abstract: The studies of solubility of the paprika extract with a high concentration of carotenoids in carbon dioxide under the pressure of 20–50 MPa and at temperatures of 313.15–333.15 K were carried out using the static method. The highest solubility of paprika extract was achieved at the temperature of 333.15 K and under the pressure of 50 MPa. The obtained experimental data were correlated with five density-based models, applied for prediction of solubility in the supercritical carbon dioxide (the Chrastil, del Valle and Aguilera, Adachi and Lu, Sparks et al. and Bian et al. models). The accuracy of particular models with reference to measurement results was specified with the average absolute relative deviation (AARD) and coefficient of determination (R2). Results showed that solubility calculated based on the selected models was compliant with experimental data.

Keywords: paprika extract; solubility; supercritical carbon dioxide; density-based models; correlation

1. Introduction Extracts obtained from red paprika (Capsicum annuum L.) contain mainly lipid matter (mono-, di- and tri-glycerides, free fatty acids, fatty acid esters), and also essential oils, tocopherol, and carotenoids (mainly capsanthin, capsorubin, zeaxanthin, β-carotene, β-cryptoxanthin) [1,2]. Due to their rich composition, they are widely applied in the food, cosmetic and pharmaceutical industries. Carotenoids are very important compounds not only for their coloring properties but also for their significant role in the functioning of the human organism [3]. Carotenoids are converted into the active form—vitamin A, which plays a crucial role in vision and the formation of new cells and tissues. The protective effect of carotenoids is related to their antioxidation properties and ability to eliminate free radicals, which damage lipids, proteins and DNA [4]. The increased intake of carotenoids in the diet lowers the content of lipid peroxide in blood plasma, thus protecting against diseases caused by reactive oxygen. Carotenoids reduce the risk of heart attack, stroke, ischemic heart disease, cataracts, age-related macular degeneration, Alzheimer’s disease, cancer, and other age-related diseases [2,3,5]. Traditionally, the paprika extract is industrially obtained using extraction with organic solvents [3]. However, large amounts of hazardous substances which can remain in the final product are used in this method. Moreover, the obtained extract is thermally processed in order to remove the solvent from the extract, which can cause degradation of biologically active compounds contained in the extract [6]. Problems related to the application of organic solvents, consumers’ preferences and legal regulations all increase the interest of using the supercritical fluids in order to obtain natural products of plant origin [7]. The technology using solvents such as carbon dioxide is a real and interesting alternative, as it eliminates drawbacks of extraction with organic solvents and ensures both a very high recovery and quality of the final product [6,8].

Molecules 2019, 24, 4174; doi:10.3390/molecules24224174 www.mdpi.com/journal/molecules Molecules 2019, 24, 4174 2 of 13

The first stage of design and evaluation of all the processes with supercritical fluids is the determination of substance solubility in these solvents [6,7]. This gives information about solvent’s capacity to dissolve and about its selectivity, and it allows us to select the most appropriate parameters for extraction and separation of extract components. Depending on the process in question, high or low solubility can be sought. High solubility is required for supercritical fluid extraction, whereas low solubility is required for separation of the obtained substances. Solubility in supercritical fluids depends on numerous factors. The process parameters, i.e., temperature and pressure, are very important because they have an impact on physical properties of substances and solvent density [9]. Molecular weight, polarity and the solute volatility have an effect on the substance solubility. The increase in solute vapor pressure increases the solubility and facilitates the removal of the solute [10]. However, the increase in molecular weight and the presence of polar groups in the structure of the solute decreases their solubility in non-polar solvents. The solute solubility in supercritical fluid is often the result of the relative effect of solvent density and solute volatility. Mathematical modeling is frequently used to describe the solubility with reference to the temperature and pressure applied. Generally, two methods of modelling or predicting the substance solubility in supercritical fluids are used. They involve the theoretical modeling with the use of equation of state and modeling based on the statistical analysis of experimental data using the semi-empirical and empirical equations. Most frequently applied equations of state for modelling the solubility are: Soave-Redlich-Kwong equation, Peng-Robinson equation and Lee-Kesler-Plocker equation. However, the prediction of solubility with the use of these equations requires tedious calculations to be made and determination of critical and thermo-physical properties of the dissolved substance, which are difficult to achieve. Moreover, for compounds with higher molecular weight, equation of state does not provide good conformity with experimental results [11]. The advantage of empirical and semi-empirical models is that they are based on the simple minimization of errors using the method of least squares and that no physical properties of the dissolved substance are necessary. Only data regarding solubility, temperature, pressure, and solvent density are needed. Therefore, these models are commonly applied for predicting the solubility in the specified range of temperatures and pressures [12,13]. Over the last two decades, a number of scientific literature reports dedicated to determination of the solubility of numerous compounds in supercritical carbon dioxide such as drugs, oils, pigments and biologically active compounds have been published [7,14–19]. It should be noted, however, that many studies presented in the literature refer to the solubility of pure substances. These results do not reflect all the phenomena occurring during extraction of deferent mixtures. Due to technological reasons, studying the multicomponent solute system is very important as the solubility of the pure compound is largely different to that of the same compound in multicomponent mixture [20]. This can be related to the nature of intermolecular interactions in the supercritical fluid and specific interactions between the solute–solute and solute–solvent molecules. Although there are literature reports about the solubility of paprika extract in the supercritical carbon dioxide, they mainly refer to extracts obtained from pungency paprika which contain capsaicinoids. The presence of capsaicinoids can have an effect on the behavior and solubility of other substances in the extract. Moreover, because of simplicity, most of the solubility measurements of paprika extracts presented in the literature are based on the dynamic method [21–24]. These data refer to the apparent solubility of low-carotenoid extracts obtained in the first stage of extraction. Moreover, the data presented are limited to the narrow range of pressures (14–30 MPa). The aim of this work was to determine the solubility of high-carotenoid paprika extract in the supercritical carbon dioxide using the static method. The solubility studies of paprika extract were carried out in the temperature range: 313.15–333.15 K and pressure of: 20–50 MPa. A very important aspect of analysis was to check the possibility of predicting the solubility with the use of commonly applied models based on density. Molecules 2019, 24, 4174 3 of 13

2. Results and Discussion

2.1. Experimental Solubility Results According to the analytical characterization of paprika extract applied in this work, Table1 shows characteristics of the compounds present in the extract used for solubility studies.

