Optimization of protein recovery by foam separation using response surface methodology

Journal of Food Engineering 79 (2007) 598–606 www.elsevier.com/locate/jfoodeng

Optimization of protein recovery by foam separation using response surface methodology S. Aksay a

a,b

, G. Mazza

a,*

Bioproducts and Bioprocesses Research Program, Pacific Agri-Food Research Centre, Agriculture and Agri-Food Canada, 4200 Hwy 97, Summerland, BC, Canada V0H 1Z0 b Department of Food Engineering, University of Mersin, 33342 C¸iftlikko¨y, Mersin, Turkey Received 5 July 2005; accepted 13 February 2006 Available online 17 April 2006

Abstract Response surface methodology was used to optimize conditions for protein recovery using bovine serum albumin (BSA) as a model protein in a foam separation system. Factors examined were pore size, initial protein concentration, and airflow rate. The size of bubbles produced using frits of different pore size were measured by a photographic method and quantitatively correlated with key process variables. Bubble size was affected significantly by porosity of frits, and the average bubble diameter was 1.36, 2.14, 1.93, 2.44, and 2.76 mm for 1, 25, 50, 75, and 100 lm frit porosity, respectively. The process time increased as the pore size and concentration increased and decreased with increasing airflow rate. Maximum protein recovery (98%) was obtained with 1 micron pore size frit, and 200 mg/L initial protein concentration. Protein enrichment increased significantly with decreasing initial protein concentration. Crown Copyright Ó 2006 Published by Elsevier Ltd. All rights reserved. Keywords: Foam separation; Pore size; Protein recovery; Enrichment ratio; Optimization

1. Introduction Foam fractionation, an adsorptive bubble separation method, has been shown to be a feasible technique for the separation and/or concentration of a variety of surface active biological materials. The strong amphipathic nature of proteins and enzymes, resulting from their mixture of polar and non polar groups, causes them to be preferentially adsorbed at the gas–liquid interface (Uraizee & Narsimhan, 1990). Foam fractionation could therefore be used to separate and concentrate proteins. The separation and recovery of proteins is usually carried out by chromatography, ultrafiltration and precipitation techniques. In recent years the relatively high cost of these methods has led to greater interest in less costly purification alternatives. Foam fractionation is simple, rela-

*

Corresponding author. Tel.: +1 250 494 6376; fax: +1 250 494 0755. E-mail address: [email protected] (G. Mazza).

tively inexpensive and in commercial practice it has significant potential for reducing the high cost of protein recovery (Bhattacharjee, Kumar, & Gandhi, 1997; Brown, Narsimhan, & Wankat, 1990; Noble, Brown, Jauregi, Kaul, & Varley, 1998; Prokop & Tanner, 1993). Foam fractionation also offers the advantages of using only small quantities of chemical additives, requiring no thermal energy, and providing for continuous processing (Wong, Hossain, & Davies, 2001). During foam fractionation, separation is achieved by bubbling gas through a dilute protein solution. Surface active proteins are adsorbed to the gas–liquid interface of the bubbles and raised bubbles of the liquid form a protein rich foam layer in the column. The foam can be collected, collapsed and a protein rich solvent (foamate) is obtained. The enhanced protein concentration in the foam is known to be a combined effect of interfacial adsorption and foam drainage, and it is assumed that adsorption occurs only while the bubbles rise in the liquid pool (Bhattacharjee et al., 1997).

0260-8774/$ - see front matter Crown Copyright Ó 2006 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2006.02.024

S. Aksay, G. Mazza / Journal of Food Engineering 79 (2007) 598–606

An important requirement of foam fractionation is that all molecules of interest (i.e., proteins and peptides) must have surface activity in order to form a stable foam (Lockwood, Bummer, & Jay, 1997). The surface activity of a protein is dependent on its physicochemical characteristics, and environmental conditions such as ionic strength, pH, presence of detergents, salts, sugars and other additives (Brown, Kaul, & Varley, 1999). The performance of foam separation process can be described by the enrichment ratio (ER), mass recovery percent (MR) and separation ratio (SR) (Gehle & Schugerl, 1984; Lambert et al., 2003; Varley, Kaul, & Ball, 1996). The enrichment ratio can be described as ER ¼

Protein concentration in foamate . Protein concentration of the initial solution

ð1Þ

Mass recovery percent is the percentage of the recovered mass of protein in foamate in relation to initial solution, and is calculated as MR ¼

