Food Chemistry 151 (2014) 182–190
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Optimisation of low temperature extraction of banana juice using commercial pectinase Sorel Tchewonpi Sagu a, Emmanuel Jong Nso a, Sankha Karmakar b, Sirshendu De b,⇑ a b
Department of Process Engineering, National School of Agro-Industrial Sciences (ENSAI), University of Ngaoundere, P.O. Box 455, Adamaoua, Cameroon Department of Chemical Engineering, Indian Institute of Technology, Kharagpur, Kharagpur 721 302, India
a r t i c l e
i n f o
Article history: Received 29 July 2013 Received in revised form 31 October 2013 Accepted 6 November 2013 Available online 16 November 2013 Keywords: Banana juice Extraction Enzymatic treatment Optimization Response surface methodology
a b s t r a c t The objective of this work was to develop a process with optimum conditions for banana juice. The procedure involves hydrolyzing the banana pulp by commercial pectinase followed by cloth filtration. Response surface methodology with Doehlert design was utilised to optimize the process parameters. The temperature of incubation (30–60 °C), time of reaction (20–120 min) and concentration of pectinase (0.01–0.05% v/w) were the independent variables and viscosity, clarity, alcohol insoluble solids (AIS), total polyphenol and protein concentration were the responses. Total soluble sugar, pH, conductivity, calcium, sodium and potassium concentration in the juice were also evaluated. The results showed reduction of AIS and viscosity with reaction time and pectinase concentration and reduction of polyphenol and protein concentration with temperature. Using numerical optimization, the optimum conditions for the enzymatic extraction of banana juice were estimated. Depectinization kinetics was also studied at optimum temperature and variation of kinetic constants with enzyme dose was evaluated. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Banana, mango, guava, and pineapple are major fruits planted widely in tropical and subtropical regions of Africa, Asia and South America. Banana has a high nutrition value and is a good source of energy due to its high level of starch and sugar. It is also a source of vitamins A and C, potassium, calcium, sodium and magnesium (Fabiano, Sueli, Odisse, Gaspareto, & Edson, 2006). Banana is highly appreciated due to its aroma and flavour. It is consumed mainly as fresh fruit and only a small quantity is stored. Banana is very susceptible to deterioration due to rapid decomposition when ripened, and techniques of cold storage are not quite suitable. Considerable amount of this fruit is wasted due to inadequate processing and preservation techniques (Maskan, 2000). For valorisation of this product, the common processed banana products manufactured are banana puree, banana figs, banana powder or flour, banana chips, canned banana slices, banana jam and banana vinegar (Tsen & King, 2002). Studies were carried out to remove water. For this, previous works focused predominantly on drying banana. Mowlah, Takano, Kamoi, and Obara (1983), Mulet, Berna, and Rossello (1989) and Wang and Chen (1998) worked on modelling of the drying kinetics of banana. Minh-Hue and Price (2007) worked on influence of experimental parameters like slab thickness, banana maturity and harvesting season on air-drying of
⇑ Corresponding author. Tel.: +91 3222 283926; fax: +91 3222 255303. E-mail address:
[email protected] (S. De). 0308-8146/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2013.11.031
banana. Fabiano et al. (2006) optimised osmotic dehydration of banana followed by air-drying. Several works also focused on banana puree (Tsen & King, 2002). Bananas are also suitable for juice production but the processing of the banana after harvesting is a major challenge (Mohapatra, Mishra, Singh, & Jayas, 2011). One of the biggest problems is the pectinaceous nature of the banana fruit that makes juice extraction difficult. Gensi, Kyamuhangire, and Carasco (1994) accomplished extraction of banana juice from ripe peeled or unpeeled bananas by a traditional method of mashing with hands in plastic basins using Imperata cylindrica grass as a processing aid. This study was conducted to elucidate traditional methods to produce banana juice in Uganda. Byarugaba-Bazirake (2008) applied commercial enzymes (Rapidase CB, Rapidase TF, Rapidase X-press and OE-Lallzyme) in the processing of banana juice for wine production. The objective was to vary enzyme concentration at different range and observe the parameters influencing wine processes. Dhamsaniya and Varshney (2013) developed a process to evaluate whey beverage with ripe banana juice. Banana slices along with water in the ratio of 1:2 (banana slice:water, w/w) were heated at 100 °C for 45 min followed by the cooling, pressing, centrifuging, filtration, pasteurization (at 90 °C for 10 min) and again cooling at room temperature and no enzyme was used. Besides that, some works have led to the optimization of operating conditions of banana juice extraction. A three-level rotatable design was used by Shahadan and Abdullah (1995) to determine optimum conditions for the extraction of banana juice. The effects of temperature
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Nomenclature ANOVA AIS AAD Af Bf C GAE I k N P R2 RSM xi X
Analysis of variance Alcohol insoluble solids, g of residue/g of the sample in % Average absolute deviation Accuracy factor Bias factor AIS concentration Gallic acid equivalent, mg GAE/100 ml of sample Number of variables Kinetic constant, min1 Number of experiments Probability level Coefficient of determination Response surface methodology Coded variables given par the Doehlert table Real variables