Table 1. Characteristics of paprika extract.

Components/Parameters Value Extract color value (ASTA) 2500.00 Carotenoids content (%) 7.33 Violaxanthin (%) 0.22 Capsorubin (%) 0.70 Capsanthin (%) 5.04 Zeaxanthin (%) 0.42 β-cryptoxanthin (%) 0.20 β-carotene (%) 0.64 Palmitic acid (C16:0) (%) 11.55 Stearic acid (C18:0) (%) 2.11 Oleic acid (C18:1) (%) 9.08 Linoleic acid (C18:2) (%) 48.53 Behenic acid (C22:0) (%) 0.21 Arachidic acid (C20:0) (%) 0.27 α-Linolenic acid (C18:3) (%) 3.16 Lauric acid (C12:0) (%) 0.40

Table2 shows the measurement results of paprika extract solubility (S) in carbon dioxide using the static method. Each reported data point is the average of ﬁve replicate measurements. The experimental data were expressed as an average value with a standard deviation.

Table 2. Solubility (S) of paprika extract in supercritical carbon dioxide a.

b 3 1 T (K) P (MPa) ρ (kg m− ) S (g kg− ) 313.15 20 839.81 4.00 c 0.28 d ± 30 909.89 4.80 0.31 ± 40 956.07 5.50 0.25 ± 50 991.30 6.10 0.38 ± 323.15 20 784.29 3.90 0.26 ± 30 870.43 5.50 0.29 ± 40 923.32 6.40 0.35 ± 50 962.45 7.20 0.41 ± 333.15 20 723.68 3.80 0.27 ± 30 829.71 6.00 0.25 ± 40 890.14 7.50 0.32 ± 50 933.50 8.60 0.45 ± a 3 b The standard uncertainties, u, of T, P and S are u(T) = 1 K, u(P) = 0.1 MPa and u(S) = 0.14 kg m− . Data was taken from NIST Chemistry WebBook (http://webbook.nist.gov/chemistry, 2018). c Average values taken from ﬁve runs. d Uncertainties refer to standard deviation. ±

Studies carried out showed that for the applied parameters (pressure and temperature), the solubility of paprika extract in carbon dioxide changed in the ranges of 3.8 g/kg CO2 to 8.6 g/kg CO2. The standard deviation ranged from 0.25–0.45, and it was related to the speciﬁcity of studies carried out under high pressures. Figure1 shows the solubility curves of paprika extract in carbon dioxide at temperatures of 313.15–333.15 K and under the pressure of 20–50 MPa. The solubility of paprika extract increases Molecules 2019, 24, x FOR PEER REVIEW 4 of 13 Molecules 2019, 24, 4174 4 of 13 Figure 1 shows the solubility curves of paprika extract in carbon dioxide at temperatures of 313.15–333.15 K and under the pressure of 20–50 MPa. The solubility of paprika extract increases with thewith pressure the pressure increase increase at constant at constant temperature temperature (Figure (Figure 1a). This1a). Thistendency tendency can be can explained be explained with with the increasethe increase in the in thesolvent solvent density density and and specific speciﬁc intera interactionsctions between between molecules molecules of ofsolute solute and and those those of of solvent,solvent, under under the the increased increased pressure. pressure.

(a) (b)

