Characterization of dietary fiber from coffee silverskin: An optimization study using response surface methodology

Author’s Accepted Manuscript Characterization of dietary fibre from coffee silverskin: An Optimization study using response surface methodology Fataneh Behrouzian, Asad Mohammad Amini, Ali Alghooneh, Seyed Mohammad Ali Razavi www.elsevier.com/locate/bcdf

PII: DOI: Reference:

S2212-6198(16)30025-0 http://dx.doi.org/10.1016/j.bcdf.2016.11.004 BCDF121

To appear in: Bioactive Carbohydrates and Dietary Fibre Received date: 16 January 2016 Revised date: 3 June 2016 Accepted date: 7 November 2016 Cite this article as: Fataneh Behrouzian, Asad Mohammad Amini, Ali Alghooneh and Seyed Mohammad Ali Razavi, Characterization of dietary fibre from coffee silverskin: An Optimization study using response surface methodology, Bioactive Carbohydrates and Dietary Fibre, http://dx.doi.org/10.1016/j.bcdf.2016.11.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Characterization of dietary fibre from coffee silverskin: An Optimization study using response surface methodology

Fataneh Behrouzian, Asad Mohammad Amini, Ali Alghooneh, Seyed Mohammad Ali Razavi, Food Hydrocolloids Research Centre, Department of Food Science and Technology, Ferdowsi University of Mashhad (FUM), POBox: 91775-1163, Mashhad, Iran

Abstract Coffee silverskin dietary fiber (DF) was obtained by alkaline hydrogen peroxide (AHP) extraction procedure. Response surface methodology was used to study the

effect of

temperature (25, 40, & 55oС), hydrogen peroxide (HP) concentration (2, 6, & 10% v/v), pH (9, 10, 11,& 12), and time (1, 6, 12, 18, & 24 hour) of AHP treatment on loss, protein removal (PR), cellulose content, whiteness index (WI), water holding capacity (WHC) and oil holding capacity (OHC). AHP treatment (extraction time 18h, extraction temperature 25 o

С, HP concentration 6%v/v, & pH 12) enhanced the cellulose content of CS from 34% to

67.4%. The desirability function approach which used to perform multiple optimizations was 0.89, indicating the distance of the lower and upper limits relative to the actual optimum was highly suitable. The optimum isolation conditions were: extraction time 9.25 h, extraction temperature 37.34 oC, HP concentration 10%v/v, & pH 10.54. At optimum condition, WI, WHC, OHC, loss, & PR of DF were 80.30, 8.18 & 5.27, 40.05, 89.04%, respectively. The results suggested CS could be used as a source of insoluble DF especially cellulose in food formulations.

Keywords: Coffee silverskin; Dietary fibre; Extraction; Response surface methodology.



Corresponding author: Tel./Fax: +98- 51- 38805763, Email: [email protected]

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1. Introduction

Coffee is one of the most consumed beverages worldwide and the second largest traded commodity after crude oil. During the industrial processing of coffee beans to produce instant coffee, large amounts of by-products are generated. Disposal of these by-products are currently an environmental concern (Mussatto et al. 2011). Accordingly, attempts should be made to more utilize by-products into value-added compounds. The worldwide production of coffee is over 105 million tons annually (Murty 2012). Almost 50% of the worldwide coffee production is processed for soluble coffee preparation (Mussatto et al. 2011a). Coffee silverskin (CS) represents about 4.2 % (w/w) of coffee beans (Balestroues 2014). CS which covers each hemisphere of the coffee bean (endosperm), is one of the main residues of roasting step in instant coffee processing which nowadays is utilised for composting or as fertilizer (Menéndez et al. 2007). As silverskin is the outer layer of the roasted coffee bean, it may have some of valuable characterization of coffee brews (Borrelli et al. 2004). It has been introduced as a DF rich ingredient (60% total DF) with antioxidative properties, which can be ascribed to the enormous amount of Maillard reaction products, the melanoidins (Borrelli et al. 2004; Pourfarzad et al. 2013). DF is a group of food components that resistant to digestion and absorption in human small intestine with complete or partial fermentation in the large intestine and it is believed to be necessary to promote good health (Sangnark and Noomhorm 2004). Generally, dietary fibers are classified into soluble and insoluble fibers. Those fibers that are mainly composed of cellulose, hemicellulose, and lignin are primarily insoluble. Some common insoluble native fiber sources used in food formulations include wheat bran and corn bran. On the other hand, fibers that include significant portions of gums, polyfructoses, pectins, and mucilages, such as psyllium, fruits, and oat bran contain significant fractions of soluble fiber (Bodner and Sieg, 2009).