(20–50 °C), pH (2.7–4.3) and enzyme concentration (0.13–0.47%) on the yield of banana juice were studied after 4 h reaction time. In this work, yield was taken as the only experimental response and the effect of incubation time was not taken into account. Lee, Yusof, Hamid, and Baharin (2006a) used response surface methodology (RSM) and the central composite experimental design to optimize conditions for hot water extraction of banana juice and then used commercial pectinolytic enzymes and amylolytic enzyme both to accomplish clarification processes (Lee, Yusof, Hamid, & Baharin, 2006b). Pieces of banana were treated with temperature range (35–95 °C) and time (30–120 min). The optimum conditions found were 95 °C for 120 min. The effect of hot water extraction on juice yield, total soluble sugar and sensory evaluation of the juice were studied and the results showed that temperature was the most important factor affecting the characteristics of the banana juice. It appears from the previous works that the heating up to 95 °C is often used as the first step of extraction of the juice and enzymes are subsequently used for clarification, to reduce high viscosity due to banana pulp. It is not shown by Lee et al. (2006a), the effect of the processing on the preservation of nutritional quality of the final product like protein concentration, total polyphenol, vitamins, Na, K, and Ca. In this regard, Yuanshan et al. (2013) conducted a study on comparing characteristics of banana juice from banana pulp treated by high pressure carbon dioxide and mild heat. The temperatures were 45, 50, 55, and 60 °C, and treatment time was 30 min. The results showed that residual polyphenol oxidase in the juice from high pressure carbon dioxide treated banana pulp was lower than that from mild heat treated banana pulp and its minimum was 11.6% at 60 °C. These results motivate us to investigate and find new procedure to extract banana juice that does not require significant energy input (lower temperature), lower enzyme concentration along with evaluation of nutritional properties of the product obtained and optimize operating parameters. Optimizing refers to improving the performance of a process in order to obtain the maximum benefit from it. During long time, optimization has been carried out by monitoring the influence of one factor at a time on an experimental response and while only one parameter is changed, others are kept at a constant level (one-variable-at-a-time optimization) (Granato, Branco, & Calado, 2010). The disadvantages of this technique are many. These are: large number of experiments; interactive effects among the variables are not studied and complete effects of the parameters on the response are not depicted (Lundstedt et al., 1998). Response surface methodology (RSM) is a collection of statistical and mathematical techniques useful for developing, improving, and
Xi DXi Y Yi,cal Yi,exp TSS v w
Middle (centre) of variable Increment Response variable The calculated response The experimental response Total soluble sugar, degree brix Volume, l Weight, g
Greek symbols bij Coefficient of the interactions terms bii Coefficient of the quadratic terms bi Coefficient of the linear terms b0 Constant term
optimizing processes in which a response of interest is influenced by several variables and the objective is to optimize this response (Granato et al., 2010). To apply the RSM methodology, many experimental designs are available. Box–Behnken design is a three-level factorial arrangement. All factor levels have to be adjusted only at three levels with equally spaced intervals between these levels (Bezerra, Santelli, Oliveiraa, Villar, & Escaleira, 2008). It is efficient and economical for using less number of experiments but lacks accuracy (Lundstedt et al., 1998). Central composite designs are based on a full or fractional factorial design, with an additional number of center points and two axial points on the axis of each design variable at a distance a from the center. All factors are studied in five levels (a, 1, 0, +1, +a). It requires a large number of experiments including experiments outside the studied domain that may not be possible for some systems. Developed by Doehlert, the Doehlert design is a practical and economical alternative in relation to other second-order experimental matrices. This design describes a circular domain for two variables and spherical for three variables, which increases the uniformity of the studied variables in the experimental domain (Bezerra et al., 2008). Each variable is studied at a different number of levels and presents some advantages, such as requiring few experimental points for its application and high efficiency. In this regard, the objectives of this work were firstly to use the commercial pectinase to treat banana pulp for juice extraction under lower range of temperature and use the response surface methodology (RSM) with Doehlert design to determine the optimal conditions. In addition to clarity and viscosity, some nutritional parameters like total polyphenol, protein concentration, calcium, sodium and potassium were also evaluated. Secondly, different concentrations of pectinase were used to study the kinetics of depectinization on banana pulp. 2. Materials and methods 2.1. Materials 2.1.1. Experimental material Fresh, mature and ripe bananas of Musa acuminate (with green skin), commonly referred as Dwarf Cavendish banana variety were obtained from a local market at Kharagpur in West Bengal, India. 2.1.2. Enzyme and other chemicals Pectinase (EC 3.2.1.15) from Aspergillus niger with activity 3.5– 7 units/mg was purchased from Sisco Research Labratory, Mumbai,