½Proteinfoamate  V foamate  100. ½Proteininitial  V initial

column containing ovalbumin and correlated enrichment ratio with bubble size and void fraction. However, all the above works were related to bubble size and its distribution, and only a few studies have investigated the effect of pore size on the performance of foam fractionation of bovine serum albumin (Crofcheck & Gillette, 2003; Uraizee & Narsimhan, 1996). While the importance of pore size is known, its effect on the process efficiency of foam fractionation has not been adequately investigated. In this investigation, the influences of operational factors which are pore size, airflow rate, and initial protein concentration on bubble size and foam fractionation were investigated. Response surface methodology (RSM) which is a collection of statistical and mathematical techniques useful for developing, improving and optimizing processes (Myers & Montgomery, 1995) was applied to better define the optimal process conditions of foam fractionation in a batch system. 2. Materials and methods

ð2Þ

The separation ratio is calculated as SR ¼

599

Protein concentration in foamate . Protein concentration of residual solution in the column ð3Þ

In foam fractionation, operational parameters (column height, bubble size, foam layer height, gas flow rate, and type of gas used for sparging) and protein solution conditions (type of protein, solution pH, ionic strength, protein concentration, and temperature) are known to affect the protein separation process (Ahmad, 1975; Banerjee, Agnihotri, & Bhattacharya, 1993; Rubin & Gaden, 1962; Uraizee & Narsimhan, 1990). The bubble size and distribution are important factors affecting the separation of proteins and only a few studies have been conducted. Du, Ding, Prokop, and Tanner (2001) described the bubble size as key variable for predicting the ability to separate and/or concentrate proteins in a foam fractionation process. They reported that bubble size is affected by solution and gas–liquid surface properties, such as solution pH, protein concentration, surface viscosity, surface tension, and some operational parameters, such as the superficial gas velocity and size of sparger. Saleh and Hossain (2001) determined the bubble size distribution in a foam fractionation column to identify the underlying mechanism and design for an improved foam fractionation process. Wong et al. (2001) measured the bubble size in a continuous system to calculate the interfacial area and reported that the smaller bubbles, which were obtained with lower feed concentration, lower air velocities and pH close to the isoelectric point, gave better protein enrichment. Du, Prokop, and Tanner (2002) measured the bubble size distribution in a continuous foam fractionation

2.1. Experimental setup Fig. 1 shows a schematic diagram of the experimental setup. A transparent acrylic pipe with 28.25 mm internal diameter and 360 mm length was used as the foam fractionation column. Stainless steel frits of 3.175 mm thickness, 25.4 mm diameter with various porosities (1–100 lm) were purchased from Applied Porous Technologies Inc. (Tarriffville, Connecticut, USA) and fitted to the bottom of the column. An ADM 1000 Intelligent Flowmeter (J&W Scientific, Folsom, California, USA) was used to control the airflow rate into the column. Air was humidified by passing through water in a washing bottle (humidifier) before entering the column. A beaker was used to collect the foam at the top of the column.

Fig. 1. Schematic diagram of the experimental setup.

600

S. Aksay, G. Mazza / Journal of Food Engineering 79 (2007) 598–606

2.2. Foam separation Aqueous solutions of bovine serum albumin (BSA) (Sigma A8551, Sigma Chemical Co., St. Louis, MO, USA) from 50 to 250 mg/L in distilled water were used as model protein solutions. The pH of the solutions was 5.0 ± 0.1 and it was not adjusted. In each run, the same volume of BSA solution (100 mL) was used. Pressurized air was introduced to the solution in the column through the steel frit. The foam produced was collected until no more foam was seen. The foam collected in the beaker was stirred manually to help collapse it and stored at 4 °C (for 12 h) for further collapse before analysis. The processing time for a run was defined as the time when the first drop of foam entered the foam collector until the time when no more foam was seen. All runs were carried out at ambient temperature (23 ± 1 °C). The protein concentration of the foamate was measured spectrophotometrically at a wavelength of 280 nm (Brown et al., 1990), and calculated by using a BSA standard curve. Process performance was evaluated by measurement of the protein enrichment ratio (ER, Eq. (1)) and mass recovery (MR, Eq. (2)) of BSA in the foamate. 2.3. Bubble size measurement and analysis of process variables