Figure 1. Solubility (S) of paprika extract in supercritical carbon dioxide: (a) solubility of extract at various temperatures depending on the pressure ((N) 313.15 K; () 323.15 K; () 333.15 K), (b) solubility Figureof extract 1. Solubility at various (S) pressures of paprika depending extract in on supercritical the temperature carbon (( dioxide:) 20 MPa; (a) (solubility) 30MPa; of ( Nextract) 40 MPa; at ♦ various() 50 MPa). temperatures depending on the pressure ((▲) 313.15 K; (●) 323.15 K; ( ) 333.15 K), (b) solubility of extract at various pressures depending on the temperature ((♦) 20 MPa; (■) 30MPa; (▲) 40The MPa; eff (ect●) 50 of MPa). temperature is more complex (Figure1b). Solubility of extract increases with the temperature increase for the pressure of 30–50 MPa when the increase in the vapor pressure of the soluteThe effect is dominant. of temperature For studies is more carried complex out (Figur undere the1b). pressure Solubility of of 20 extract MPa, solubilityincreases decreaseswith the temperaturewith the temperature increase for increase the pressure as a result of 30–50 of decrease MPa when in carbon the increase dioxide in density.the vapor The pressure occurrence of the of solutethese twois dominant. competitive For estudiesffects leads carried to theout crossingunder th ofe pressure solubility of isotherms, 20 MPa, solubility broadly describeddecreases inwith the theliterature temperature [10,25 ,increase26]. Studies as a showedresult of (Figure decrease1a) in that carbon an isotherm dioxide crossingdensity. The area occurrence for paprika of extract these twosolubility competitive falls between effects 20 leads and 30to MPa.the crossing While comparing of solubility the obtained isotherms, results broadly with literaturedescribed reports, in the it literaturecan be observed [10,25,26]. that Studies this pressure showed is lower (Figure than 1a) the that pressure an isotherm of 35 MPa crossing obtained area for oilfor from paprika peanuts extract [27], solubilitythe pressure falls ofbetween 30 MPa 20 obtained and 30 MPa. for soyaWhile oil compar [10] anding thethe pressureobtained ofresults 28–34 wi MPath literature obtained reports, for oil itfrom can pistachiobe observed [28 ].that However, this pressure the obtained is lower range than of the isotherm pressure crossing of 35 isMPa similar obtained to results for oil achieved from peanutsby Fernandez-Ronco [27], the pressure et al.of 30 for MPa paprika obtained extract for soya with oil high [10] capsaicinoids and the pressure content of 28–34 [21]. MPa The obtained existing fordiff oilerences from arepistachio probably [28]. caused However, by the the fact obtained that the range discussed of isotherm oils and crossing paprika extractsis similar are to complex results achievedmixtures by consisting Fernandez-Ronco of various components.et al. for paprika The nature extract and with content high ofcapsaicinoids particular components content [21]. have The an existingeffect on differences the specific are curves probably of extract caused solubility by the fact in carbon that the dioxide. discussed oils and paprika extracts are complexThe experimentalmixtures consisting data achieved of various for paprika compon extractents. wereThe comparednature and to availablecontent literatureof particular data components(Figure2). The have results an effect of analyses on the carried specific out cu showrves of that extract solubility solubility values in determined carbon dioxide. at 333.15 K and for the pressureThe experimental of 30 MPa varydata significantly.achieved for Paprikapaprika extract extrac solubilityt were compared determined to available in this work literature is lower data than (Figuresolubility 2). determinedThe results of by analyses Fernández-Ronco carried out et show al. and that Ambrogi solubility et al. values for low-carotenoid determined at extracts 333.15 K [21 and,24]. forHowever, the pressure it is higherof 30 MPa than vary the solubilitysignificantly. achieved Paprika by extract Fernández-Ronco solubility dete etrmined al. for high-carotenoidin this work is lowerextract. than Di ffsolubilityerences indetermined solubility mayby Fernández-Ronco be related to the et composition al. and Ambrogi of studied et al. extracts for low-carotenoid and method extractsfor measurement [21,24]. However, of solubility it is hi (i.e.,gher dynamic, than the solubility static, online achieved or offl byine) Fernández-Ronco [22]. Solubility determinedet al. for high- by carotenoidFernández-Ronco extract. Differences et al. [21] and in solubility Ambrogi may et al. be [24 related] for the to dynamicthe composition method of refers studied to theextracts apparent and methodsolubility for of measurement chemically less of complex solubility extracts (i.e., obtained dynamic, in thestatic, first online stage ofor extraction. offline) [22]. Therefore, Solubility these determinedextracts have by better Fernández-Ronco solubility. Simultaneously, et al. [21] and it Ambr can beogi observed et al. [24] that for the the increased dynamic carotenoids method refers content to thein extractapparent reduces solubility the extract of chemically solubility. less This complex is due toextracts the fact obtained that these in compoundsthe first stage are of less extraction. soluble in Therefore,the supercritical these extracts fluid than have lipid bette substancesr solubility. present Simultaneously, in the extract. it can The be lack observed of complete that the characteristics increased

Molecules 2019, 24, x FOR PEER REVIEW 5 of 13 carotenoidsMolecules 2019 , 24content, 4174 in extract reduces the extract solubility. This is due to the fact that these5 of 13 compounds are less soluble in the supercritical fluid than lipid substances present in the extract. The lack of complete characteristics of extracts, for which solubility data were presented in the literature, preventsof extracts, the for exact which clarification solubility dataof the were existing presented differences. in the literature, All components prevents and the exacttheir clarificationcompositions of affectthe existing the volatility differences. and Allsolubility components of the and extract. their compositionsFor pressures a(40–50ffect the MP volatilitya) different and solubilitythan those of presentedthe extract. in Forliterature, pressures the (40–50 obtained MPa) data di ffsupplemenerent thant thosethe knowledge presented of in paprika literature, extract the obtained solubility data in carbonsupplement dioxide. the knowledge of paprika extract solubility in carbon dioxide.