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India and used for enzymatic treatment of banana pulp. Folin–Ciocalteu reagent, anhydrous sodium carbonate and copper (II) sulphate pentahydrate were purchased from Merck Specialities Pvt. Ltd., Mumbai, India and Potassium sodium tartrate was obtained from Loba Chemie, Mumbai, India. Bovine serum albumin (BSA) was used for calibration for protein estimation and it was procured from Sisco Research Laboratories Pvt. Ltd, Mumbai, India. Gallic acid standard, used for calibrating polyphenol content was obtained from Loba Chemie, Mumbai, India. Ethyl alcohol for AIS measurement was procured from Jiangsu Huaxi International Trade Co. Ltd, China. The commercial grade filter papers (diameter 110 mm) were procured from Whatman, GE Healthcare UK Ltd., UK and all the glassware used were obtained from Borosil Glass Works Ltd., Mumbai, India. All the chemicals listed above were of analytical grade. 2.2. Process of juice extraction The processing steps of banana juice are outlined below. Fresh and ripe bananas were selected, washed to remove surface filth and microbial flora on the surface, peeled and cut in small pieces. 100 mg of banana was weighed and put into a 600 ml beaker. Based on preliminary experiments, banana to water ratio of 1:2 (weight/volume) was used in the mixing process at 1000 rpm for 10 min using a Remi Motor agitator, type RQ 122 supplied by Elektrotechnik Ltd, Kolkata, India. The mixture was put in water bath at convenient temperature, and then enzyme was added according to the range of experimental design. For example, enzyme dose of 0.03% indicated that 0.09 ml of enzyme was added to 300 g of banana. This means that the unit of enzyme dose was in ml/100 g or % v/w. Throughout the incubation period, the mixture was stirred regularly to allow complete homogenization of the system. The initial pH before enzymatic hydrolysis was not adjusted. At the end of the enzymatic treatment, pectinase was inactivated by heating at 95 °C for 5 min. The mixture was cooled and filtered by a fine mesh cloth filter to remove pulp. The filtrate was collected and various physical and chemical analysis were performed. 2.3. Experimental design RSM with Doehlert design was used to carry out the experiments to optimize the conditions of enzyme treatment. The independent variables were the incubation temperature (X1), incubation time (X2) and concentration of enzyme (X3). The ranges of these variables are: X1, 30–60 °C; X2, 20–120 min; X3, 0.01–0.05% (w/v). The elements of the Doehlert design which are given in coded values are converted to real values for the experiment and following transformation is used:
X ¼ X i þ DX i xi
ð1Þ
where, X is the real variable; xi is coded variable given par the Doehlert table; Xi is the centre of variable and DXi is the increment corresponding to Xi. The number of experiments (N) using the Doehlert matrix with k variables is given by the relationship:
N ¼ kðk þ 1Þ þ 1
ð2Þ
A total of 13 experiments were performed with four replicates at the central point. Experimental design with coded form and real values is shown in Table 1. Five selected responses were: AIS (% w/ w), viscosity (mPa.s), clarity (% transmittance), protein concentration (mg/l) and total polyphenol (mg GAE/100 ml). The mathematical model indicating the effect of variables in terms of linear, quadratic, and interactions terms were related to the variables (Xi, i = 1–3) by a second-degree polynomial given by Eq. (3).
Y ¼ b0 þ
X
bi X i þ
X
bii X 2i þ
X
bij X i X j
ð3Þ
where, Y is the experimental response, Xi and Xj are the levels of variables, b0 is the constant term, bi are the coefficients of the linear terms, bii are the coefficients of the quadratic terms, and bij are the coefficients of the interactions terms. The model equations were examined by analysis of variance (ANOVA) using Stat-Ease Design-Expert Software, version 8.0.7.1. The response surfaces and contour plots were represented with model equations, and were used to describe the individual and cumulative effects on the response. 2.4. Analysis The five experimental responses subjected to optimization were viscosity, clarity, AIS, total polyphenol and protein concentration. For each experiment of experimental design, banana juice was also analyzed for their color, pH, total soluble sugar (TSS), conductivity, calcium, potassium and sodium. 2.4.1. Color and clarity Color of the extract was measured by absorbance (A) at a wavelength of 420 nm using a spectrophotometer (M/s Perkin Elmer, Connecticut, USA) (Rai, Majumdar, Jayanti, Dasgupta, & De, 2006). Clarity of the extract was measured by transmittance (%T) at 660 nm using the same spectrophotometer (M/s Perkin Elmer, Connecticut, USA) (Rai et al., 2006). 2.4.2. Alcohol insoluble solids (AIS) AIS is the measure of pectineous substances in banana. For AIS determination, 10 g of juice was weighed into a 250 ml beaker. 150 ml of 80% alcohol was added, stirred, brought to boil and then simmered slowly for 30 min. Whatman filter paper of appropriate size, which has been previously dried and weighed were used for filtration. The content of the beaker was transferred to the filter paper on funnel and the residue was washed on the filter paper with 80% alcohol until the washing was clear and colorless. The filter paper and the alcohol-insoluble solids were dried for 2 h at 100 °C, then cooled in desiccator, weighed and AIS was determined by as percentage (% w/w) the following equation (Ranganna, 2005). AIS ¼ ðweight of residue=weight of sample taken for estimationÞ 100 ð4Þ