the camera and the cross-section of photograph in the column are shown in Fig. 2. The pictures of bubbles were transferred into a computer. The diameter of the bubbles was then measured using image analysis software ImageJ Version 1.32 (National Institute of Health, Maryland, USA). To avoid the stretching effect of cylindrical column, the axial distance of bubble according to column was measured as bubble diameter (di). The mean diameter (d32) of the bubbles was calculated using the following equation (Du et al., 2001): Pn 3 di d 32 ¼ Pi¼1 n 2 i¼1 d i where n is the number of bubbles measured in the picture. Bubble size was measured at the start of a run. In a single picture about 33–248 bubble size measurements were obtained. Bubble size was determined for various combinations of frit pore size, initial protein concentration, and airflow. Analysis of variance (ANOVA) and Tukey multiple range tests were used to compare results for bubble size using the different combinations of variables. 2.4. Optimization of process variables by response surface methodology (RSM)

The size of bubbles was measured by taking photographs with a digital camera (Nikon D1, Tokyo, Japan) with a 60 mm objective lens placed about 11 cm from the column (Wong et al., 2001). The shutter speed was set at 1/125 s and a Nikon SB-28DX model flash was used with about 45° indirect reflection from a white back zone. The photograph was taken at the middle of the liquid section of column. For scaled measurements a 3.57 mm diameter steel bead, held in place with a thin (1.5 mm in diameter) wire, was placed at the same section of the column. The position of the steel ball in the column, the position of

Optimization of the independent variables, pore size (X1), initial protein concentration (X2) and airflow (X3) for processing time (YT), percent protein recovery (YR) and enrichment ratio (YE) was performed by RSM. The central composite design was applied for three process variables each at five equidistant levels with 18 runs which contain four replicates at the center point (Myers & Montgomery, 1995). Pore sizes were 1, 25, 50, 75, and 100 lm; initial protein concentrations were 100, 150, 200, 250, and 300 mg/L BSA; and airflows were 50, 100, 150,

Column flash

11 cm Steel ball

w

d White board

Camera

h a

b

c

Fig. 2. Position of reference steel ball (a), position of column and camera with flash (b), cross-section of photograph (c) in the column (w: width, h: height of photograph, d: diameter of the bubble).

S. Aksay, G. Mazza / Journal of Food Engineering 79 (2007) 598–606 Table 1 Experimental codes, ranges and levels of the independent process variables Levels

Pore size (lm) Concentration (mg/L) Airflow (mL/min)

X1 X2 X3

2

1

0

+1

+2

1 100 50

25 150 100

50 200 150

75 250 200

100 300 250

200, and 250 mL/min. The range and levels of independent variables and codes are given in Table 1. The optimization data were fitted to a second order polynomial regression model which contained the coefficient of linear, quadratic and two factors interaction effects. The model equation of response (Y) of the three process variables (X1, X2, X3) is Y ¼ b0 þ

3 X i¼1

bi X i þ

3 X

bii X 2i þ

i¼1

2 3 X X

bij X i X j

i¼1 j¼iþ1

where Y is the dependent variable, b0 is the constant coefficient (intercept), bi is the linear coefficient (main effect), bii is the quadratic coefficient and bij is the two factors interaction coefficient. 2.5. Statistical analysis All data were analyzed using SAS (1996–2000) (SAS Institute Inc., Cary, North Carolina, USA). The 3D response graphs of predicted values of model of RSM were plotted using SigmaPlot v. 8.02 (2002) (SPSS Inc., Chicago, Illinois, USA).

601

Table 2 Analysis of variance for the effect of pore size, initial protein concentration, and airflow rate on bubble size Process variable

Sum of Squares

df

Mean square

F-value

Significance

Frit pore size Protein concentration Airflow rate Error

10.412 0.1794 0.3204 1.7501

4 2 2 36

2.6031 0.0897 0.1602 0.0486

53.55 1.84 3.30

<0.0001 0.1727 0.0485

Total

12.6625

44

df: Degree of freedom.

concentration, and airflow on bubble size. As can be noted the frit pore size had a highly significant effect on the bubble size (p 6 0.01), and increasing the pore size resulted in bigger bubbles at all protein concentrations. However, bubble size was not significantly affected by the initial protein concentration or airflow (p > 0.01). The smallest bubbles (1.36 mm in diameter) were obtained with 1 lm frit pore size and the largest bubbles (2.79 mm in diameter) were obtained with a 100 lm pore size frit (Fig. 3). The differences in bubble diameter using 25 and 50 lm pore size frits and 25 and 75 lm pore size frits were not significant at p > 0.01. Airflow effect on the bubble diameter was not significant at p 6 0.01. Wong et al. (2001) has also reported that superficial gas velocity had negligible effect on the average bubble size and size distribution. Photographs of bubbles in the column at different pore size and airflows are shown in Fig. 4. 3.2. Optimization of process variables by response surface methodology

