Synergistic extraction of α-Lactalbumin and β-Lactoglobulin from acid whey using aqueous biphasic system: Process evaluation and optimization

Separation and Purification Technology 146 (2015) 301–310

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Synergistic extraction of a-Lactalbumin and b-Lactoglobulin from acid whey using aqueous biphasic system: Process evaluation and optimization Sivakumar Kalaivani, Iyyaswami Regupathi ⇑ Department of Chemical Engineering, National Institute of Technology Karnataka, Surathkal, Srinivasanagar (PO), Mangalore 575025, India

a r t i c l e

i n f o

Article history: Received 27 September 2014 Received in revised form 19 March 2015 Accepted 22 March 2015 Available online 8 April 2015 Keywords: a-Lactalbumin b-Lactoglobulin Whey PEG 1000–trisodium citrate Aqueous two-phase system

a b s t r a c t The present investigation focused on the simultaneous partitioning of a-Lactalbumin (a-La) to the PEG rich phase and b-Lactoglobulin (b-Lg) to the salt rich bottom phase from acid whey using polyethylene glycol 1000–trisodium citrate systems. The effect of Tie Line Length (TLL), pH, volume ratio and whey concentration on the proteins partitioning was evaluated. Further the Response Surface Methodology (RSM) was employed to analyze the synergistic and interactive effect of these variables (TLL, pH and whey loading) on the responses (recovery and purity of a-La and b-Lg). Optimum values of these variables were obtained for different goals through desirability based multi response optimization. A system of 54.12% (w/w) TLL with 30.6% (w/w) whey at pH 7.23 separates a-La with 89% recovery and purity of 96% in the top phase, whereas that of b-Lg in the bottom phase was 96% and 76% respectively. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction In recent years, a-Lactalbumin (a-La) the utmost significant nutraceutical whey protein, grabbed the attention of researchers owing to its potential application as a drug delivery agent, anticancer drug [1], an adsorbent of cellular glucose to prevent type II diabetes [2] and is also being used to examine the mechanism of protein folding. On the other hand, b-Lactoglobulin, the major bovine whey protein, is not present in human milk and hence induces allergic reactions in few individuals [3]. Hence, bLactoglobulin depleted whey protein is preferred in formula milk and health beverages. However, hydrolyzed b-Lg has been used in the confectioneries as a better foam stabilizing agent [4]. Though the raw material (whey) is easily available and cheap, the higher cost of these proteins is resulted due to the purification strategies involved to achieve the desired degree of purity. Potential applications of these whey proteins are well established and thus the quest for finding the prospective purification techniques is in progress. In recent years, precipitation [5], hydrophobic interaction/anion exchange chromatography [6,7] and ultrafiltration [8] have been proposed by researchers for the purification of either individual or mixtures of proteins from Whey Protein Concentrate (WPC). Metsamuuronen and Nystrom

⇑ Corresponding author. Tel.: +91 824 2474000x3609; fax: +91 824 2474057. E-mail address: [email protected] (I. Regupathi). http://dx.doi.org/10.1016/j.seppur.2015.03.057 1383-5866/Ó 2015 Elsevier B.V. All rights reserved.

[9] reported 48% recovery of a-La from diluted whey using the hydrophilic polymeric membrane in ultrafiltration. Whey proteins which are high value and low volume products could be recovered at higher purity from whey using Aqueous two-phase systems. Beijernick demonstrated Aqueous Two Phase Systems (ATPS) in the year 1896 nevertheless the prospective use of these systems in the separation of the biomolecules was unveiled by Albertsson in 1986. ATPS are formed when two solutes dissolved in water are mixed above the critical concentration. The solutes could be two polymers, polymer-salt or ionic liquid-salt. The potentiality of the polymer-salt ATPS for the partitioning of whey proteins using pure protein solutions has been examined in PEG–potassium citrate [10]/potassium phosphate [11–13] and ammonium sulfate [14] systems. A methodical protein partitioning approach with whey is necessary since the partitioning characteristics are shown to vary with the presence of other biological components in the source [13]. Response Surface Methodology (RSM), a statistical approach is being widely used by researchers to determine the optimum values of input parameters to maximize or minimize the response. Optimum conditions for the extraction of propionic acid in PEG 4000–ammonium sulfate [15], bromelain in PEG 1500–potassium phosphate [16] and luciferase in PEG 1500– ammonium sulfate [17] systems were obtained through RSM and reported in the literature. The authors previously demonstrated the influence of phase components concentration, pH and phase volume ratio on the