2.4.3. Total polyphenol Total polyphenol was measured using a modified Folin and Ciocalteu method described by Vasco, Ruales, and Kamal-Eldin (2008). Briefly, an aliquot (0.5 ml) of the banana juice blank or standard was placed in a 25 ml flask and 0.5 ml of the Folin–Ciocalteu reagent was added. The mixture was allowed to react for 5 min with stirring. 10 ml of solution of sodium carbonate (concentration 75 g/ l) was added and mixed well. Distilled water was added to make up the volume of the solution to 25 ml and then it was kept at room temperature for 1 h. The absorbance was then measured at 750 nm using a spectrophotometer (M/s Perkin Elmer, Connecticut, USA). The results were expressed as mg of gallic acid equivalents per 100 ml. 2.4.4. Protein concentration Protein concentration was determined according to the dye binding method of Lowry, Rosebroughi, Farr, and Randall (1951) with BSA as standard. 2.4.5. Viscosity The viscosity of banana juice was determined using an Oswald capillary viscometer (Pisco, Kolkata, India) and viscosity was mea-
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S.T. Sagu et al. / Food Chemistry 151 (2014) 182–190 Table 1 Doehlert design: coded variables, real variables and experimental responses. Exp. No.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Coded variables
Real variables
Experimental responses
x1
x2
x3
X1 (°C)
X2 (min)
X3 (%)
Viscosity (mPa.s)
Clarity (%T)
AIS (%)
Polyphenol (mg GAE /100 ml)
Protein (mg/l)
0.00 1.00 1.00 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.00 0.50 0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.87 0.87 0.87 0.87 0.29 0.29 0.29 0.58 0.29 0.58 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.82 0.82 0.82 0.82 0.82 0.82 0.00 0.00 0.00 0.00
45 60 30 52.5 37.5 52.5 37.5 52.5 37.5 52.5 45 37.5 45 45 45 45 45
70 70 70 120 20 20 120 87 53 53 103 87 37 70 70 70 70
0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.05 0.01 0.01 0.01 0.05 0.05 0.03 0.03 0.03 0.03
1.4 ± 0.10 2.7 ± 0.30 1.4 ± 0.05 1.3 ± 0.10 1.7 ± 0.20 2.9 ± 0.25 1.3 ± 0.10 1.4 ± 0.05 1.6 ± 0.15 2.9 ± 0.25 1.4 ± 0.10 1.4 ± 0.12 1.4 ± 0.05 1.4 ± 0.08 1.4 ± 0.06 1.4 ± 0.01 1.4 ± 0.05
35.5 ± 3.0 22.5 ± 2.0 34.3 ± 3.1 59.0 ± 4.0 24.0 ± 2.1 33.8 ± 3.2 65.9 ± 4.4 61.1 ± 4.0 27.6 ± 2.5 25.2 ± 2.0 46.4 ± 4.1 63.5 ± 3.5 53.0 ± 4.9 32.1 ± 2.7 36.9 ± 3.7 36.0 ± 3.2 30.5 ± 3.0
0.56 ± 0.05 0.59 ± 0.04 0.55 ± 0.05 0.41 ± 0.02 0.53 ± 0.03 0.63 ± 0.05 0.51 ± 0.04 0.44 ± 0.03 0.58 ± 0.06 0.59 ± 0.05 0.70 ± 0.06 0.42 ± 0.03 0.63 ± 0.04 0.56 ± 0.04 0.50 ± 0.03 0.60 ± 0.05 0.53 ± 0.04
12.5 ± 1.0 8.4 ± 0.5 15.2 ± 0.7 7.6 ± 0.6 13.3 ± 1.0 11.1 ± 0.8 14.6 ± 1.1 8.6 ± 0.6 14.4 ± 0.3 12.1 ± 0.9 13.1 ± 0.6 11.9 ± 1.0 13.3 ± 1.1 12.7 ± 1.2 12.8 ± 0.9 12.5 ± 0.6 12.9 ± 0.7
1415.0 ± 71.2 1120.2 ± 56.5 1938.5 ± 74.7 510.0 ± 35.8 1400.0 ± 68.9 1080.3 ± 47.4 953.1 ± 46.4 1184.0 ± 63.5 1666.0 ± 68.1 873.7 ± 39.8 1409.4 ± 63.5 704.0 ± 23.7 1450.7 ± 63.8 1357.3 ± 58.2 1514.6 ± 65.1 1422.5 ± 69.2 1338.5 ± 57.5
sured at room temperature (30 ± 1 °C). The unit of measurement used for viscosity is mPa.s. 2.4.6. Total soluble sugar (TSS) The total soluble sugar content in degree Brix (°Brix) was determined using an ABBE type refractometer (Excel International, Kolkata, India). 2.4.7. pH and conductivity Conductivity and pH values of juice were measured using a multi parameter pocket tester (EUTECH Instruments Ltd, Singapore). The unit for conductivity is lS/cm. 2.4.8. K, Na and Ca Calcium was determined by Atomic Absorption Spectrophotometer (M/s Perkin Elmer, Connecticut, USA). Potassium and sodium was determined using ion-selective electrode potentiometer (model: Orion 720A+, Thermo Electron Corporation, Beverly, USA). The results are given in mg/l.
For depectinization study pectinase concentration of 0.01% v/w, 0.03% v/w and 0.05% v/w were used to check the AIS degradation every 10 min during 90 min. Experiments were made at constant temperature and initial pH of juice in order to determine the kinetic constants. 2.6. Statistical analysis 2.6.1. Model equations To validate the different model equations, average absolute deviation (AAD) and coefficient of determination (R2) values were determined. AAD was calculated by the following equation. N X Y i;exp Y i;cal =Y i;exp =N
Bf ¼ 10ð
P
Af ¼ 10ð
P
logðY i;cal =Y i;exp Þ=N Þ
j logðY i;cal =Y i;exp Þj=NÞ
ð6Þ ð7Þ
where, Yi,exp is the experimental response and Yi,cal is the calculated one and N is the number of experiments used in the calculation. 2.6.2. Experimental measurements All the experiments were repeated three times. Various quality parameters were measured and the average values were reported along with standard deviation. 3. Results and discussions
2.5. Kinetic study of depectinization
AAD ¼
factor and the accuracy factor are measures of the relative average deviation of predicted and experimental responses. They are expressed as the antilogarithm of the average of the ratio between the predicted and experimental response and were calculated (Ross, 1996 and Baranyia, Pinb, & Ross, 1999) and showed by Eqs. 6 and 7, respectively.