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The major components of CS are cellulose and hemicelluloses; besides, proteins and extractives are other portions present in significant amounts (Alghooneh et al. 2015). Hemicelluloses are cell wall polysaccharides, smaller than cellulose in size (Anita and Abraham 1997). Although lignin exists at minor amount in CS, due to strong intramolecular bonding, it demonstrates greater resistance than any other naturally occurring polymer (Dhingra et al. 2012). Cellulose is widely used due to its multipurpose applications in many areas including the food, paper, and pharmaceutical industries. Natural by-products are rich in both DF and polyphenolic compounds, and known as antioxidant dietary fibers. CS could have enhanced preference than other sources of DF due to the presence of associated bioactives (phenolic compounds) with antioxidant properties, which impart additional health benefits (Murthy and Naidu, 2012). Harmful effects have been observed when untreated lignocellulose materials were added to food formulations (Borrelli et al. 2004), so extraction process for the untreated lignocellulose materials is necessary. Response surface methodology (RSM) has been reported as an effective tool for optimization of process when the independent variables had nonlinear and interaction effect on the desired response (Dean and Voss 2000). It was successful in modeling, improving and optimizing of extraction processes (Ajala et al. 2015; Mushtaq et al. 2015; Tang et al. 2015). The main idea behind RSM is exploring the relationships between several independent variables and one or more response variables. Also, it considers the interaction between the variables and therefore covers the effect of all process parameters. The physiological and functional properties of DF are often ascribed to their physicochemical characteristics such as water holding capacity, swelling, and fat binding properties (Dikeman and Fahey 2006). Treatment with AHP could improve hydration properties of lignocellulose materials, not only by reduction of the lignin and hemicellulose content but also the stirring, as essential part of the process, opens the fiber structure to more available

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free hydroxyl groups of cellulose, consequences in higher WHC (Gould 1985). On the other hand, CS’s brown color, which limits its incorporation in food formulations, could be greatly diminished by this procedure. AHP treatment has mostly been applied to bleach lignocellulosic material. In a study by Sangnark and Noomhorm (2003), the brightness of sugarcane bagasse was increased by 34% and AHP method significantly improved the WHC and OHC. Rising consumer awareness about the potential therapeutically role of the DF has led to increased consumer demands for fibre-rich products. Fibres from novel sources are introduced to meet the continuous demands of the industries by food scientists. In this regard, CS has potentials to serve as a new source of DF to formulate health related food products as a result of its sufficient availability. Limited studies have been reported in the literature on silverskin's DF (Alghooneh et al. 2015; Borrelli et al. 2004; Napolitano et al. 2007; Pourfarzad et al. 2013). In our previous research on extraction and characterisation of cellulose from CS, AHP treatment increased the whiteness index of CS, remarkably. Nevertheless, the optimization of AHP treatment parameters based on loss, color, WHC, OHC, PR and cellulose contents, which had not been investigated before, was studied in this research, which aimed to provide more detailed information on functionality and commercial viability of the CS fiber.

2. Material and methods

2.1. Materials The coffee silverskin (CS) from robusta variety coffee beans was a gift from Part Makers Coffee Industry (Multi-Café) Corp. (Mashhad, Iran). The CS was sieved, pneumatically cleaned and washed with tap water thrice. Then, it was dried using an air-forced oven at 60 °C, milled and sieved through a 70 mesh sifter. The dried powder was packed in polyethylene

4

bags and kept at cold storage (4 oC) prior to experiments. All used chemicals were of analytical grade.