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partitioning of a-La and b-Lg in PEG 1000–trisodium citrate system using individual pure protein solutions [18]. With that acquired knowledge, synergistic separation of these commercially important (a-La and b-Lg) proteins from whey has been investigated in PEG 1000–trisodium citrate system in the present work. Physicochemical properties of whey used in the experiment are also reported since the nature and concentration of other biomolecules present in whey can alter the partitioning of the desired proteins. The effect of significant independent variables such as pH, Tie Line Length (TLL) and concentration of whey on four responses (a-La recovery (Y1) and purity (Y3) in PEG rich phase and b-Lg (Y2) recovery and purity (Y4) in bottom salt rich phase) were studied. Optimum values of these variables for different goals (like maximum of (i) Y1, Y2, Y3 & Y4; (ii) Y1 & Y2; (iii) Y3 & Y4; (iv) Y1 & Y3; (v) Y2 & Y4) were obtained through desirability based optimization. Desirability based optimization in Design Expert is a useful tool which permits to perform the partitioning process with a definite goal/response while monitoring the other responses. 2. Materials and methods 2.1. Chemicals Polyethylene glycol (PEG) [HO–(CH2CH2O)n–CH2OH] of average molar mass 1000 g mol1, a-Lactalbumin (L 5285) type I, b-lactoglobulin (L 2506) of purity greater than 85% and Bradford Reagent were procured from Sigma–Aldrich. Molecular biology grade Bovine Serum Albumin (BSA, MB083-5G) from Himedia, India was used for the studies. Ammonia solution, ethanol, analytical grade trisodium citrate dihydrate (294.1 g mol1) of purity 99%, citric acid monohydrate and the solvents (Petroleum ether, dimethyl ether, Acetonitrile and TriFluoro Acetic Acid) of HPLC grade were procured from Merck, India. All the experiments were carried out using the deionized water from Siemens, Labostar ultra-pure water purification system. 2.2. Preparation and characterization of whey Acid whey was prepared by acidification (pH 4.6) of the pasteurized milk. The major milk protein casein coagulates in this acidic pH which was then removed by centrifugation at 15,000 g for 30 min at 4 °C (Kubota 6930, Japan). The clear straw colored whey was obtained as a supernatant and stored at 4 °C for further studies [19]. Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD) and Total Solid Content (TSC) of whey were determined as described in AOAC [20]. Total carbohydrate estimation was done by phenol-sulfuric acid method. Sodium, potassium and calcium metal ion concentrations were determined in flame photometer (Elico Ltd., model CL 378). Magnesium, Zinc and iron content were analyzed through Atomic Absorption Spectrophotometer (AAS) (GBC 932 plus, Australia). Fat content in whey and the phases were determined by Rose– Gottlieb method [20]. 10 g of the sample was treated with 1.25 mL of ammonia solution to dissolve the proteins and then 10 mL of ethyl alcohol was added to precipitate the dissolved proteins. Fat was extracted by the addition of 25 mL diethyl ether and 25 mL

Table 1 Factor and levels for the 23 full factorial experimental designs. Variables

a

1

0

1

+a

pH (A) Tie Line Length (TLL) (B) Concentration of whey (g) (C)

6.7 43.33 1.32

7 47.33 2

7.5 53.19 3

8 59.05 4

8.3 63.045 4.69

petroleum ether. The contents were mixed well and kept undisturbed for the formation of two layers. The upper ethereal layer was collected in a glass beaker. Extraction procedure was repeated twice using 15 mL of each solvent every time. The ethereal layer was collected in the same beaker and dried in a hot air oven at 102 ± 2 °C for two hours and cooled in a desiccator. This heating and cooling procedure was repeated till the beaker attain the constant weight (difference between the successive measurement should be <1 mg). 2.3. Partitioning in Aqueous Two-phase Systems (ATPS) The PEG 1000 rich top and trisodium citrate rich bottom phases were prepared separately for different tie lines and the desired phase volume ratio was obtained by adding the appropriate volumes of the phases. Feed compositions for the tie lines lies within the range of 24 (%, w/w) PEG 1000–14 (%, w/w) trisodium citrate to 34 (%, w/w) PEG 1000–14 (%, w/w) trisodium citrate. Experimentally determined equilibrium concentrations in the phases and the corresponding TLL at particular pH are given in Appendix (Table A.1). For experiments with whey, predetermined quantity of whey was added while preparing the bottom phase and the pH was adjusted using citric acid or NaOH. For all the experiments, the system weight was maintained as 10 g. The systems were mixed thoroughly at 150 RPM for 3 h prior to overnight incubation at 25 °C. Later volumes of the clear samples were recorded and separated using micropipettes. Total protein concentration was analyzed by Bradford assay with a-La as external standard. The interference of phase components in the analysis was neglected by using the corresponding phases without protein as blank system [13]. Individual protein concentration was analyzed using C-18 column (Shodex, C-18, RSpak RP 18-415) in High Performance Liquid Chromatography (HPLC) (Dionex, summit 3000). Two different mobile phases (A – 0.1% Trifluoro Acetic Acid (TFA) & B – 0.09% TFA in 90% Acetonitrile) at the flow rate of 1 mL/min were used to elute the proteins at different retention times based on their hydrophobicity using multi step gradient program [19] . The column was maintained at 25 °C and the absorbance was recorded at 214 nm using UV/visible detector. Prior to sample injection, column was equilibrated with 80% of A for 5 min. External standard addition method was employed to quantify the proteins in the phases. Standard curve was developed using the mixture of pure proteins (a-La, bLg and BSA). Both the phases were analyzed at two different dilutions to ensure the detection of trace proteins. The degree of separation was expressed in terms of partition coefficient, which is the ratio of desired protein concentration in