ð5Þ
i¼1
where, Yi,exp is the experimental response, Yi,cal is the response calculated using the model equation and N is the total number experiment. The response model that had the lowest AAD and the highest R2 (close to 1.0) was considered as the proper model equation for expression of response. Moreover, for better validation of equations, the bias factor (Bf) and accuracy factor (Af) were also calculated to compare the experimental values with predicted values given by models. The bias
The properties of raw banana juice are reported in Table 2. Effects of three factors, incubation temperature, incubation time and enzyme concentration on the five experimental responses (viscosity, clarity, AIS, total polyphenol and protein content) are shown in Table 1. The regression coefficients of the variables in the models for the second order polynomial equations and the corresponding R2, AAD, Bias factor (Bf) and accuracy factor (Af) are shown in Table 3. The analysis of variance (ANOVA) for the five responses showed that the proposed model was adequate and with acceptable values of R2. These values were 0.99, 0.95, 0.85, 0.90 and 0.92, respectively for viscosity, clarity, AIS, total polyphenol and protein content (Table 3). Different R2 values indicate that all the proposed mathematical models can explain more than 90% experimental observations as a function of independent variables, except AIS. According to Baranyia et al. (1999) and Ross (1996) for evaluation of real performance of predictive models in complex system, AAD, bias factor and accuracy factor were also calculated. These three parameters are measures of the relative average deviation of predicted and observed responses. A bias factor and accuracy factor of 1 and AAD of 0 indicate perfect agreement between observed and predicted responses. In our case, all the values obtained for the AIS model (R2 was 0.85) and also for other four responses indicated that all the models are perfectly representative relationships between independent variables and experi-
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Table 2 Properties of the raw banana juice before enzymatic treatment. Viscosity (mPa.s)
Clarity (%T)
AIS (%)
Polyphenol (mg GAE/ 100 g)
Protein (mg/l)
TSS (°Brix)
pH
Color (A)
Conductivity (lS/cm)
Na (mg/l)
K (mg/l)
Ca (mg/ l)
–
0.06 ± 0.003
2.1 ± 0.05
18.9 ± 1.0
1827 ± 70
6.7 ± 0.4
4.7 ± 0.2
3.64 ± 0.01
1905 ± 65
8 ± 0.1
2484 ± 40
61 ± 5.0
Table 3 Coefficients of regression, R2, AAD, Bf and Af values for the different mathematic models. Coefficient
Viscosity y1 (mPa.s)
Clarity y2 (%T)
AIS y3 (% w/w)
Polyphenol y4 (mg GAE/100 ml)
Protein y5 (mg/l)
b0 b1 b2 b3 b11 b12 b13 b22 b23 b33 R2 AAD Bf Af
3.34 0.11*** 0.003*** 8.18*** 0.003** 0.0001* 1.41* 0.0001* 0.35* 274.7*** 0.99 0.01 1.00 1.01
24.6 2.6 0.1*** 1312*** 0.03 0.01* 9.6 0.01** 0.3 25,132*** 0.95 0.05 1.00 1.05
0.22 0.002 0.012** 0.82*** 0.0001 0.0001* 0.13 0.00001 0.17*** 39.01 0.85 0.02 1.00 1.02
4.68 0.3*** 0.22* 18.96** 0.004 0.003** 1.2 0.0003 1.49* 280.13 0.90 0,03 1.00 1.03
4499.3 130.5*** 39.3*** 52090.8 0.53 0.08 2190.5*** 0.2** 498.2** 27,685 0.92 0.05 1.00 1.05
b Represents the coefficients of equations different of models with b0 the constant term; b1, b2 and b3 the linear effects (1, 2 and 3, respectively the temperature, time and concentration of enzyme); b11, b22 and b33 the quadratic effects; and b12, b13 and b23 different interactions. Significant at p 6 0.05. ** Significant at p 6 0.01. *** Significant at p 6 0.001. *
mental responses (AAD: 0.01, 0.05, 0.02, 0.03 and 0.05; Bf: 1.00, 1.00, 1.00, 1.00 and 1.00; Af: 1.01, 1.05, 1.02, 1.03 and 1.05, respectively, for models of viscosity, clarity, AIS, total polyphenol and protein concentration). Therefore, the models were found to be adequate to represent the response data and were further used to plot the response surfaces, followed by optimization of viscosity, clarity, AIS, total polyphenol and protein concentration.
well known gel forming agent, forms gel at higher temperature, thereby increasing viscosity of the solution (Evageliou, Richardson, & Morris, 2000; Oakenfull & Scott, 1984). Moreover, at high temperature, high molecular weight protein gets denatured and protein content becomes less.
3.1. Effect of variables on viscosity
3.2. Effect of variables on clarity
It is observed in Table 3 that viscosity was mostly affected by all the linear factors: temperature (X1) (p < 0.001), time (X2) (p < 0.001), and concentration of pectinase (X3) (p < 0.001). The quadratic parameters X 21 , X32 (p < 0.001) and three interaction parameters (p < 0.05) were also significant. The regression model describing the effect of temperature, time and enzyme concentration on viscosity of banana juice, in terms of their real level, is given as:
As in Table 3, time (X2) (p < 0.001), concentration of pectinase (X3) (p < 0.001) and their quadratic parameters X 22 (p < 0.01) and X 23 (p < 0.001) had the most significant effect on the clarity of banana juice. It is observed in Fig. 1(c) that concentration of pectinase and time had a positive effect on clarity of banana juice. Even more, their interaction between them had a high effect up to 80% at 120 min and 0.05% v/w of concentration of pectinase. It is observed in Fig. 1(d) that the clarity does not depend strongly with the temperature but its interaction with pectinase concentration has positive effect. This result is perfectly in accordance with works of Rai, Majumdar, DasGupta, and De (2004) who had worked on the effect of pectinase for pretreatment of mosambi juice. Their results showed that the temperature increased the rate of enzymatic reactions. Thus, clarity of the juice is proportional to temperature (below enzyme denaturation temperature) and time of reaction. The regression model representing the effect of temperature, time and enzyme concentration on banana juice extract, in terms of their real level, is given as:
ViscosityðmPa:sÞ ¼ 3:34 0:11X 1 þ 0:003X 2 þ 8:18X 3 þ 0:003X 21 0:001X 1 X 2 1:41X 1 X 3 þ 0:0001X 22 þ 0:35X 2 X 3 þ 274:7X 23 : 2
ð8Þ
The coefficient of determination R for the above equation is 0.99. Thus, the regression model explains 99% of the total variability (p < 0.001) for viscosity of the banana juice extract. Fig. 1 shows the response surfaces for the effect of the independent variables on viscosity. These figures show that the viscosity decreases with the reaction time to reach its lowest level in 120 min and also decreases with concentration of enzyme (Fig. 1a). Also, the viscosity decreases with temperature up to 35–40 °C at pectinase concentration 0.05% v/w, and then begins to grow with further increase of temperature (Fig. 1b). These results are in perfect agreement with the literature that shows that optimal activity of pectinase is around 30–40 °C (Demir, Acar, Sarıog˘lu, & Mutlu, 2001 and Sandri, Fontana, Barfknecht, & da Silveira, 2011). With increase in temperature (typically beyond 40 °C), the activity of pectinase enzyme decreases (Kittur, Kumara, Gowdab, & Tharanathana, 2003). Hence, pectin, a
Clarityð%TÞ ¼ 24:6 þ 2:6X 1 þ 0:1X 2 1312X 3 0:03X 21 0:01X 1 X 2 þ 9:6X 1 X 3 þ 0:01X 22 þ 0:3X 2 X 3 þ 25132X 23 :
ð9Þ 2
The value of coefficient of determination R for the above equation is 0.95. This value indicates that the regression model is able to explain 95% of variability of the data.