3. Results and discussion 3.1. Effect of process variables on bubble size Table 2 presents the results of the analysis of variance for the determination of effect of frit pore size, protein

The results of the analysis of variance (ANOVA) of the second order response surface model fitting and the effect of independent process variables on process time, percent protein recovery and enrichment ratio are presented in Table 3.

3 d

Bubble diameter (mm)

2.5 cd

2

bc b

1.5 a

1

0.5

0 1

25

50

75

100

Frit pore size (μm) Fig. 3. Changes in mean bubble diameter with different frit pore sizes (different letters in the bar graph indicates significant difference at p 6 0.01).

602

S. Aksay, G. Mazza / Journal of Food Engineering 79 (2007) 598–606

Fig. 4. Effects of different frit pore size and airflows at 150 mg/L BSA concentration on bubble size and distribution.

Table 3 Response surface model fit and effect of independent process variables on process time, percent protein recovery and enrichment ratio

Linear Quadratic Cross-product Model R2 Process variables Frit pore size Concentration Airflow ns * **

Process time

% Protein recovery

Enrichment ratio

<0.0001 0.0046 0.0142 <0.0001 0.9700

0.0094 0.8864 0.001 0.0001 0.9302

0.0002 0.0062 0.001 0.001 0.9305

**

0.0004 0.0022** <0.0001**

*

0.0316 0.0003** 0.9588ns

ns

0.415 0.0002** 0.1885ns

Not significant. Significant at p 6 0.05. Significant at p 6 0.01.

The ANOVA model for process time showed that linear and quadratic effects were more significant (p 6 0.01) than the cross-product effect (significant at p 6 0.05). For percent protein recovery, linear and cross-product effects were significant (p 6 0.01), but quadratic effect was not significant. Linear, quadratic and cross-product effects were all significant (p 6 0.01) for enrichment ratio. The model fit was highly significant (p 6 0.01) with reasonably high correlation coefficients 0.97, 0.93 and 0.93 for all three response variables, process time, protein recovery and enrichment ratio, respectively. The independent process variables showed different effects on each response variable. In the model, frit pore size, initial protein concentration and airflow rate were highly significant (p 6 0.01) for process time. For protein recovery, the effect of initial protein concentration was highly significant at p 6 0.01, while frit pore size was significant at p 6 0.05 and airflow had no significant effect. The enrichment ratio was significantly affected by protein concentration only (p 6 0.01).

The following empirical regression Eqs. (4)–(6) as a function of the independent process variables frit pore size, initial protein concentration and airflow rate using the linear, quadratic and interaction coefficient were derived by the RSM model for process time, protein recovery, and enrichment ratio, respectively. Y Time ¼ 8:99 þ 0:29X 1 þ 0:22X 2  0:11X 3  0:000218X 12  0:000495X 22 þ 0:000405X 32 þ 0:00115X 1X 2  0:00155X 1X 3  0:000425X 2X 3

ð4Þ

Y Recovery ¼ 38:41  0:4X 1 þ 0:76X 2  0:07X 3  0:000789X 12  0:001978X 22 þ 0:000157X 32 þ 0:001057X 1X 2 þ 0:000515X 1X 3  0:0000245X 2X 3

ð5Þ

Y Enrichment ¼ 13:67 þ 0:06X 1  0:1X 2  0:03X 3 þ 0:000011324X 12 þ 0:00025X 22 þ 0:000044152X 32  0:000194X 1X 2  0:000096X 1X 3 þ 0:000067X 2X 3;

ð6Þ

where Y is the response value, X1, X2 and X3 are frit pore size, initial protein concentration and airflow, respectively. The predicted values calculated by using the above empirical regression equations were similar to the experimental values obtained from the RSM design (Table 4). These predicted values and the correlation coefficients of the model clearly illustrate the reliability of the model. The response surface graph for process time as a function of frit pore size, concentration, and airflow is shown in Fig. 5. The process time increased as the pore size and protein concentration increased, and decreased with increasing airflow. When the pore size was 100 lm and the airflow was increased from 50 mL/min to 150 mL/