2.2. Alkaline hydrogen peroxide treatment According to Alghooneh et al. (2015) procedure, samples of silver skin were pretreated with n-hexane (1:4 w/v ratio) and allowed to stir gently for 48 hour. Then, samples were filtered, 96% ethanol was added at the same ratio of n-hexane to the residues, and stirred for 12 hours. Again, samples were filtered and mixed with a blend of chloroform (1:1 w/v ratio) and methanol (1:2 w/v ratio). Subsequently, chloroform was added at a ratio of 1:1 w/v. After stirring for 30 seconds, distilled water was added (1:1 w/v ratio) and mixing continued for further 30 seconds. The dewaxed and defatted CS was desolventised and dried in air-forced oven for 12 hours at 45 °C. The main steps of AHP treatment were as follows: The slurries of defatted CS at fixed ratio of 1:22 (w/w) were prepared by mixing the CS with AHP solutions. The hydrogen peroxide solutions were prepared in the concentration range of 2-10% (v/v) and then their pHs were adjusted in the range of 9 to 12 by 25% sodium hydroxide solution. After that, the slurries were stirred using a hot-plate magnetic stirrer in air-tight containers (to prevent vaporisation) for 1-24 h at 25-55°C. The neutralisation of treated slurries was performed by adding 4 N phosphoric acid solution, then, the solid residues after filtration were collected and thoroughly washed with water, and dried in air-forced oven at 60 °C for 24 h to complete dryness. Upon removal from the oven, the meals were ground, sieved and stored at room temperature (25°C) for further analysis.

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2.3. Chemical compositions The amounts of moisture, ash, fat (using a Soxtec apparatus), protein (Kjeldahl method and nitrogen conversion factor of 6.25), and the amount of soluble (SDF), insoluble (IDF), and total dietary fibres (TDF) were determined according to standard AACC (2000) methods. Carbohydrate content was determined by subtracting the sum of protein, moisture, ash and fat contents from 100%.

2.4. Loss and protein removal: The loss of extraction was determined gravimetrically after oven drying until they reached a constant weight, it was calculated as: Loss% =

(

)

(1)

×100

The protein removal (PR) during extraction was calculated by following equation, using Kjeltec Digestion System (Tecator, Sweden) with nitrogen conversion factor of 6.25: PR% =

(

)

(2)

2.5. Cellulose content determination Cellulose content of AHP treated samples was determined based on the method described by Brendel et al. (2000). Briefly, 2.0 ml of acetic acid (80%v/v) and 0.2 ml of concentrated nitric acid (69%v/v) were added to a screw-capped Pyrex tube containing 100 mg AHP treated powder. Then, the tubes were autoclaved to 120 °C at 15 psi for 20 min. After cooling to RT, the residues were washed thrice with 2.5 ml of ethanol (99% v/v), twice with 2.5 ml deionised water, twice with 2.5 ml of ethanol and finally twice with 2.5 ml acetone. The isolated cellulosic materials were grounded, passed through 70 mesh screen aperture and kept in air-tight zip lock polystyrene bags until performing experiments. The cellulose content was calculated as the percentage of the weight of solid residues to the initial sample weight. 6

2.6. Water and oil holding capacity (WHC and OHC) determinations The WHC and OHC of samples were determined using the procedure described by Ang and Miller (1991). For determining the WHC, 2g of sample was transferred in to a 50 ml centrifuge tube contained 30 ml of deionised water and allowed to stand for 10 min at room temperature. The mixture was centrifuged at 2000 × g for 15 min (Sigma, 3-30 k, Germany). After centrifugation, the supernatant was drained on a fine-meshed wire. A portion of the residue on the screen was removed, weighed and dried to constant weight (± 0.05 mg) in a forced-air oven (110 °C). WHC was expressed as the ratio of weight (g) of water adsorbed per weight (g) of sample. The same procedure was applied for OHC determination except corn oil (with a density of 0.85 g/ml) was used instead of water. OHC was expressed as gram oil absorbed per gram sample.

2.7. Color evaluation The color characteristics of samples were determined using ImageJ software version 1.6.0 (National Institute of Health, USA) on scanned images. The images were acquired using a Genius colorpage HR6X slim scanner (Genius, Taiwan) in RGB color space, 24bit color depth, 600 dpi resolution, and 3×3 cm dimensions. The images were denoised and made binary, then converted to CIE-Lab color space and then, the measurements were performed. In CIE-Lab color space, L* indicates lightness, a* specifies hue on a green (-) to red (+) axis, and b* specifies hue on a blue (-) to yellow (+) axis. Whiteness index (WI) was measured using the following equation (Park et al. 1995): √

(

)

(3)

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2.8. Temperature dependency The temperature dependence of any characteristic mentioned above described by Arrhenius equation: ( )

(4)

Where, A is the proportionality constant, Ea is the activation energy (kJ/mol), R is the universal law gas constant (kJ/mol K), and T is the absolute temperature (K).