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Fig. 1. Response surfaces for viscosity of banana juice as a function of (a) temperature and enzyme concentration, (b) enzyme concentration and time; for clarity as function of (c) temperature and enzyme concentration, (d) enzyme concentration and time; and for AIS as function of (e) temperature and enzyme concentration and (f) enzyme concentration and time, keeping the constant variable at central point.
3.3. Effect of variables on alcohol insoluble solids Fig. 1 indicates the effect of temperature, time and concentration of pectinase on alcohol insoluble solids. It is shown that time and pectinase concentration had a negative effect on AIS and their interaction X2X3 (p < 0.01) (Fig. 1e). Moreover, this negative effect is better realized in Fig. 1(f), where, there is a very strong negative slope with concentration of pectinase. This means that concentration of pectinase is the factor that has most significant effect on AIS. This result is found by the analysis of variance that shows a probability (p < 0.001) for this factor. At the maximum of pectinase concentration (0.05% v/w), it was observed a minimum value of AIS (less than 0.5% w/w of AIS) when temperature was 30 °C, and a maximum value of AIS (up to 0.6% v/w of AIS) at the minimum pectinase concentration (0.01%). The regression model representing the effect of temperature, time and enzyme concentration of the banana juice extract, in terms of their real level, is given as:
AISð%w=wÞ ¼ 0:22 0:002X 1 þ 0:012X 2 þ 0:82X 3 þ 0:0001X 21 0:0001X 1 X 2 þ 0:13X 1 X 3 0:00001X 22 0:17X 2 X 3 þ 39:01X 23 : 2
ð10Þ
The value of the coefficient of determination R of above equation is 0.85. The values of AAD (0.02), bias factor (1.00) and accuracy factor (1.02) that measure the relative average deviation of predicted and experimental response show that the model describes the values of AIS adequately. It is apparent from the Fig. 1(e) that at a fixed temperature 45 °C, AIS decreases non-linearly with the duration of enzymatic treatment. AIS consist mainly of pectineous substances, and also starch, hemicelluloses, fiber and proteins and in banana pulp, these compounds are found in high concentration. Because pectineous substances are substrates for pectinase activity, the principal effect of pectinase is on the AIS. This in turn affects parameters such as clarity and viscosity of banana juice.
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3.4. Effect of variables on total polyphenol and protein content The effects of temperature, time and enzyme concentration were also studied for two nutritional quality parameters of banana juice (Fig. 2). The results obtained for this analysis are given in Table 3. It is clear that temperature had the most significant effect on the two variables (p < 0.001). Fig. 2(b) and (d) show that this influence was negative, with strong negative slopes indicating a significant reduction in the amount of polyphenol and protein with temperature. The minimum quantities of these responses were recorded at the maximum temperature (60 °C), and were 500 mg/l and less than 9 mg of GAE/100 ml, respectively, for protein and total polyphenol in banana juice. These effects show that the temperature used for the extraction has a direct impact on the nutritional quality of banana juice and it was important to optimize its range. The regression models representing the effect temperature, time and enzyme concentration on the total polyphenol and protein concentration of banana juice, in terms of their real level, are given as:
PolyphenolðmgGAE=100gÞ ¼ 4:68 þ 0:3X 1 þ 0:22X 2 þ 18:96X 3 0:004X 21 0:003X 1 X 2 þ 1:2X 1 X 3 0:0003X 22 1:49X 2 X 3 280:13X 23
ð11Þ
Proteinðmg=LÞ ¼ 4499:3 130:5X 1 þ 39:3X 2 52090:8X 3 þ 0:53X 21 0:08X 1 X 2 þ 2190:5X 1 X 3 0:2X 22 498:2X 2 X 3 27685X 23 :
ð12Þ
The values of coefficients of determination R2 of above equations are 0.90 and 0.92, respectively, for total polyphenol and protein concentration. These indicate that the regression models explain more than 90% of the variability of total polyphenol and protein concentration of the banana juice extract. It was also noted in Table 3 that there exists significant effect of the interaction between temperature and reaction time on total polyphenol (p < 0.01). The minus sign of the factor shows that this interaction had a negative influence on the response. This means at high temperature and sufficiently long time, the concentration of total polyphenol is reduced. A significant effect of enzyme concentration was also observed in the total polyphenol (p < 0.001). It was noted that this effect was positive for short time (20 min) and became negative for long extraction times (120 min). 3.5. Numerical optimization of the extraction of banana juice The optimum processing conditions for viscosity, clarity, AIS, total polyphenol and protein concentration were investigated. The numerical optimization of process parameters (incubation temperature, incubation time and enzyme concentration) involved in banana juice extraction is carried out using Stat-Ease Design-Expert v8.0.7.1 Software. During the extraction process, enzyme pectinase acts on macromolecules (including pectic substances) by reducing the amount of AIS, decreases the viscosity and increases clarity of the juice. Similarly, augmentation of the amount of polyphenol and protein is directly related to temperature used in the range studied herein. For this purpose, conditions taken in consideration for optimization were to minimize the viscosity and alcohol insoluble solids, and to maximize the values of clarity, total polyphenol and protein concentration. The range of three independent parameters was
Fig. 2. Response surfaces (a) polyphenol as a function of time and enzyme concentration, (b) polyphenol as a function of temperature and enzyme concentration, (c) protein as a function of time and enzyme concentration, (d) protein as a function of temperature and enzyme concentration.