Table 2 Characterization of whey. Parameters

Present work

Literature value

Reference

BOD (ppm) COD (ppm) Total Protein (mg/mL) a-La (mg/mL) b-Lg (mg/mL) BSA(mg/mL) Lactose (mg/mL) Total solids %

48,000 ± 7 57,335 ± 5 5.49 ± 0.05 0.78 ± 0.03 2.86 ± 0.05 0.18 ± 0.01 47.2 ± 1.2 6.4 ± 0.8

35000–60,000 80,000–1,00,000 6 1.2 3.0 0.4 47 6.5

[4]

Minerals Sodium (ppm) Potassium (ppm) Calcium (ppm) Magnesium (ppm) Zinc (ppm) Iron (ppm)

586.5 ± 10 1940.5 ± 2 480 ± 7 115.7 ± 0.03 2.61 ± 0.02 1.27 ± 0.05

500 1500 600 100 1.5 0.6

[22]

[22]

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the top phase to that of the bottom phase. Since, the major contaminant is b-Lg; selectivity for a-La partitioning in a particular system is given as the ratio of the partition coefficient of a-La to that of b-Lg [12]. For partitioning studies with whey, recovery and purity of the desired proteins in the respective phases after extraction was calculated using Eqs. (1)–(4).

Recoveryða-LaÞ% ¼

Recoveryðb-LgÞ% ¼

ðC a-La Þtop phase  100 initial concentration of a-La added in the system ð1Þ ðC b-Lg Þbottom phase  100 initial concentration of b-Lg added in the system ð2Þ

Purity% of a-La in top phase ¼

ðC a-La Þtop phase  100 ðC total protein Þtop phase

Purity% of b-Lg in bottom phase ¼

ðC b-Lg Þbottom phase  100 ðC total protein Þbottom phase

ð3Þ

ð4Þ

where C is the concentration of protein in the respective phases.

2.4. Response Surface Methodology (RSM) Response Surface Methodology (RSM) was performed to study the interactive effect of the three independent variables, namely pH, TLL and concentration of whey on the partitioning of a-La and b-Lg. Partitioning conditions for the extraction of a-La and b-Lg from whey were optimized to achieve maximum purity% and recovery% of a-La in the top phase and that of b-Lg in the bottom phase. Experiments were designed by Circumscribed Central Composite Design (CCCD) which is rotatable and has circular, spherical or hyperspherical symmetry. Each factor is varied over five levels. Rotatability depends on the value of a chosen and can be calculated by considering number of variables. (a = [2k]1/4 = 1.682, where k = 3 is the number of variables). The central point was repeated six times to allow estimation of pure experimental error. Factor and levels of variables were chosen based on the knowledge gained during one factor at a time analysis. The value of the factors was coded and varied over 5 levels: plus and minus alpha (a = ±1.682) (axial points) plus and minus 1 (factorial points) and the center point. The uncoded values of independent variables at 5 different levels are given in Table 1. Experimental design, statistical and graphical analysis were performed with a statistical software ‘‘Design Expert 8.0’’. Statistical analysis of the responses was carried out using ANOVA (Analysis of variance). The responses were fitted into the quadratic model with highest coefficient of determination (R2). General quadratic model for the responses is given by Eq. (5). 2

2

2.5. Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS– PAGE) analysis

2

Yi ¼ b0 þ b1 A þ b2 B þ b3 C þ b11 A þ b22 B þ b33 C þ b12 AB þ b13 AC þ b23 BC

Fig. 1. Influence of whey concentration on partition coefficient (a-La, b-Lg and total protein), recovery (a-La and b-Lg), and purity% (a-L) in the PEG 1000–trisodium citrate system of TLL 53% (w/w). (1a) Partition coefficient of a-La at pH 7.5 (s)/8.0 (4) and b-Lg at pH 7.5 (d)/8.0 (N). (1b) System pH 7.5 – top phase a-La recovery% ( ) and purity% (s); bottom phase b-Lg recovery% ( ) and the total protein partition coefficient (kp h). (1c) System pH 8.0 – top phase a-La recovery% ( ) and purity% (s); bottom phase b-Lg recovery% ( ) and the total protein partition coefficient (kp h).