2.9. Experimental design and statistical analysis The experimental scheme of the present study was designed as a D-optimal design through response surface methodology (RSM) with four independent variables. Each one had different levels, in order to find the effect of these variables on the functional properties of CS powder. The variables were included concentration of HP, mixing time, pH, and temperature. The levels of variables are presented in Table 1. A D-optimal design was constructed using Design Expert software (version 8/0/7/1, Stat-Ease Corporation, Minneapolis, MN, USA). D-optimal algorithm selects a subset of design points from the list of candidate points to maximize determinant of X.X' matrix, where X is the matrix of the design, and X' is the transpose of the matrix X. On the other words, experimental region in the D-optimal design is irregular which allows maximum information about experimental regions by performing minimal tests. Furthermore, it provides the use of asymmetrical intervals for independent variables (Table 1), a factor that conventional RSM designs (central composite and box behenken) are not capable of performing. The design included a total of 25 experimental runs (Table 2); 15 runs for determining coefficients of model, 5 runs for estimating lack of fit, and 5 replications for calculating the error. In order to perform data modelling for responses, a quadratic polynomial model (Eq. 5) was used and the coefficient

8

of determination (R2) and root mean square error (RMSE) were calculated to assess the efficiency of models for predicting responses accurately: ∑

∑

∑∑

(5)

Where, xi is the variables; β0 is the intercept; βi is the linear term; βii is the interaction term, βij is the quadratic term, and e is the residual. The investigated response functions were color (YC), water holding capacity (YW), oil holding capacity (YO), cellulose (YC), protein removal (YP), & loss (Yl). The stepwise technique was applied on models to eliminate non-significant terms. In this method, the first predictive variable enters the analysis based on the highest correlation coefficient with dependent variable. Afterwards, other predictive variables enter the analysis based on the correlation coefficient. Once any new variable enters, the correlation coefficients of all pervious variables are checked. If a variable reduces model performance, it will omit from the equation. Results were obtained from average of duplicate experiments. Also in order to eliminate effect of extraneous factors on the observed responses, any experiments were carried out in a randomized order.

3. Result and discussion

3.1. Chemical composition of coffee silverskin The results of proximate chemical composition revealed that CS contained about 18.97±1.32 % protein, 71.73±0.81 % dietary fibre, 2.72±0.81 % fat, 5.73±0.32 % ash and 8.23± 0.23% moisture (dry base). The total dietary fibre content (71.71±0.53 %) consisted mostly of IDF (64.21±0.32 %) with SDF (7.61±0.72 %). These results were consistent with those reported by Alghooneh et al. (2015) and Borrelli et al. (2004). The compositional data of CS suggested it could be used as a source of insoluble DF.

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3.2. Loss From the quadratic model of the loss, the linear effect of all parameters, except temperature, were significant (p<0.05). Low RMSE value (2.21) supported the high significance of the model. The RSM model for prediction of the loss value in terms of the coded variables was determined as follows: Loss = 46.52 + 6.44x1+ 8.29x2 + 14.61x3 + 10.21x4 – 4.57x1x2 +4.98x1x3 – 10.46x1x4 + 5.41x2x4

(6)

Where, x1 is temperature, x2 is the HP concentration, x3 is the pH value and x4 is the reaction time. ANOVA results indicated that pH and interactive effect of temperature-time were the most affecting factors on the loss value of CS. The interactive effect of temperature-time on the loss value of CS is shown in Fig. 1 (a). Furthermore, based on Table 3, the most affecting range of pH, time of reaction, HP concentration and temperature on increasing the loss value of CS, were 9-10 (8.12% with 0.1 unit increase in pH), 1-6 h (8.02% with 1 hour increase in reaction time), 2-6v/v% (14.66% with %1 increase in HP concentration) and 25-35°C (1.47% with 1°C increase in temperature), respectively. The loss of treated samples ranged between 14.2 (code 1) - 76.93% (code 25). Results showed the effect of temperature on the loss value depended on the reaction time and the pH of the solution. In this way, loss value showed no significant changes with increasing the temperature from 25 to 55 °C at low pH value (pH=9), whereas it increased with increasing temperature at high pH solution (pH=12). Furthermore, when the reaction time was low (1h), the loss value continually increased with increase in temperature; on the contrary, in high reaction time (24h), reverse behavior was observed. Fang et al. (1999) reported over 90% of the original hemicelluloses removed during 12 hour treatment with 2% H2O2 at pH 11.5 and 50oC.