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dC=dt ¼ kC
incubation temperature, 30–60 °C; time of reaction, 20–120 min and pectinase dose 0.01–0.05% v/w. This range for optimization was selected due to following reasons: (i) optimal activity of pectinase occurs in the range of 30–40 °C (Kittur et al., 2003). A higher range was selected to observe the depectinization along with variation of other two independent variables, time of reaction and enzyme dose; (ii) more depectinization occurs with the time of reaction. However, this incurs the cost of energy. Most of depectinization studies revealed that the maximum depectinzation occurred within two hours (Rai et al., 2004); (iii) extent of depectinization is directly proportional to the pectinase dose. But, being costly, higher dose of pectinase again becomes expensive. Thus, its dosage was varied between 0.01% v/w and 0.05% v/ w. Solution having the maximum overall desirability (0.743) was selected as the optimum condition for the process of banana juice extraction. Optimum conditions generated by numerical optimization are: incubation temperature: 33 °C, incubation time: 108 min and concentration of pectinase: 0.03% v/w. The corresponding values of all the five experimental responses are, viscosity: 1.45 mPa.s, clarity: 58.41, AIS: 0.54% w/w, total polyphenol: 15.2 mg of GAE/ 100 ml and protein concentration: 1081 mg/l. To confirm the values obtained from the numerical optimization, the trials have been conducted with the optimal points of the three factors (temperature, time and enzyme concentration). The results of these experiments were, viscosity: 1.42 ± 0.04 mPa.S; clarity: 55.50 ± 5.2%T; AIS: 0.52 ± 0.02% w/w; total polyphenol: 14.8 ± 0.4 mg of GAE/ 100 ml; protein concentration: 1341 ± 71 mg/l. These data confirmed the optimal values generated by numerical optimization.
ð13Þ
where C is the value of AIS at any time point t and k is the kinetic constant. Integration of above equation with initial condition, at t = 0, C = C0, results in the following expression:
C ¼ C 0 expðktÞ:
ð14Þ
The negative sign of k indicates that the concentration of the substrate decreases with time. The enzyme activity of pectinase on banana pulp was carried out at optimum temperature of 33 °C. AIS concentration for different dose of pectinase (0.01% v/w, 0.03% v/w and 0.05% v/w) was plotted with reaction time and is shown in Fig. 3. The kinetic constants were estimated by fitting Eq. (14) with the experimental data. As shown in Fig. 3, lower concentration of pectinase (0.01% v/w) showed relatively low activity as compared to the enzyme concentration 0.03% v/w and the decrease of AIS was more important with enzyme concentration 0.05% v/w. Variation of k with pectinase concentration was: 0.006 ± 0.0004 min1 for 0.01%; 0.011 ± 0.0005 min1 for 0.03%; 0.016 ± 0.0002 min1 for 0.05%. The variation shows decrease of pectin (major compound in AIS) with augmentation of pectinase concentration. It can also be observed that kinetic constant k increases linearly with enzyme concentration. These results are of great scientific importance because they show that the pectinase can act directly on the banana pulp in accordance with the enzyme kinetics, which validates all the
1.1
3.6. Effect of variables on TSS, pH, conductivity, sodium, potassium, calcium and magnesium
Pectinase dose = 0.01 % v/w Pectinase dose = 0.03 % v/w Pectinase dose = 0.05 % v/w Temperature = 33 0C
1.0 0.9
Apart from the variables studied in RSM analysis, some other parameters of banana juice were also recorded for each of 17 experiments. The results are reported in Table 4. As observed, these parameters did not change significantly. Thus, the range of TSS was in between 5.6–6.5 °Brix and 1691–1808 lS/cm for conductivity. pH varied from 4.31 to 4.51. It is also observed from Table 4 that potassium had a high concentration (from 1400 to 1630 mg/l) whereas the values of sodium and calcium were low with maximum concentration at 5.9 and 41.4 mg/l for Na and Ca, respectively.
AIS (% w/w)
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0
20
40
60
80
100
Time (min)
3.7. Kinetic of pectinase activities on banana pulp
Fig. 3. Variation of AIS of banana pulp as a function of time at different concentration of pectinase (0.01% v/w, 0.03% v/w and 0.05% v/w).
For the first-order irreversible enzymatic reaction, the kinetic of degradation of substrate is given by the following relationship: Table 4 Values of TSS, pH, conductivity, sodium, potassium and calcium for various experiments. Exp. No.