S. Aksay, G. Mazza / Journal of Food Engineering 79 (2007) 598–606

603

Table 4 Experimental and predicted values of process time, protein recovery and enrichment ratio for the foam separation process Frit pore size (lm)

Concentration (mg/L)

Airflow (mL/min)

Experimental Time (min)

Recovery (%)

Enrichment

Time (min)

Recovery (%)

Enrichment

25 (1) 25 (1) 25 (1) 25 (1) 75 (+1) 75 (+1) 75 (+1) 75 (+1) 1 (2) 100 (+2) 50 (0) 50 (0) 50 (0) 50 (0) 50 (0) 50 (0) 50 (0) 50 (0)

100 (1) 100 (1) 200 (+1) 200 (+1) 100 (1) 100 (1) 200 (+1) 200 (+1) 150 (0) 150 (0) 50 (2) 250 (+2) 150 (0) 150 (0) 150 (0) 150 (0) 150 (0) 150 (0)

150 250 150 250 150 250 150 250 200 200 200 200 100 300 200 200 200 200

15.5 12.5 17.5 12 23.5 14.5 33 18 12 22 8 17 32 11 18 17 18 18

81.56 84.43 96.87 97.27 64.12 67.34 82.49 87.69 97.41 82.69 46.89 97.79 93.53 93.85 92.61 92.03 91.12 88.43

3.14 2.45 1.66 1.43 4.75 3.37 2.09 1.59 1.95 2.36 7.81 1.42 3.34 1.77 2.06 2.05 2.02 2.01

15.5 12.2 18.9 11.3 23.0 12.0 32.1 16.8 11.2 24.0 9.1 17.2 31.4 12.8 18.1 18.1 18.1 18.1

81.02 81.41 99.59 99.73 66.05 69.02 89.91 92.63 98.89 76.96 49.10 91.27 89.98 93.09 89.97 89.97 89.97 89.97

3.87 3.03 1.55 1.38 5.09 3.77 1.80 1.15 1.50 2.49 6.93 1.99 3.15 1.66 1.96 1.96 1.96 1.96

(1) (+1) (1) (+1) (1) (+1) (1) (+1) (0) (0) (0) (0) (2) (+2) (0) (0) (0) (0)

Predicted

Fig. 5. Response surface graph for process time as a function of frit pore size, initial protein concentration and airflow rate (* Airflow rate, mL/ min).

min at 300 mg/L concentration, the process time decreased from about 70–40 min. On the other hand, when the airflow was 150 mL/min and the frit pore size increased from 1 lm to 100 lm, the process time increased from about 5– 40 min. The airflow was the most significant factor affecting the process time but frit pore size also had a significant effect. Process time was also affected by concentration but to a lesser degree than airflow and pore size (Table 3). The response surface graph for percent protein recovery as a function of initial protein concentration, frit pore size and airflow is shown in Fig. 6. Lower frit pore size resulted in higher protein recovery which can be attributed to smaller air bubbles producing higher air liquid interfacial area

Fig. 6. Response surface graph for protein recovery as a function of frit pore size, initial protein concentration and airflow rate (* Airflow rate, mL/min).

and as a result higher protein absorption. Du, Prokop, and Tanner (2003) reported that the bubble size did not only determine the interfacial area where protein adsorption occurs, but also coalescence and drainage in the foam phase. Larger bubbles lead to greater drainage, and hence less liquid hold-up, while the smaller bubbles enhance the surface area. This means that higher airflow increased the average bubble size, and larger bubbles had a smaller capacity for protein adsorption (Hossain & Fenton, 1998). Crofcheck and Gillette (2003) found that while the

604

S. Aksay, G. Mazza / Journal of Food Engineering 79 (2007) 598–606

higher superficial gas velocity (described as the ratio of volumetric flow rate to column cross-section area) increased percent recovery and decreased the enrichment ratio for small pore size (less than 100 lm), and airflow produced no significant increase in percent recovery. When the initial protein concentration increased from 50 mg/L to 200 mg/L at 1 lm frit pore size, recovery increased from about 50–100%. Further increase in the initial concentration caused a decrease in the percent recovery. For the whole range of frit pore size a similar trend was observed but less protein recovery with increased frit pore size. An increase in initial bulk concentration produced a more stable foam and hence a holdup of more liquid in the foam and thus increased recovery but decreased enrichment ratio. The enrichment ratio of a 50 mg/L protein solution increased up to 12 times when the frit pore size increased to 100 lm at 50 mL/min airflow rate (Fig. 7). Brown et al. (1999) concluded that larger bubble size decreased the foam flow rate and, hence, enhanced the enrichment and separation ratios. At higher frit pore size, the enrichment value was higher at lower airflow than enrichment ratio at higher airflow. The effects of pore size and airflow were not statistically significant though (Table 3). Enrichment was dependent mainly on the initial protein concen-