FigureFigure 2. 2. ComparisonComparison of of experimental experimental data of paprika extractextract solubilitysolubility (S) (S) in in carbon carbon dioxide dioxide at at 333.15 333.15 K with literature reports: (∆) paprikaΔ extract 2500 CU [21]; ( ) paprika× extract 140,000 CU [21]; (o) paprikaο K with literature reports: ( ) paprika extract 2500 CU [21];× ( ) paprika extract 140,000 CU [21]; ( ) paprikaextract [extract24]; () [24]; results (♦) of results this work. of this work. 2.2. Correlation Results with Experimental Solubility Data 2.2. Correlation results with experimental solubility data The experimental data obtained in this work were used for calculation of semi-empirical equations The experimental data obtained in this work were used for calculation of semi-empirical coefficients for paprika extract solubility for the studied variables. For modelling, the density-based equations coefficients for paprika extract solubility for the studied variables. For modelling, the models proposed by Chrastil, del Valle and Aguilera, Adachi and Lu, Sparks et al. and Bian et al. were density-based models proposed by Chrastil, del Valle and Aguilera, Adachi and Lu, Sparks et al. and applied because of their simplicity and broad applicability. Bian et al. were applied because of their simplicity and broad applicability. Chrastil [29] proposed one of the first equations derived from association theory (Equation (1)). Chrastil [29] proposed one of the first equations derived from association theory (Equation 1). This is based on a hypothesis that one molecule of a solute A associates with k molecules of a solvent B This is based on a hypothesis that one molecule of a solute A associates with k molecules of a solvent to form a solvato complex ABk at equilibrium with the system. There is a linear dependency between B to form a solvato complex ABk at equilibrium with the system. There is a linear dependency solubility logarithm and density logarithm of a supercritical pure fluid and dependency of solubility between solubility logarithm and density logarithm of a supercritical pure fluid and dependency of on the temperature. solubility on the temperature. A S = ρk exp + B (1) AT S=ρexp +B (1) T 3 3 where: S is the solubility of the solute in the solvent (kg m− ), ρ is the density of the solvent (kg m− ), where:T is the S operatingis the solubility temperature of the solute (K). Constantin the solvent k expresses (kg m−3), the ρ is average the density number of the which solvent describes (kg m−3), a Tnumber is the ofoperating CO2 molecules temperature in the formed(K). Constant complex k andexpresses it is a characteristicthe average valuenumber for which a particular describes system. a numberConstant of A CO is a2 functionmolecules of in the the enthalpy formedof complex solvation and and it enthalpyis a characteristic of vaporization value for (A =a ∆particularH/RT), R 1 1 system.is the universal Constant gas A constantis a function (8.314 of J molthe −enthalpyK− ), constant of solvation B is a and function enthalpy of association of vaporization number (A and = ΔmolecularH/RT), R weightis the universal of solute gas and constant solvent. Constants(8.314 J mol k,−1 A, K− B1), of constant equation B canis a be function determined of association using the numberregression and of molecular experimental weight data. of solute and solvent. Constants k, A, B of equation can be determined usingChrastil’s the regression equation of experimental has certain data. limits as it is not suitable for solubility higher than 3 100–200Chrastil’s kg m− equation[30]. Chrastil’s has certain model limits has as been it is modified not suitable several for solubility times and higher the obtained than 100–200 equations kg maimed−3 [30]. at Chrastil’s improving model the conformity has been ofmodified calculations seve resultsral times with and experimental the obtained data. equations aimed at improvingThe model the conformity proposed of by calculations del Valle and results Aguilera with experimental is the modification data. of the model proposed by ChrastilThe andmodel it takes proposed into account by del changes Valle and of vaporization Aguilera is the enthalpy modification with temperature of the model (Equation proposed (2)) [31by]. Chrastil and it takes into account changes of vaporization enthalpy! with temperature (Equation 2) A C [31]. S = ρk exp + B + (2) T T2

Molecules 2019, 24, 4174 6 of 13

The constants A and C represent thermal effects which occur in dissolution process, whereas the constant B is related to molecular weight of the solute. For both the Chrastil’s model and del Valle – Aguiler’s model, k association number is constant and it does not depend on density. However, Adachi and Lu proposed a new equation in which they made the association number k dependent on solvent density (Equation (3)) [32]. The modification made allows the achievement of the following errors during the prediction of substance solubility. 2 A S = ρ(e0+e1ρ+e2ρ ) exp + B (3) T Sparks et al. combined Adachi-Lu and del Valle-Aguilera equations, and they proposed a six-parameter equation (Equation (4)) [33]. It is recommended for high-pressure applications. ! 2 A C S = ρ(e0+e1ρ+e2ρ ) exp + B + (4) T T2 Taking into account a non-linear dependency between the solubility and density of supercritical fluid for a wide scope of temperatures and pressures, and also the effect of temperature and density on association number, solvation enthalpy and vaporization enthalpy, Bian et al. proposed another six-parameter empirical equation for the specification of compound solubility in supercritical fluid (Equation (5)) [34].

e (e +e ρ+ 2 ) A + Cρ S = ρ 0 1 lnT exp + B (5) T Consistence and practical value of the above semi-empirical equations for the description of solubility curves were assessed by analysis of determination coeﬃcient value (R2) and the value of average absolute relative deviation (AARD). The AARD between the calculated and experimental solubility was determined based on the following equation (Equation (6)):

N 100 X yi, calculated yi, experimental AARD (%) = − (6) N y i=1 i, experimental where: N is the number of measuring points, yi, experimental – solubility determined in experiment i, yi, calculated – solubility calculated for experimental point i. Calculations of coefficients in semi-empirical equations were done with Solver program in Microsoft Excel 2010 using the least squares method. Initial values for every parameter were estimated, and then square sums for every iteration were calculated point after point until convergence tests were completed. Table3 presents the results of mathematical modeling of paprika extract solubility in supercritical carbon dioxide with the use of the discussed models. High values of determination coefficients R2 (0.9954–0.9983) and low values of average absolute relative deviation AARD (1.15–1.99) indicate that all models describe experimental data with great accuracy. Values of constant A, which is the function of solvation and vaporization enthalpies (∆H/R) were very similar in all equations. While comparing these values with literature reports, it can be recorded that these values are similar to the value given for triolein [12]. Based on the determined coefficient A, the value of total dissolution heat for paprika extract was determined for studied temperatures and it 1 was 21.01–21.10 kJ mol− . Values of association constant k and parameter B in the Chrastil model and del Valle and Aguilera model were identical, whereas constant C was very low in Sparks et al. equation as compared to del Valle and Aguilera equation. Therefore, modification made by del Valle and Aguilera to Chrastil’s equation does not cause significant changes in description of experimental data. However, modification Molecules 2019, 24, x FOR PEER REVIEW 7 of 13