ð5Þ

where Yi is the predicted responses (i = 1–4), b represents the coefficients, A, B and C denotes the independent variables pH, TLL (%, w/w) and concentration of whey respectively. Significance of each term in the model was analyzed by computing F-value and P-value. The generated models were used for the numerical optimization to examine the feasible optimum conditions of the factors to satisfy the desired goal.

Whey and the top and bottom phases of the ATPS after partitioning were subjected to SDS–PAGE analysis in a gel of 13% resolving gel and 5% stacking gel. To avoid the interference of phase components in the analysis, the samples were dialyzed overnight at 4 °C against deionized water. Samples were boiled for 10 min in boiling water bath with equal volumes of sample loading buffer. Further, the samples were centrifuged at low RPM to settle the phase components. Separation process was carried out at a constant voltage of 100 V till the dye front reaches the bottom of

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Fig. 2. Variation of partition coefficient, top phase recovery (a-La and b-Lg) and purity (a-L) with TLL at system pH of 7.5 and 30% (w/w) whey. (a) Partition coefficient of a-La (s) and b-Lg (h). (b) Top phase recovery% ( ) and purity% of a-La (s), bottom phase recovery% of b-Lg ( ) and the total protein partition coefficient (kp h).

the gel. Gel was then stained with Coomassie Brilliant Blue R-250 for visualization of protein bands. 3. Results and discussions 3.1. Characterization of whey Partition coefficient of a specific biomolecule present in whey may not be identical as with the pure protein studies in the same system [21]. Whey contains diverse minerals and other complex biological substances. These contaminants can alter the equilibrium characteristics of the system and as a consequence the optimum conditions for recovery of individual proteins from whey may vary. Hence, the characterization of whey is highly essential. Chromatographic separations of proteins present in acid whey and the standard proteins are shown in Fig. A.1. Fat content was found to be 0.18% through Rose-Gottlieb method. Physical properties such as density (1.02043 g/cm3), viscosity (0.9009 mPa s) and surface tension (50.120 mN/m) of whey have also been obtained. Characteristics of whey used in the present work and the literatures values are given in Table 2. 3.2. Effect of pH and whey loading Change in system pH amends with the minor increase of PEG concentration, hydrophobicity and effective excluded volume of the top phase [23]. The ionizable groups of aminoacid side chains present on the protein surface are either protonated or deprotonated depending on the system pH which significantly affects the

stability and conformation of the biomolecules. Hence, net charge of the desired protein and also the contaminants varies with pH. The authors previously studied the partitioning behavior of the pure proteins (a-La and b-Lg) in PEG 1000–trisodium citrate system at different pH and found that the a-La partition coefficient increases with increasing pH. However, a remarkable increase in the a-La partition coefficient was noticed between the pH 7 and 8 [18]. Further, the conformation of a-La and b-Lg at acidic (<7) and basic pH (>8) are not favorable for differential partitioning in PEG 1000–trisodium citrate system [18]. Therefore in the present study, the influence of pH 7.5 and 8.0 on the partitioning was studied in the system of 28 (%, w/w) PEG 1000–14 (%, w/w) trisodium citrate system. Individual and total protein concentrations in the phases were quantified and the calculated partition coefficient (Fig. 1a), recovery and purity of a-La in top phase and b-Lg recovery in bottom phase (Fig. 1b and c) were plotted. Partition coefficient of a-La is more at pH 8.0, whereas purity is less. This could be due to the partitioning of b-Lg to the top phase in systems of pH 8.0 (Fig. 1a). The pH maintained in the system is above the isoelectric point of a-La and b-Lg and therefore the proteins gained a net negative charge [13]. The net negative charge of proteins and the prevalence of trivalent citrate anions in the system increases with increasing pH. Besides, the trivalent citrate ions have a higher potential to salting-out the proteins and hence the proteins are expelled to the PEG rich phase [24]. Brownstein [25] stated that the formation of hydrogen bond between polyether oxygen atoms of PEG and the surrounding water molecules makes the PEG ether oxygen atom partially electronegative and the hydrogen atom (water) slightly electropositive. In addition, the PEG molecule interacts with water in such a way that the repeating hydrophobic ethyl groups have minimal exposure to water molecules [26]. Thus, the salted out proteins form hydrophobic interaction with the ethyl group of PEG. However, surface hydrophobicity and net negative charge of a-La is more than that of b-Lg. Hence, PEG prefers to form hydrophobic interaction with a-La. Thermodynamically, the formation of hydrophobic interaction is entropically driven (DS > 0) which was experimentally verified by Alcantara [27]. The Gibbs free energy (DG) of formation of hydrophobic interaction between PEG and a-La is negative, which indicates that the reaction is spontaneous and endothermic (DH > 0). Recovery and purity of a-La in top phase and b-Lg in bottom phase was observed to be better in systems with pH 7.5 (Fig. 1b and c). Fat, lactose and traces of proteins was observed to precipitate at the interface and the thickness of interface was also observed to increase with increasing system pH. It was also observed that only a negligible amount of a-La was partitioned to the bottom phase at pH 8.0, which reveals that most of the added a-La is precipitated at the interface. The contaminant proteins may acquire more net negativity and hydrophobicity than a-La at pH 8.0 and compete with a-La to form hydrophobic interaction with PEG. Based on the experimental results and analysis it was concluded that, the second stage of ATPE using fresh bottom phase or addition of NaCl as additive would not increase the recovery or purity of aLa in top phase. Hence, it is suggested that purification of a-La from the PEG rich phase could be possible with the separation techniques like size exclusion chromatography. Addition of the natural complex biomolecule source into ATPS has been demonstrated to change the equilibrium characteristics of phase components as well as the partition coefficient of the biomolecule. Fig. 1a–c clearly show the impact of whey concentration on partition coefficient, recovery and purity of the partitioned proteins. This scenario is fairly acceptable and the extent of variation greatly depends on the concentration of impure biomolecules. Biomolecule content in the system increases with whey loading and above the solubility limit of the phases the excess proteins