300

80

0

4

Fig. 7. Response surface graph for enrichment ratio as a function of pore size, initial protein concentration and airflow rate (* Airflow, mL/min).

tration and particularly at concentrations lower than 200 mg/L. The lowest enrichment was seen around

80 10

20

80

5

A

90 250

25

2

90 15

2

Protein concentration (mg/L)

10 100

20 90

200

25 100 2

90

150

80

2 20

15

90

80 100

80

60

70

4

B 15 6

60

70 10 50

70 4

60 20

6

50 40

40

10 60

50

80

8 100

Frit pore size (μm) Fig. 8. Contour plot for predicted value of percent protein recovery (- - -), enrichment ratio (—) and process time (     ) as a function of pore size and protein concentration at 200 mL/min airflow rate.

S. Aksay, G. Mazza / Journal of Food Engineering 79 (2007) 598–606

200 mg/L and there was a slight increase in enrichment from this point to higher concentration. These results are in agreement with the results of Wong, Hossain, Stanley, and Davies (1996) who reported that enrichment ratio dropped sharply from a value of 10 to 1.2 when the BSA concentration increased from 50 mg/L to 500 mg/L. Fig. 8 shows a contour plot of process time, percent protein recovery, and enrichment ratio values as a function of initial protein concentration and frit pore size at 200 mL/min airflow. It illustrates that when the pore size was increased from 1 to 100 lm, the process time and enrichment ratio increased, and the protein recovery decreased. Increasing the initial protein concentration up to 200 mg/L resulted in increased recovery and process time, but decreased enrichment. The highest protein recovery (100%) was obtained near 200 mg/L concentration with 1 lm frit pore size. The highest enrichment ratio was near 50 mg/L concentration at 100 lm pore size. For the determination of the optimal process variables from Fig. 8, the desired variable—enrichment ratio, protein recovery or process time (or a combination of these variables) should be first selected. Thus if a 90% protein recovery is desired (point A in Fig. 8), it can be obtained with a initial concentration of approximately 270 mg/L and 25 lm frit pore size at a process time of 10 min. Under these conditions the enrichment ratio will be 3. On the other hand, if an enrichment ratio of 5 is desired (point B in Fig. 8), it can be obtained with a 75 lm frit pore size and about 80 mg/L in about 15 min. Under these conditions the percent protein recovery will be about 60%. To obtain high percent protein recovery and short process time, small frit pore size must be used. If obtaining a high enrichment ratio is more important than high percent recovery, frit pore size must be as high as possible. 4. Conclusions Foam separation of BSA in batch systems was performed successfully using different frit pore sizes, initial protein concentrations, and airflow rates. Bubble size in the liquid section of the column was mainly dependent on the frit pore size. The average bubble diameters were 1.36 mm and 2.79 mm at 1 and 100 lm frit pore size, respectively. The protein concentration and airflow did not significantly affect the bubble size. Protein recovery increased and process time decreased with decreasing pore size. Maximum protein recovery was obtained at 200 mg/L initial protein concentration. Process time decreased with increasing the airflow. On the other hand, the enrichment ratio was mainly dependent on the initial bulk concentration and increased with decreasing concentration. High correlation coefficient for all three response process variables process time, percent protein recovery, and enrichment ratio showed the applicability of a second order response surface model. Appropriate process conditions can be chosen using the response surface model for high