AARD (%) 1.99 1.99 1.20 1.20 1.15 R2 0.9954 0.9954 0.9982 0.9982 0.9983 Values of constant A, which is the function of solvation and vaporization enthalpies (ΔH/R) were very similar in all equations. While comparing these values with literature reports, it can be recorded that these values are similar to the value given for triolein [12]. Based on the determined coefficient A, the value of total dissolution heat for paprika extract was determined for studied temperatures and it was 21.01–21.10 kJ mol−1. Values of association constant k and parameter B in the Chrastil model and del Valle and Aguilera model were identical, whereas constant C was very low in Sparks et al. equation as comparedMolecules 2019 to , del24, 4174 Valle and Aguilera equation. Therefore, modification made by del Valle and7 of 13 Aguilera to Chrastil’s equation does not cause significant changes in description of experimental data. However, modification made by Adachi and Lu making the association number k dependent on made by Adachi and Lu making the association number k dependent on solvent density had the effect solvent density had the effect on improving the description of experimental data for the studied range on improving the description of experimental data for the studied range of pressures. of pressures. Vales ofTable constants 3. The A, fitting B, e0 constants, e1, e2 in obtainedthe Adachi for fiveand semi-empirical Lu and Sparks density-based et al. model correlations. were similar, and constant C in Spark et al.’s equation was also very low. As a consequence, the application of the entire Equation SparksParameters et al. model with 6 controlled parameters does not improve the accuracy of experimental data description but these modelsChrastil remain del Vallein great and Aguileraconformity. Adachi Further and Lumodifications Sparks et al. of the BianBian et et al. al. model, whichk identifies 4.01the effect of temperatur 4.01e and density -not only on the - average number - of A 2538.33 2537.98 2527.27 2529.23 2529.92 − − − − − associationsB but also on 17.74solvation and vaporization17.74 enthalpies,66.20 lead to only 65.38slight improvement36.82 of − − − − − Adachi andC- Lu’s equation with 5 controlled1.23 10 pa4 rameters. Figure- 3 shows6.52 correlations10 3 between1.08 − × − − × − − experimentale0 data of paprikaextract solubility - in supercritical 12.62 carbon dioxide 12.47 and the 4.78 values e -- 2.11 10 3 2.06 10 3 7.34 10 5 achieved with1 the use of the discussed equations. The obtained− ×curves− are− parallel,× − which− indicates× − a e -- 5.02 10 7 4.85 10 7 14.69 2 × − × − good adjustmentAARD (%) for the 1.99entire range of temperatures. 1.99 1.20 1.20 1.15 The analysisR2 of literature0.9954 reports showed 0.9954 that the Chrastil, 0.9982 del Valle and 0.9982 Aguilera as 0.9983 well as Adachi and Lu models also described experimental data of pungency paprika extract solubility with high accuracyVales of constants[21,23,35]. A, As B, epresented0, e1, e2 in in the this Adachi work, and Fernández-Ronco Lu and Sparks et al.et al. model achieved were similar,the best and adjustmentconstant Cfor in Adachi Spark et and al.’s Lu equation equation was [21]. also At very the low.same As time, a consequence, Fernández-Ronco the application et al. [21] of and the Illés entire et Sparksal. [23] etobtained al. model higher with 6values controlled of the parameters association does constant not improve k in the the Chrastil accuracy equation of experimental (10.18 and data 11.067description respectively) but these compared models remainto data inachieved great conformity. in the present Further work. modifications This can be ofattributed the Bian to et al.the model,fact thatwhich differences identifies in thesolubility effect of occur temperature mainly in and the density high pressure not only onregion. the average At low numberpressure of and associations at low densitybut also of carbon on solvation dioxide, and the vaporization solubility determined enthalpies, in lead our tostudy only using slight the improvement static method of are Adachi similar and to Lu’sthose equation given by with Fernández-Ronco 5 controlled parameters. et al. using Figure the dynamic3 shows method. correlations The betweenvalues of experimental parameter A datain theof Chrastil paprika equation extract solubility determined in supercritical by Fernández-Ronco carbon dioxide et al. and (A the= −5226.34) values achieved and Illés with et al. the (A use = of −4364.72),the discussed which equations. takes into Theaccount obtained the heat curves of solvation are parallel, and which vaporization indicates of athe good solute, adjustment were higher for the thanentire the rangeabsolute of temperatures.value achieved in the present work. This indicates that the solubility of extracts for which data are reported in the literature are most affected by the operating temperature.

Figure 3. Cont.

Molecules 2019, 24, 4174 8 of 13 Molecules 2019, 24, x FOR PEER REVIEW 8 of 13

Figure 3. Comparison of the experimental and correlated solubility data.

The analysisFigure of literature 3. Comparison reports of showed the experimental that the Chrastil, and correlated del Valle solubility and Aguilera data. as well as Adachi and Lu models also described experimental data of pungency paprika extract solubility with high 3. Materialsaccuracy [ 21and,23 Methods,35]. As presented in this work, Fernández-Ronco et al. achieved the best adjustment for Adachi and Lu equation [21]. At the same time, Fernández-Ronco et al. [21] and Illés et al. [23] obtained 3.1.higher Materials values of the association constant k in the Chrastil equation (10.18 and 11.067 respectively)

comparedThe paprika to data extract achieved obtained in the presentfrom supercritical work. This canCO2 beextraction attributed process to the fact(pressure that di ﬀ45erences MPa, in temperaturesolubility occur323.15 mainly K) carried in the out high at pressureŁukasiewicz region. Research At low Network pressure – andNew at Chemical low density Syntheses of carbon Institutedioxide, in thePuł solubilityawy was determinedthe material in used our studyfor these using studies. the static Carbon method dioxide are similar(mass tofraction those givenpurity by 0.999)Fern usedández-Ronco as solvent et al.was using purchased the dynamic from method.“Linde Gaz The Polska" values ofLLCo. parameter All the A organic in the Chrastil solvents equation and determined by Fernández-Ronco et al. (A = 5226.34) and Illés et al. (A = 4364.72), which takes reagents had analytical purity or GC purity and− were purchased from Sigma-Aldrich− and Avantor Performanceinto account Materials the heat Poland of solvation S.A. Carotenoids and vaporization analytical of the standards solute, were for higherqualitative than and the absolutequantitative value analysesachieved were in thepurchased present from work. Caro ThisteNature indicates GmbH that the and solubility analytical of standards extracts for of which methyl data esters are of reported fatty acidsin the were literature provided are by most Sigma-Aldrich. aﬀected by the operating temperature.