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3.3. Protein removal The results indicated the quadratic model could satisfactorily fit the result of PR in AHP treatment (R2 and RMSE values were equal to 96% and 2.81, respectively). All the main, quadratic, and interactive effects were significant on the PR, except interactive effect of concentration-pH, concentration-time and pH-time (p < 0.05). The RSM model for prediction of the PR in terms of the coded variables was determined as follows: PR = 81.65 + 4.34x1+ 12.95x2 + 20.62x3 + 10.57x4 – 12.80x1x2 + 6.38x1x3 – 12.68x1x4 – 14.48x32 – 8.17x42

(7)

The results of the ANOVA indicated the linear and quadratic effects of pH were the most affecting factors on the PR of CS. According to Table (3), the most affecting range of pH, time of reaction, HP concentration and temperature on the eliminating of protein from CS were 9-10 (8.09% increase with 0.1 unit increase in pH), 1-6 h (10.23% increase with 1 hour increase

in

reaction

time),

2-6v/v%

(15.30% increase

with

%1

increase

in

H2O2 concentration) and 45-55°C (1.77% increase with 1°C increase in temperature), respectively. The effect of temperature on the PR depended on the reaction time, HP concentration and the pH of the solution. Thus, increasing the temperature from 25 to 55 °C at low pH value (pH=9) had no significant effect on PR from CS, but the PR significantly increased with increasing temperature in high pH solution (pH=12). Furthermore, when the reaction time and HP concentration were low (1h & 2%w/w), PR value continually increased with increase in temperature; on the contrary, in high reaction time and HP concentration (24h & 10%w/w) the reverse trend was observed. The effect of pH-temperature on PR is shown in Fig. 1 (b). With increasing the pH up to a certain value (11.5), PR increased, but then slightly decreased at higher pH value. The precipitation of degraded protein on DF in the sever condition might cause the PR reduction at pH>11.5. In addition, results showed that, increase in HP concentration led to greater PR. Alkalinity enhancement results in decomposition of peroxide to the reactive hydroxyl radicals (HO•) and superoxide anion

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radicals (O2- •) (Sun et al. 2000), which have the ability, either directly or indirectly, to damage all biomolecules, including proteins, lipids, DNA, and carbohydrates. Thus proteins susceptibility to oxidative damage could alter a protein’s physiochemical characteristics (e.g., folding and subunit association) and direct to aggregation or fragmentation (Shacter 2000). The protein content of DF decreased with increase of time. High extent of PR appeared in the range of 1-15 h, while a slight increase was found with further increase of time (15-24h). High PR was obtained at low temperature and long time; while at a high temperature, intermediate levels of time was needed to obtain a high reduction in protein content, and further increasing of time would lead to a slight reduction in PR. This may be caused by precipitating and aggregating of solubilized protein on DF. Seres et al. (2005) reported AHP treatment at pH= 11, time= 30 min and temperature= 30°C could diminish the protein content from 12.30 in original sugar beet pulp to 4.16 at treated one.

3.4. Cellulose content Quadratic model with high R2 (95%) and low RMSE (1.23) adequately described the relation between independent variables and cellulose content of CS. The results showed that the main effect of temperature and the interactive effect of temperature-pH, HP concentration-pH, HP concentration-time, pH-time, and quadratic effect of temperature and time were not significant. The RSM model for prediction of cellulose content in terms of the coded variables was determined as follows: Cellulos= 59.40 + 1.11x1+ 5.28x2 + 8.69x3 + 8.54x4 – 4.25x1x2 – 4.36x1x4 – 6.83x22 – 6.08x32

(8)

Generally, errors are derived from two sources, lack of fit and pure error. Pure error is a measure of the variation in response at the same operating condition and is calculated independently from the regression. The lack of fit is calculated as the difference between total error and pure error. The P value of the lack of fit test was not significant (0.26), which again