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Fig. 3. Chromatographic separation of proteins in top and bottom phase at 53.19 (%, w/w) tie line with 30 (%, w/w) whey and at pH 7.5 (center point of RSM studies).

it was concluded that PEG 1000–trisodium citrate is the most suitable system for the real-time simultaneous extraction of a-La and b-Lg from whey when compared to the other ATPSs reported in the literature. 3.3. Effect of TLL on the partitioning of a-La and b-Lg

Fig. 4. Effect of phase volume ratio on top phase recovery% ( ) and purity% of aLa (s), bottom phase recovery% of b-Lg ( ) and the total protein partition coefficient (kp h) in 53.19 (%, w/w) TLL system of pH 7.5 and 30% (w/w) whey.

start precipitate at the interface [28]. Beyond 30 (%, w/w) whey loading the recovery of a-La was observed to decrease and which shows the attainment of maximum solubility of a-La in the phases. Simultaneous increase of impurities concentration in the system and their competence with a-La to occupy free volume available in the top PEG rich phase might be the cause for a-La precipitation at the interface [29]. A thick interface precipitation was observed for all the experiments conducted at pH 8.0 with PEG 1000–trisodium citrate system. Moreover, the less recovery of proteins in both the phases suggests that the system conditions are not in favor for the extraction of a-La from whey. Though, several researchers have reported the ATP separation of pure whey proteins (a-La, b-Lg and BSA) in PEG-ammonium sulfate/potassium phosphate and PVP-potassium phosphate systems, very limited research work have been carried out with whey. Nevertheless, the selectivity of 14 (%, w/w) PEG 1500–18 (%, w/w) potassium phosphate system at pH of 7 and at an equilibration temperature of 25 °C for the partitioning of a-La from whey protein isolate was reported as 318 [12]. a-La selectivity of 2252.96 was obtained in the present work with the conditions of: 28 (% w/w) PEG 1000–14 (%, w/w) trisodium citrate system (TLL 53.19 (%, w/w)), pH 7.5, 30 (%, w/w) whey loading and equilibration temperature of 25 °C. Based on these observations,

Increase in tie line length is associated with the increase of top phase polymer and bottom phase salt concentrations and therefore variation in the physical properties of the system as well as the available free volume in the phases were eminent. Effect of TLL (47–59 (%, w/w)) on the partitioning was studied at a system pH of 7.5 and the results are presented in Fig. 2a and b. The increasing trend of partition coefficient with TLL is due to the decrease of protein concentration in the bottom phase (Fig. 2a). Maximum recovery of a-La was observed at 53.19 (%, w/w) TLL and further increase in TLL does not favor the partitioning of a-La to the top phase. A

Table 3 Experimental design for 5 levels of CCRD for 3 factors with 4 responses. Run

Factors pH (A)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

7.00 8.00 7.00 8.00 7.00 8.00 7.00 8.00 6.66 8.34 7.50 7.50 7.50 7.50 7.50 7.50 7.50 7.50 7.50 7.50

Responses TLL (%, w/w) (B)

Whey (g) (C)

47.33 47.33 59.05 59.05 47.33 47.33 59.05 59.05 53.19 53.19 43.33 63.05 53.19 53.19 53.19 53.19 53.19 53.19 53.19 53.19

2.00 2.00 2.00 2.00 4.00 4.00 4.00 4.00 3.00 3.00 3.00 3.00 1.32 4.68 3.00 3.00 3.00 3.00 3.00 3.00

Recovery%

Purity%

a-La

a-La

(Y1)

b-Lg (Y2)

(Y3)

b-Lg (Y4)

75.54 81.45 69.53 65.92 75.18 72.43 66.16 49.62 85.66 81.60 60.41 36.01 87.28 74.17 90.05 91.01 92.00 90.88 91.00 91.05