TSS (°Brix)
pH
Color (A)
Conductivity (lS/cm)
Na (mg/l)
K (mg/l)
Ca (mg/l)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
6.2 ± 0.3 5.7 ± 0.2 6.5 ± 0.3 5.5 ± 0.1 6.1 ± 0.3 5.9 ± 0.3 5.6 ± 0.2 6.0 ± 0.3 5.9 ± 0.1 6.3 ± 0.3 6.1 ± 0.2 5.7 ± 0.2 6.3 ± 0.3 6.1 ± 0.3 6.0 ± 0.3 6.1 ± 0.2 5.9 ± 0.1
4.4 ± 0.4 4.5 ± 0.3 4.3 ± 0.3 4.5 ± 0.2 4.4 ± 0.4 4.4 ± 0.2 4.3 ± 0.4 4.4 ± 0.3 4.5 ± 0.3 4.4 ± 0.1 4.3 ± 0.4 4.4 ± 0.3 4.4 ± 0.1 4.3 ± 0.2 4.4 ± 0.1 4.4 ± 0.3 4.4 ± 0.4
1.12 ± 0.05 0.78 ± 0.03 1.27 ± 0.06 0.24 ± 0.01 1.63 ± 0.07 1.28 ± 0.09 0.37 ± 0.04 0.29 ± 0.01 1.54 ± 0.04 1.58 ± 0.04 0.89 ± 0.03 0.32 ± 0.04 1.28 ± 0.05 1.13 ± 0.02 1.16 ± 0.04 1.05 ± 0.07 1.15 ± 0.04
1789 ± 58 1710 ± 80 1796 ± 79 1759 ± 81 1722 ± 48 1783 ± 35 1728 ± 72 1759 ± 78 1783 ± 86 1808 ± 92 1691 ± 37 1734 ± 68 1731 ± 72 1770 ± 85 1776 ± 84 1770 ± 58 1783 ± 75
4.0 ± 0.04 3.3 ± 0.02 5.3 ± 0.03 5.7 ± 0.03 3.5 ± 0.03 4.5 ± 0.05 4.7 ± 0.03 3.3 ± 0.01 4.3 ± 0.03 5.9 ± 0.03 3.1 ± 0.01 3.5 ± 0.01 3.2 ± 0.03 4.2 ± 0.06 4.6 ± 0.04 3.7 ± 0.01 4.3 ± 0.03
1580 ± 27 1420 ± 57 1630 ± 37 1470 ± 54 1410 ± 86 1400 ± 36 1570 ± 51 1470 ± 43 1580 ± 68 1580 ± 51 1510 ± 65 1490 ± 27 1500 ± 95 1490 ± 62 1490 ± 67 1420 ± 80 1530 ± 47
20.4 ± 1.2 41.5 ± 5.0 32.7 ± 3.2 25.7 ± 1.8 28.6 ± 1.5 22.2 ± 6.1 21.9 ± 5.8 30.9 ± 5.8 18.2 ± 4.1 18.5 ± 1.6 29.5 ± 4.9 25.0 ± 2.0 35.0 ± 5.3 28.5 ± 1.8 28.0 ± 2.1 32.3 ± 1.5 28.8 ± 4.4
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preceding results of this work and it is clear that the use of pectinase in optimal conditions actually provides a banana juice better quality than that obtained by extraction at a higher temperature. 4. Conclusion An extraction process based on low temperature and low concentration of commercial enzyme is outlined in the present study. The optimum conditions for extractions were evaluated using response surface methodology. These were incubation temperature 33 °C, time 108 min and concentration of the enzyme 0.03% v/w. At these conditions, the major properties were viscosity: 1.42 ± 0.04 mPa.s; clarity: 55.5 ± 5%T; AIS: 0.52 ± 0.02% w/w; total polyphenol: 14.8 ± 0.4 mg GAE/100 ml and protein concentration: 1341 ± 71 mg/l. Total soluble sugar in the extract was in the range 5.6–6.5 °Brix, calcium 20.4–41.4 mg/l. The extract contained high amount of potassium. It was in the range of 1400–1630 mg/l for various extraction conditions. A first order rate equation was fitted to depectinization kinetics. The rate constant was found to vary linearly with enzyme concentration. Acknowledgements We acknowledge and thank the Federation of Indian Chambers of Commerce & Industry (FICCI), the Department of Science & Technology (DST) and the Ministry of External Affairs (MEA), Government of India, for awarding the author Sorel Tchewonpi Sagu, the CV Raman fellowship for African researcher to work at the Department of Chemical Engineering, IIT Kharagpur, West Bengal, India. References Baranyia, J., Pinb, C., & Ross, T. (1999). Validating and comparing predictive models. International Journal of Food Microbiology, 48, 159–166. Bezerra, M. A., Santelli, R. E., Oliveiraa, E. P., Villar, L. S., & Escaleira, L. A. (2008). Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta, 76, 965–977. Byarugaba-Bazirake, G.W. (2008). The effect of enzymatic processing on banana juice and wine. PHD Thesis, Institute for Wine Biotechnology, Stellenbosch University, Cape Town, South Africa. Demir, N., Acar, J., Sarıog˘lu, K., & Mutlu, M. (2001). The use of commercial pectinase in fruit juice industry Part 3: Immobilized pectinase for mash treatment. Journal of Food Engineering, 47, 275–280. Dhamsaniya, N. K., & Varshney, A. K. (2013). Development and evaluation of whey based RTS beverage from ripe banana juice. Journal of Food Process Technology, 4, 203–207. Evageliou, V., Richardson, R. K., & Morris, E. R. (2000). Effect of pH, sugar type and thermal annealing on high-methoxy pectin gels. Carbohydrate Polymers, 42, 245–259. Fabiano, A. N. F., Sueli, R., Odisse, C. P., Gaspareto, C., & Edson, L. O. (2006). Optimization of osmotic dehydration of bananas followed by air-drying. Journal of Food Engineering, 77, 188–193.
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