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confirmed that the quadratic model was adequate for cellulose content prediction. The analysis of variance of results indicated that pH and time of reaction were the most affecting factors on the increasing the cellulose extraction from CS. As shown in Table 3, the most affecting range of pH, time of reaction, hydrogen peroxide concentration and temperature on the increasing the cellulose content were 9-10 (5.22% with 0.1 unit increase in pH), 1-6 h (5.72% with 1 hour increase in reaction time), 2-6v/v% (9.16% with %1 increase in HP concentration) and 35-45°C (0.97% with 1°C increase in temperature), respectively. The results showed that the effect of temperature on the cellulose content depended on the reaction time and HP concentration. In this way, at low reaction time and HP concentration (1h & 2%w/w), cellulose content continually increased with increase in temperature; on the contrary, in high reaction time and HP concentration (24h & 10%w/w), it decreased with increase in temperature, continually. With increasing the reaction time, the cellulose content increased. Fig. 1(c) shows the interactive effect of temperature-pH on the cellulose content of CS. It can be seen that increasing the pH value up to 11.5 caused an increase in cellulose content; although, it decreased slightly by further pH increasing. The delignification is most effective at pH 11.5–11.6, which is the pKa for the dissociation reaction of H2O2 to hydroperoxide anion (HOO-), the active bleaching species (Gould, 1985). Slight decrease in cellulose content at pH>11.5 suggested, to some extent, cellulose degradation at high pH value. Although at low temperature, increasing of HP concentration increased cellulose content of DF, this constant enhancement was not noticeable at high temperature. At high temperature, when HP loading was at a low level, an increase in HP concentration only resulted in a slight increase in the cellulose content, while at higher HP concentration, a vast reduction of cellulose content was observed. This may be due to solubilizing effect of these sever condition (high temperature- high HP concentration) on cellulose. The cellulose content increased from 34% in the original silverskin to 67.4% at code 3 sample. Correia et al. (2013)

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reported an increase of 37.6% of cellulose in the cashew apple bagasse treated with 4.3% v/v AHP (pH=11.5, temperature=35 oC, biomass loading= 10% w/v, time 24h) compared with the original sample.

3.5. Water holding capacity It was found that quadratic model could satisfactorily fit the results of water holding capacity (WHC) (R2 and RMSE values were equal to 93% and 0.69, respectively). The linear effect of all parameters, except temperature, and the interactive effect of pH- time and quadratic effect of time were significant (p<0.05). The RSM model for prediction of the WHC in terms of the coded variables was determined as follows: WHC =8.17+ 0.66x2 + 0.26x3 + 1.23x4 – 0.93x32 –1.11x42

(9)

The results of the ANOVA indicated the linear and quadratic effects of the time of reaction were the most affecting factors on the WHC of DF from CS. In addition, the most affecting range of pH, time of reaction, HP concentration and temperature on the increasing of the WHC of CS were 9-10 (2.58% with 0.1 unit increase in pH), 1-6 h (6.03% with 1 hour increase in reaction time), 6-10 v/v% (2.10% with %1 increase in HP concentration) and 4555°C (1.29% with 1°C increase in temperature), respectively (Table 3). With increasing the reaction time, the WHC of DF from CS increased from 3.25 to 9.54 gr water / gr dry sample within 24 h. The effect of HP concentration -time on WHC is shown in Fig. 1 (d). Seemingly, cellulose chains became separated from each other under mechanical shear force, which resulted in higher available free hydroxyl groups of cellulose to bind with water. These results are consistent with the earlier observation (Sangnark et al. 2004; Schmidt 1994). Bodner and Sieg (2009) explained that the mechanical shears force produced by apparatus resulted in the modification of fiber structure. Furthermore, Schmidt (1994) reported, in a reaction medium containing an aqueous solution of strong AHP, higher solubilization of

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lignin occurred from substrate, consequential in the disruption of substrate structure and enhancing the WHC of obtained DF.