605

protein recovery or high enrichment ratio for a desired process time. Acknowledgements We gratefully acknowledge Michael Weis and Juan Eduardo Cacace for their constructive suggestions, and Rod Hocking for his technical assistance with the preparation of the foam fractionation column. References Ahmad, S. I. (1975). Laws of foam formation and foam fractionation. I. The effect of different operating parameters on the foam fractionation of albumin from a solution containing organic and inorganic materials. Seperation Science, 10, 673–688. Banerjee, R., Agnihotri, R., & Bhattacharya, B. C. (1993). Purification of alkaline protease of Rhizopus oryzae by foam fractionation. Bioprocess Engineering, 9, 248–255. Bhattacharjee, S., Kumar, R., & Gandhi, K. S. (1997). Prediction of separation factor in foam separation of proteins. Chemical Engineering Science, 52(24), 4625–4636. Brown, K., Kaul, A., & Varley, J. (1999). Continuous foaming for protein recovery: Part I. Recovery of b-Casein. Journal of Biotechnology and Bioengineering, 62(3), 278–290. Brown, L., Narsimhan, G., & Wankat, P. C. (1990). Foam fractionation of globular proteins. Journal of Biotechnology and Bioengineering, 36, 947–959. Crofcheck, C., & Gillette, K. (2003). Evaluation of foam fractionation column scale-up for recovering bovine serum albumin. Transaction of the ASAE, 46(6), 1759–1764. Du, L., Ding, Y., Prokop, A., & Tanner, R. D. (2001). Measurement of bubble size distribution in protein foam fractionation column using capillary probe with photoelectric sensors. Applied Biochemistry and Biotechnology, 91–93, 387–404. Du, L., Prokop, A., & Tanner, R. D. (2002). Effect of bubble size on foam fractionation of ovalbumin. Applied Biochemistry and Biotechnology, 98–100, 1075–1091. Du, L., Prokop, A., & Tanner, R. D. (2003). Effect of ph on the startup of continuous foam fractionation process containing ovalbumin. Separation Science and Technology, 38(5), 1093–1109. Gehle, R. D., & Schugerl, K. (1984). Protein recovery by continuous flotation. Applied Microbiology and Biotechnology, 20, 133–138. Hossain, M. M., & Fenton, G. (1998). Concentration of proteins from single component solution using a semi-batch foaming process. Journal of Separation Science and Technology, 33(11), 1703–1721. Lambert, W. D., Du, L., Ma, Y., Loha, V., Burapatana, V., Prokop, A., et al. (2003). The effect of pH on the foam fractionation of bglycosidase and cellulase. Bioresource Technology, 87, 247–253. Lockwood, C. E., Bummer, P. M., & Jay, M. (1997). Purification of proteins using foam fractionation. Pharmaceutical Research, 14(11), 1511–1515. Myers, R. H., & Montgomery, D. C. (1995). Response surface methodology: Process and product optimization using designed experiments. Toronto: John Wiley and Sons, Inc., p. 700. Noble, M., Brown, A., Jauregi, P., Kaul, A., & Varley, J. (1998). Protein recovery using gas–liquid dispersions. Journal of Chromatography, 711, 31–43. Prokop, A., & Tanner, R. D. (1993). Foam fractionation of proteins: Potential for separation from dilute starch suspensions. Starch, 44, 150–154. Rubin, E., & Gaden, E. L. Jr., (1962). Foam separation. In H. M. Schoen (Ed.), New chemical engineering separation techniques (pp. 320–382). New York: Interscience Publishers.

606

S. Aksay, G. Mazza / Journal of Food Engineering 79 (2007) 598–606

Saleh, Z. S., & Hossain, M. M. (2001). A Study of the separation of proteins from multicomponent mixtures by semi-batch foaming process. Chemical Engineering and Processing, 40, 371–378. Uraizee, F., & Narsimhan, G. (1990). Foam fractionation of proteins and enzymes. II. Performance and modelling. Journal of Enzyme Microbiology and Technology, 12, 315–316. Uraizee, F., & Narsimhan, G. (1996). Effects of kinetics od adsorption and coalescence on continuous foam concentration of proteins: Comparison of experimental results with model predictions. Biotechnology and Bioengineering, 51, 384–398.

Varley, J., Kaul, A., & Ball, S. (1996). Partition of protein from binary mixtures by a batch foaming process. Biotechnological Techniques, 10, 133–140. Wong, C.H., Hossain, M.M., Stanley, R.A., & Davies, C.E. (1996). In: Proceedings of CHEMECA’96 24th Australian and New Zealand Chemical Engineering Conference. Vol. 4, Sydney, Australia, pp. 105– 110. Wong, C. H., Hossain, M. M., & Davies, C. E. (2001). Performance of a continuous foam separation column as a function of process variables. Bioprocess and Biosystem Engineering, 24, 73–81.