84.78 95.38 84.98 47.84 49.65 50.28 79.27 30.93 98.28 68.88 60.97 43.00 86.65 45.96 96.80 95.90 96.90 96.90 96.50 95.70

42.91 51.29 69.05 47.45 54.98 56.26 91.78 54.27 69.19 41.49 46.60 60.24 67.20 78.00 95.25 94.34 93.91 89.51 88.09 90.93

29.30 59.57 50.46 47.04 51.44 42.00 79.43 38.33 71.70 58.84 26.21 48.83 48.91 56.32 77.07 76.99 73.25 72.43 77.32 75.69

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Table 4 Overall goodness of fit evaluation for the responses. Responses

R2

Adj. R2

Pred. R2

Std. dev

Model F -value

Model P -value

Y1 Y2 Y3 Y4

0.9981 0.9994 0.9668 0.9903

0.9964 0.9988 0.9475 0.9816

0.9874 0.9962 0.8945 0.9519

0.92 0.79 4.32 2.28

582.72 1811.07 49.96 113.33

<0.0001 <0.0001 <0.0001 <0.0001

similar observation was noted in the pure protein studies of a-La in PEG 1000–trisodium citrate system [18]. Decrease in recovery of both the proteins at higher TLL suggests that the proteins are excluded from both the phases and precipitated at the interface due to unfavorable system conditions. Recovery and purity suggests that PEG 1000–trisodium citrate system of pH 7.5 and 53.19 (% w/w) TLL is the most appropriate choice for the simultaneous effective extraction of a-La and b-Lg from whey. Chromatographic separation of proteins partitioned to the top and bottom phase is shown in Fig. 3.

3.4. Effect of phase volume ratio (Vr) Recovery and purity of biomolecules in the ATPS can be manipulated by altering the phase volume ratio of the system since there is a change in the free volume available for the partitioning [30]. The partition coefficient of pure proteins has not been significantly affected by the change in volume ratio of the systems [10]. However, either increase of protein concentration or due to the complex biological mixture as source, there may be a change in the partition coefficient [31]. Solubility of the biological components in the particular phase has been affected by change in volume of the phases which in turn depends on the physico-chemical properties of the biomolecule. Impact of volume ratio on the recovery and purity of a-La and b-Lg was studied at 53.19 (%, w/w) TLL with 30 (%, w/w) whey and at a system pH of 7.5 (Fig. 4). Increase in volume ratio from 0.6 to 2.9 results in the minor decrease of bottom phase protein recovery (a-La, b-Lg and total protein). Simultaneously, the increase of top phase volume (2.8– 5.8) resulted in slight increase of b-Lg and total protein recovery in the PEG rich phase. However, a-La recovery and purity increases when the volume ratio is increased from 0.6 to 1.0. Further, increase in the top phase volume resulted in the decrease of recovery and purity of a-La. On examining these effects, we have come to a conclusion that the occurrence of hydrophobic interaction between contaminant (other hydrophobic) proteins and PEG is increasing in systems of volume ratio above one. The cause of this phenomenon is highly complicated since the interaction of biomolecules from whey with the system components and the proteinprotein interaction has been altered by dilution of the whey [21,32]. The milk used for the whey preparation contains 3.51% fat and whey has 0.18% fat. Therefore, the system of phase volume ratio one with 3 g of whey contains 5.4 mg of fat. After equilibration, fat content in the phases were analyzed. Top phase contains 40.61% of added fat and 22.5% fat was partitioned to the bottom salt rich phase. This implies that the remaining 36.89% fat was precipitated at the interface. Interfacial partitioning of whey fat in PEG-potassium phosphate salt systems has been reported by Anandharamakrishnan et al. [33] and however, there is no quantitative information of fat present in the phases. Exclusion of fat from the phases would be an added benefit, while separating the partitioned biomolecules from the phases.

Lack of fit test F-value

P-value

3.39 3.4 1.92 1.32

0.1033 0.1007 0.2445 0.3855

3.5. Analysis of the responses (experiments designed by RSM) by statistical methods Analysis of experimental results reveals that the independent variables viz., pH, TLL and concentration of whey have major effect on the partitioning behavior of proteins in whey. Maximum recovery and purity of a-La can be achieved by eliminating all the contaminants including b-Lg to the bottom salt rich phase. With this objective, optimization of these independent variables was carried out using RSM. The experiments were designed by ‘‘Design Expert’’ through CCCD and the experimentally determined responses corresponding to the design points are reported in Table 3. Through regression analysis, the obtained responses were best represented by the following second order polynomial Eqs. (6)–(9). The effect of independent variables and the interaction between these variables on the responses were analyzed using the developed equations.