3.2.3. Measuring Materials Equipment and Methods and Experimental Methods 3.1.Solubility Materials analysis of paprika extract in supercritical carbon dioxide was carried out using the static method. The SITEC (SITEC-Sieber Engineering AG, Maur (Zurich), Switzerland) equipment The paprika extract obtained from supercritical CO2 extraction process (pressure 45 MPa, was applied for carrying out studies (Figure 4). The detailed scheme and description of the equipment temperature 323.15 K) carried out at Łukasiewicz Research Network – New Chemical Syntheses applied was presented in the previous paper [18]. The temperature was determined using the PID Institute in Puławy was the material used for these studies. Carbon dioxide (mass fraction purity 0.999) controller with an uncertainty of 1 K. The pressure was measured with a transducer with an used as solvent was purchased from “Linde Gaz Polska" LLCo. All the organic solvents and reagents uncertainty of 0.1 MPa.

Molecules 2019, 24, 4174 9 of 13

USV Symbol Macro(s) Description had analytical purity or GC purity and were purchased from Sigma-Aldrich and Avantor Performance Materials Poland01ED S.A.ǭ Carotenoids\textogonekoverline{o} analytical standards for qualitative and quantitativeLATIN SMALL LETTER analyses O WITH OGONEK were AND MACRON \={\k o} purchased from CaroteNature GmbH and analytical standards of methyl esters of fatty acids were 01EE Ǯ \v{\EZH} LATIN CAPITAL LETTER EZH WITH CARON provided by Sigma-Aldrich.\textEzh 01EF ǯ \v{\ezh} LATIN SMALL LETTER EZH WITH CARON 3.2. Measuring Equipment and\textezh Experimental Methods 01F0 ǰ \v{j} LATIN SMALL LETTER J WITH CARON Solubility analysis of paprika\v{\j} extract in supercritical carbon dioxide was carried out using the static method.01F1 The SITECǱ (SITEC-Sieber\DZ Engineering AG, Maur (Zurich), Switzerland)LATIN CAPITAL LETTER equipment DZ was 01F2 ǲ \Dz LATIN CAPITAL LETTER D WITH SMALL LETTER Z applied for carrying out studies (Figure4). The detailed scheme and description of the equipment 01F3 ǳ \dz LATIN SMALL LETTER DZ applied was presented in the previous paper [18]. The temperature was determined using the PID 01F4 Ǵ \'{G} LATIN CAPITAL LETTER G WITH ACUTE controller with an uncertainty\capitalacute{G} of 1 K. The pressure was measured with a transducer with an uncertainty 01F5 \'{g} LATIN SMALL LETTER G WITH ACUTE ofMolecules 0.1 MPa. 2019, 24, x FORǵ PEER REVIEW 9 of 13 01F6 Ƕ \HV LATIN CAPITAL LETTER HWAIR

01F7 Ƿ \WYNN LATIN CAPITAL LETTER WYNN \textwynn 1 01F8 Ǹ \`{N} LATIN CAPITAL LETTER N WITH GRAVE \capitalgrave{N} 01F9 ǹ \`{n} LATIN SMALL LETTER N WITH GRAVE 2 01FA Ǻ \'{\r A} LATIN CAPITAL LETTER A WITH RING ABOVE AND ACUTE 01FB ǻ \'{\r a} LATIN SMALL LETTER A WITH RING ABOVE AND ACUTE

01FC Ǽ \'{\AE} LATIN CAPITAL LETTER AE WITH ACUTE 5 3 01FD ǽ \'{\ae} LATIN SMALL LETTER AE WITH ACUTE 01FE Ǿ \'{\O} LATIN CAPITAL LETTER O WITH STROKE AND ACUTE 01FF ǿ \'{\o} LATIN SMALL LETTER O WITH STROKE AND ACUTE

0200 Ȁ \G{A} LATIN CAPITAL LETTER A WITH DOUBLE GRAVE

0201 ȁ \G{a} LATIN SMALL LETTER A WITH DOUBLE GRAVE

0202 Ȃ \textroundcap{A} LATIN CAPITAL LETTER A WITH INVERTED BREVE

0203 ȃ \textroundcap{a} LATIN SMALL LETTER A WITH INVERTED BREVE 4 6 0204 Ȅ \G{E} LATIN CAPITAL LETTER E WITH DOUBLE GRAVE

0205 ȅ \G{e} LATIN SMALL LETTER E WITH DOUBLE GRAVE

0206 Ȇ \textroundcap{E} LATIN CAPITAL LETTER E WITH INVERTED BREVE \textinvbreve{E} 0207 ȇ \textroundcap{e} LATIN SMALL LETTER E WITH INVERTED BREVE \textinvbreve{e} 0208 Ȉ \G{I} LATIN CAPITAL LETTER I WITH DOUBLE GRAVE