Y1 ¼ 91  1:74A  6:91B  3:74C  2:64A2  15:16B2  3:66C 2  2:91AB  2:7AC  1:29BC

ð6Þ

Y2 ¼ 96:45  9:06A  4:93B  12:54C  4:58A2  15:75B2  10:69C 2  12:09AB  2:65AC þ 7:20BC

ð7Þ

Y3 ¼ 90:34  7:03A þ 5:86B þ 4:74C  12:4A2  13:08B2  6:3C 2  8:6AB

ð8Þ

Y4 ¼ 75:78  3:32A þ 5:2B þ 2:73C  3:74A2  13:55B2  8:22C 2  8:17AB  9:67AC þ 1:96BC

ð9Þ

where Yi is the predicted response. The statistical analysis method ‘‘ANOVA’’ was used to estimate the statistical parameters and to determine the impact of independent variables on the dependent variable in a regression analysis. The coefficient of determination (R2), adjusted R2 and the predicted R2 were estimated to test the global fitness of the model. The greater coefficient of determination (R2 > 0.9) and model F-value for all the four models authenticates the significance of the proposed equations (Table 4). The efficiency of the model to represent the experimental data must be cross checked with the adjusted R2 value since there is a possibility of getting greater R2 value by the manipulation of the model. Adjusted R2 value of all the four models (Table 4) is greater than 0.9 and those values are also lesser than R2 value, which shows that the model R2 value has not been improved by the addition of more inappropriate terms to the model [34]. Lesser (0.01%) P-value shows there is less than 0.01% chance of error in model Fvalue due to noise (Table 4). Lack of fit test compares residual error to the pure error which was obtained by repeating the central point six times. P-value of lack of fit test for all the four models is greater than 0.05 and F-value is also less which states that the model can be satisfactorily used to predict the responses. F-Value of quadratic term of TLL (B2) is greater for all the four responses which convey that quadratic term of TLL has major

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Factors pH (A)

1

2

3

4

5

Responses TLL Wt% (B)

Whey conc (C)

Desirability

Recovery%

Purity%

a-La (Y1)

b-Lg (Y2)

a-La (Y3)

b-Lg (Y4)

Maximize the recovery% (Y1) and purity% (Y3) of a-La Predicted values 7.34 53.5 Experimental values

3.10

91 91 ± 1

98 98 ± 0.54

92 90 ± 1.2

77 82 ± 0.12

Maximize the recovery% (Y2) and purity% (Y4) of b -Lg Predicted values 7.0 55.52 Experimental values

3.23

86 87 ± 0.3

99 99 ± 1

89 90 ± 1.47

81 83 ± 1.05

Maximize the recovery% of a-La (Y1) and b-Lg (Y2) Predicted values 7.52 51.95 Experimental values

2.51

93 93 ± 0.8

101 99 ± 1.8

85 82 ± 1.34

71 68 ± 0.98

Maximize the purity% of a-La (Y3) and b-Lg (Y4) Predicted values 7.16 55.79 Experimental values

3.51

84 86 ± 0.5

92 93 ± 1.2

93 95 ± 2.6

81 82 ± 1.02

90 89 ± 0.9

99 96 ± 0.5

92 96 ± 2

79 76 ± 1.4

0.859

0.855

0.94

0.808

Maximize the recovery% and purity% of a-La (Y1,Y3) and b-Lg (Y2,Y4) Predicted values 7.23 54.13 3.06 Experimental values

0.842

major role in the determination of recovery of these proteins from whey. Statistical analysis for the response Y3 (purity% of a-La) shows that the interaction of concentration of whey (C) with pH (A) and TLL (B) are not significant since the prob > F values are greater than 0.1. However, the interactive term AB is significant (B2 > A2 > AB > A > C2 > B > C). The interaction of pH with whey concentration and TLL was observed to be more significant than BC in determining the purity of b-Lg in bottom phase (Y4) (B2 > C2 > AC > AB > B > A2 > A > C > BC). Further, the interactive effects of variables on the responses are represented graphically as surface plots (Fig. A.2). The Surface plots of all the four responses were plotted by varying two of the operational parameters while retaining the third factor at zero level. The interactive effect of variables on the responses is denoted as contour levels. The contours are circular in shape which confirms the presence of interaction between the variables. Further, the central region of the contours is within the design space. This indicates the maximum value of the responses is within the experimental domain. It is also clear that the maximum recovery and purity of both proteins can be achieved within the experimental domain, whereas the exact optimum condition for individual response varies. Therefore, optimization tool was used to find the appropriate condition for the responses with a specific goal.