FigureFigure0209 4.4. EquipmentEquipmentȉ for for\G{i} solubility solubility and and phase phase equilibrium equilibrium measurements: measurements: (1) mobileLATIN (1) SMALL mobile piston; LETTER piston; I( WITH2) optical DOUBLE (2) GRAVE opticalchamber; chamber; (3) magnetic (3) magnetic stirrer\G{\i} drive; stirrer ( drive;4) light (4 source) light sourcefor endoscopic for endoscopic specula; specula; (5) hand (5 pump;) hand ( pump;6) cooling (6) coolingthermostat.020A thermostat. Ȋ \textroundcap{I} LATIN CAPITAL LETTER I WITH INVERTED BREVE \textinvbreve{I} 020B ȋ \textroundcap{i} LATIN SMALL LETTER I WITH INVERTED BREVE TheThe knownknown amountamount ofof\textroundcap{\i} paprikapaprika extractextract (0.4–0.6(0.4–0.6 g)g) waswas placedplaced inin thethe opticaloptical chamberchamber withwith thethe volumevolume ofof 3030 cmcm33.. TheThe chamberchamber\textinvbreve{i} waswas closedclosed andand purgedpurged withwith gaseousgaseous carboncarbon dioxidedioxide inin orderorder toto \textinvbreve{\i} removeremove oxygen.oxygen. Then, carbon dioxide dioxide was was introduced introduced to to the the cell cell from from the the bottle bottle up up to to the the pressure pressure of 020C Ȍ \G{O} LATIN CAPITAL LETTER O WITH DOUBLE GRAVE ofapprox. approx. 6 MPa. 6 MPa. The The content content of of the the chamber chamber was was heated heated to to the required temperaturetemperature andand mixedmixed 020D ȍ \G{o} LATIN SMALL LETTER O WITH DOUBLE GRAVE intenselyintensely withwith aa magneticmagnetic stirrer.stirrer. AfterAfter reachingreaching thethe specifiedspecified temperature,temperature, thethe pressurepressure inin thethe opticoptic 020E Ȏ \textroundcap{O} LATIN CAPITAL LETTER O WITH INVERTED BREVE chamberchamber was was increased increased to theto\textinvbreve{O} the required required value. value. When When the experience the experience parameters parameters were achieved, were achieved, mixing wasmixing continued was020F continued for 2 hoursȏ for and \textroundcap{o}2 hours it was and turned it was off andturned the off system and wasthe system left for 1wasLATIN hour left SMALL to for LETTERreach 1 Ohour WITH equilibrium. INVERTED to reach BREVE \textinvbreve{o} Afterequilibrium. this period After of this time, period mixture of time, samples mixture were sa takenmples to were the taken sampler to the with sampler the capacity with the of 1.5capacity cm3. 0210 Ȑ \G{R} LATIN CAPITAL LETTER R WITH DOUBLE GRAVE Duringof 1.5 cm sampling,3. During the sampling, pressure the changes pressure in thechanges scope in of the0.5 scope MPa of were ±0.5observed. MPa were observed. 0211 ȑ \G{r} ± LATIN SMALL LETTER R WITH DOUBLE GRAVE In order to determine the amount of extract dissolved in carbon dioxide, the content of sampler In order0212 to determineȒ \textroundcap{R} the amount of extract dissolved in carbon dioxide,LATIN CAPITALthe content LETTER R WITH of INVERTEDsampler BREVE 3 3 waswas expanded expanded0213 to to a round-bottomeda ȓround-bottomed\textroundcap{r} flask flask with with the capacitythe capacity of 50 cmof 50, and cm then, andLATIN the SMALLthen sampler LETTER the R sampler WITH was INVERTED flushed was BREVE 3 withflushed acetone with0214 (25 acetone cm Ȕ) to(25 elute cm\G{U}3 the) to extract.elute the In extract. the subsequent In the subsequent stage, acetone stage, was acetoneLATIN removed CAPITAL was LETTER with removed U WITH a DOUBLE rotatory with GRAVE evaporatora rotatory 0215evaporator (R-210, Bȕchi). (R-210, After\G{u} Bȕ beingchi). After cooled being to room cooled temperature, to room temperature, the flaskLATIN was SMALL the left LETTER flask in a U desiccator WITHwas DOUBLE left GRAVE in to a attaindesiccator solid to weight attain and solid then weight the extract and then weight the ex wastract measured weight was gravimetrically measured gravimetrically ( 0.0001 g). (±0.0001 ± g).

3.3. Analysis Methods 11 The chromatographic and spectrophotometric methods were applied for qualitative evaluation of the paprika extract used for solubility studies.

3.3.1. Determination of Total Carotenoid Content The total content of carotenoids in paprika extract was determined in compliance with the procedure described in Compendium of Food Additive Specifications [36]. This method is based on absorbance measurements of acetone red paprika extract at 462 nm. The absorbance of the sample was measured using JASCO spectrophotometer model V- 650 UV-Vis. The total carotenoid content was calculated according to Equation 7.

100 A C = (7) 2100m

Molecules 2019, 24, 4174 10 of 13

3.3. Analysis Methods The chromatographic and spectrophotometric methods were applied for qualitative evaluation of the paprika extract used for solubility studies.

3.3.1. Determination of Total Carotenoid Content The total content of carotenoids in paprika extract was determined in compliance with the procedure described in Compendium of Food Additive Speciﬁcations [36]. This method is based on absorbance measurements of acetone red paprika extract at 462 nm. The absorbance of the sample was measured using JASCO spectrophotometer model V- 650 UV-Vis. The total carotenoid content was calculated according to Equation (7). 100 A462 Ck = (7) 2100mp

1% where: Ck—total carotenoid content (%), A462—sample absorbance at 462 nm, 2100—A 1cm for capsanthin/capsorubin in acetone for 462 nm, mp—sample weight (g).