3.6. Optimization of the partitioning through desirability approach

Fig. 5. Estimated desirability contour plots for the maximization of all the four responses. (a) At constant TLL (%, w/w) of 54.13 and (b) at constant 3.06 g of whey in 10 g system.

impact on the responses. The order of significance of variables on the response Y1 is quadratic term of TLL, TLL, quadratic term of whey and whey concentration followed by the interactive effects AB, AC and BC (B2 > B > C2 > C > A2 > AB > AC > A > BC). Recovery of b-Lg (Y2) from whey depends on the quadratic term of TLL, whey concentration, interactive effect of AB and trailed by the system pH, interactive effect of BC (B2 > C > C2 > AB > A > BC > B > A2 > AC). This clearly shows that the interactive effect of pH and TLL plays

In a multi response system, optimization of any one of the responses often results in the optimum condition that varies disproportionality from the optimal factor of other responses. In the optimization of simultaneous biomolecules purification process, where the responses are interdependent, it is mandatory to find the single feasible optimal condition for the desired goal. Hence the multi-response optimization, which is based on the desirability objective function, was performed through numerical optimization approach. Desirability based optimization is a relatively new powerful and efficient method for the optimization of multi-response processes [34,35]. This involves the simultaneous optimization of the quadratic models and transformation of the multiple responses (Yi) to a weighted mean average of the individual desirability function (di) (Eq. (10)). The inputs are the model equations, weightage, target value or the upper and the lower bounds. The desirability (D and d) ranges from zero to one (least to most desirable, respectively).

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Fig. 6. (a–d). Estimated surface plots for maximization of all the four responses.

D ¼ ðd1  d2  . . . . . . :dn Þ

Fig. 7. SDS–PAGE profile of molecular marker (lane 1), whey (lane 2), top phase (lane 3) and bottom phase (lane 4) samples at the optimized condition obtained for maximization of all the four responses.

1=n

ð10Þ

where n is the number of responses in the process. The overall function (D) becomes zero even any one of the responses or factors falls outside their desirability range [36]. The overall desirability function shape for different goals may be modified by assigning different weightage to the individual responses and variables. In the present work, the partitioning of a-La to the PEG rich phase and b-Lg to salt rich phase was targetted and accordingly the recovery and purity of the proteins in the respective phases were considered as responses. The goal of optimization may vary depending on the requirement of the process implementation, whether the desired protein has to be partitioned with maximum purity or recovery. Since the partitioning process is equilibrium governed, the responses purity and recovery are higly inter dependent. The optimum conditions and the desirability for different goals are reported in Table 5. Desirability contour (Fig. 5a and b) and response surface plots (Fig. 6a–d) for maximization of all the four responses was plotted by fixing one of the variable as constant. The maximum curvature of the surfaces and circular contour is near the center of the graphs shows that the optimum responses are within the selected variable range. Moreover, the desirability of the responses in that experimental domain can be predicted using Fig. 5. Feasibility of these optimized conditions and the predicted responses were validated experimentally (Table 5) and the experimental values are found to be in agreement with the predicted values. SDS–PAGE analysis

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of top and bottom phase samples of 54.13 (%, w/w) TLL at pH 7.23 with 3.06 g of whey (optimum condition obtained for maximization of all the four goals) was carried out. Separation of proteins presents in molecular marker (lane 1), whey (lane 2), top phase (lane 3) and bottom phase (lane 4) shown in Fig. 7 clearly indicates the enrichment of a-La in top phase and the presence of b-Lg and BSA in bottom phase. The whey proteins (a-La and b-Lg) partitioning in PEG 1000–trisodium citrate system process has been successfully described by the response equations (Eqs. (6)–(9)), using which, the process can be modified and operated for the preferred goals with complete control over the responses. Chen [13] reported the highest recovery of 71.2% a-La in 14 (%, w/w) PEG-potassium phosphate system whereas Alcantara et al. [11] achieved the maximum recovery a-La of 81.1% in 13 (%, w/w) PEG-potassium phosphate system with 0.35 mol/L of NaCl as additive in three stages of extraction. However, the proposed system, extracts a-La with greatest recovery in a single step extraction process. Therefore, the suggested ATPS and optimum conditions of the variables could be useful to study the scale-up parameters and also useful for the industrial scale application.

[5]

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[7]

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[11]

[12]

4. Conclusions [13]

The suitability of the proposed PEG 1000–trisodium citrate system for the differential partitioning of a-La to the top phase and the contaminants including (b-Lg) to the bottom phase has shown with the ample experimental data. The surface hydrophobicity of a-La plays a major role in the formation of hydrophobic interaction with PEG, which is the major cause for the separation of a-La to the PEG rich phase. CCCD based full factorial experimental design and the analysis of responses shows tie line is the most significant variable in determining the recovery and purity of the partitioned whey protein. With an aid of proposed equations for the responses and the optimization tool, optimum conditions for various goals were obtained and are validated experimentally. As a conclusion, the proposed eco-friendly system is more proficient and cost-effective for the single step concurrent extraction of whey proteins. Acknowledgement The authors acknowledge the grant (Scheme number: BT/ PR11935/PID/06/456/2009 & October 31, 2011) from the Department of Biotechnology (DBT), Government of India, for this research work.

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