3.3.2. Determination of Extract Color Value The color value of paprika extract used for studies was determined with the spectrophotometric method [37]. These studies were carried out in compliance with the Oﬃcial Analytical Methods of the American Spice Trade Association [38]. This method is based on absorbance measurement of acetone paprika extract at 460 nm. The absorbance of the sample was measured using JASCO spectrophotometer model V- 650 UV-Vis. The extract color value was calculated according to Equations (8) and (9).

164 A f ASTA = 460 (8) mp

1CU = 40.24 ASTA (9) where: ASTA—American Spice Trade Association color value, CU—Color Units, A460—sample absorbance at 460 nm, f—coeﬃcient of correction of optical path length, mp—sample weight (g), 164—calculator factor.

3.3.3. Determination of Carotenoids Concentration The methodology involved determination of carotenoid content in saponified sample of extract using the internal standard. Waters Liquid chromatograph equipped with DAD 2996 detector and 4.6 150 mm chromatographic column YMCTM Carotenoid with granulation of 3 µm which was × additionally equipped with a protective column YMCTM Carotenoid S-3, was used for studies. Mixture of methyl alcohol, acetonitrile and water with the ratio of 75:10:15 (Eluent A) in gradient elution with dichloromethane (Eluent B) was used as a mobile phase. The remaining conditions of the chromatographic separation were as follows: flow rate 1 mL/min, injector volume 20 µL, column operation temperature 298.15 K, analysis time 55 min. In order to specify the content of particular carotenoids in paprika extract, a known amount of extract was dissolved in dichloromethane and the basic solution was obtained. The specified volume of this solution was dissolved in diethyl ether and then it was saponified using 15% KOH. The reaction was carried out in a separator. The formed ether phase was washed with water to obtain neutral pH. The obtained ether solution was dried with anhydrous Na2SO4. The solvent was removed using the vacuum evaporator and the residual amount was re-dissolved in the specified volume of dichloromethane. The sample thus obtained was filtered by a syringe filter 0.45 µm, and then it was analyzed with HPLC-DAD for wavelength of 450 nm. The qualitative analysis was based on the comparison of retention times of the obtained peaks with the retention times of peaks obtained for carotenoid standards. The quantitative analysis was carried Molecules 2019, 24, 4174 11 of 13 out using the internal standards method based on the calibration curves. The β-Apo-8’-carotenal, which was also added to real test at the beginning of preparation process, was used as an internal standard. The correlation coefficients of calibration curves for particular carotenoids were R2 0.995. ≥ 3.3.4. Determination of Fatty Acid Content The method involved indirect determination of the fatty acid (FA) value as methyl esters (FAME) obtained by esterification with trimethylsulfonium hydroxide (TMSH) solution. For the analysis, the Agilent Technologies gaseous chromatograph 6890N coupled with mass spectrometer MSD 5975 was used. Measurements were carried out for the following operating parameters of gas chromatograph: capillary column adjusted to separation of the selected FA: HP-88 (60 m, 0.25 mm i.d., 0.20 µm film thickness); carrier gas flow (He 5.0): from 1.0 to 1.5 mL/min; inject volume: 1 µL; scan range: 50–500 amu; quadrupole temperature: 423.15 K; ion source temperature: 503.15 K; electron energy: 70 eV. − In order to determine the content of fatty acids, a known amount of extract was weighed to reaction vial and 0.5 cm3 tert-butyl methyl ether (TBME) was added and mixed. To this solution, 0.25 cm3 TMSH solution (0.25 M in methanol) was added and then it was shaken vigorously for 1 min and then it was injected into the chromatographic column. The qualitative analysis was based on mass spectra available in the NIST Research Library and on the comparison of peak retention times of available standard. Based on calibration curves made, the FAME value was specified and then it was calculated into the FA value in the tested sample.

4. Conclusions This paper shows the experimental data which enhance the knowledge of solubility of high-carotenoid paprika extract in supercritical carbon dioxide. Studies carried out using the static method showed that for the following parameters (20–50 MPa, 313.15–333.15 K), paprika extract solubility varied from 3.8 g/kg CO2 to 8.6 g/kg CO2. The extract solubility under the pressure of 50 MPa (T = 333.15 K) was twice as high as solubility under 20 MPa (T = 333.15 K). Based on the achieved data, the relationships between paprika extract solubility and the properties of supercritical carbon dioxide were developed. Paprika extract solubility was correlated with five semi-empirical density-based models: the Chrastil, del Valle and Aguilera, Adachi and Lu, Sparks et al. and Bian et al. The modeling results show that modifications made by Adachi and Lu had the effect on the improvement of AARD compared to Chrastil and del Valle and Aguilera models. For the studied range of parameters, Sparks et al.’s equation does not improve accuracy of the experimental data compared to Adachi and Lu’s equation. In contrast, Bian et al.’s model, which takes into account the effect of temperature and density on the average association number as well as on solvation and vaporization enthalpies, presents only a slight improvement of mapping the experimental data compared to Adachi and Lu’s model. Simultaneously, all the applied models described experimental data with a great accuracy.

Author Contributions: Conceptualization, D.K., A.D.-I. and A.T.; Methodology, D.K. and A.D.-I.; Software, D.K. and A.D.-I.; Validation, D.K. and A.D.-I.; Formal Analysis, D.K., A.D.-I. and A.T.; Investigation, D.K.; Resources, D.K.; Data curation, D.K.; Writing-Original Draft Preparation, D.K.; Writing, Reviewing and Editing, D.K. and A.D.-I.; Visualization, D.K, A.D.-I and A.T.; Supervision, A.T.; Funding Acquisition, A.D.-I. Funding: This research received no external funding. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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Sample Availability: Samples of the compounds are not available from the authors.

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