3.6. Oil holding capacity Quadratic equation satisfactorily fitted the results of oil holding capacity (OHC) (R2 and RMSE values were equal to 95% and 0.02, respectively). The results showed that all linear model parameters except of temperature were significant (p < 0.05). “Adeq Precision” measures the signal to noise ratio. This parameter compares the range of the predicted values at the designed points to the average prediction errors. In this study, a ratio greater than 4 was desirable. The ratio of 18.40 for OHC indicated an adequate signal. The RSM model for prediction of the OHC in terms of the coded variables was determined as follows: OHC=4.17+0.68x1- 0.15x2 + 0.03x3 -0.36x4 -0.21x1x2 + 0.37x1x3 + 0.16x2x3 -0.12x2x4 +0.43x12 +0.61x22 - 0.37x32 -0.21x42

(10)

The results of the ANOVA indicated that linear and quadratic effect of temperature and HP concentration were the most affecting factors on the OHC of silverskin’s DF, respectively. Furthermore, the most effective range of pH, time of reaction, HP concentration and temperature on the OHC of CS, were 10-11 (1.97% increase with 0.1 unit increase in pH), 16 h (4.46% increase with 1 hour increase in reaction time), 2-6v/v % (2.75% decrease with %1 increase in H2O2 concentration) and 45-55°C (2.98% increase with 1°C increase in temperature), respectively (Table 3). The effect of pH-time on OHC is shown in Fig. 1 (e). Concerning both WHC & OHC it was obvious that the AHP-treatment affected the hydration and oil binding properties in the same manner, except any interactions effect was significant in OHC. These might be caused by the modification of physical structure of the product by this procedure. These results were consistent with Sangnark et al. (2004) which found that AHP-treatment increased OHC of DF prepared from rice straw.

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3.7. Color The results showed quadratic model of whiteness index (WI) was highly significant (R2 and RMSE values were equal to 98% and 0.63, respectively). The lack-of-fit tests did not result in a significant F-value, indicating that the model is sufficiently accurate for predicting the WI. In addition, the coefficient of variation (1.09) indicated acceptable reproducibility of the results. Eexcept of the linear term of temperature and interactive effect of temperatureconcentration and temperature-time, any model parameters were significant (p < 0.05). The RSM model for prediction of the WI in terms of the coded variables was determined as follows: WI= 79.14+ 1.19x2 + 1.18x3 + 1.67x4 – 0.55x1x3 + 0.61x2x3 + 1.29x3x4 –4.54x32 –1.11x42

(11)

The results of the ANOVA indicated that quadratic and linear effects of both pH and time of reaction were the most affecting factors on the WI of DF from CS. Furthermore, according to Table (3), the most effective range of pH, time of reaction, HP concentration and temperature on the increasing the WI of silverskin’s DF were 9-10 (0.84% with 0.1 unit increase in pH), 12-18 h (3.26% with 1 hour increase in reaction time), 2-6 v/v% (0.94% with %1 increase in HP concentration) and 25-35°C (0.42% with 1°C increase in temperature), respectively. The brightness of CS increased with increasing the pH, time, concentration of HP and their interactions. The effect of pH-time on WI is shown in Fig. 1 (f). It is generally accepted that hydroperoxide anion HOO-, which increases with increasing the value of pH, involved in the elimination of chromophoric groups in the lignin macromolecules (Sun et al. 2000). Also, positive linear and negative quadratic effects of time were observed on brightness. This might be due to a loosening of the structure of CS by continues mechanical shear force. These results were consistent with earlier research (Sangnark et al. 2004; Pourfarzad et al. 2013).

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3.8. Temperature dependency It was found that Arrhenius model adequately fitted the temperature dependency of all mentioned characteristic (R2=0.88-0.95 & RMSE = 0.67-1.25). As shown in Table 3, the highest activation energy was determined for loss value (134.30 kJ/mol) and followed by PR (85.26 KJ/mol); while, the lowest activation energy was found for WI (18.28 kJ/mol); indicating in AHP treatments the loss value showed the lowest temperature sensitivity, whereas the WI presented the highest temperature tolerance. In addition, temperature dependency for cellulose content, WHC, OHC were obtained as 45.27, 54.87, 79.07 kJ/mol, respectively.

3.9. Optimization of procedures and verification of results Numerical optimization is performed to obtain the optimum levels of independent variables in order to achieve the desired response goals. In this paper, the desirability function approach was used to perform multiple optimizations. This approach started firstly by converting each response variable into a desirability function di, which varied from 0 to 1, where if the response yi was at its target, then di=1, and if the response was outside of an acceptable region, di=0. Global desirability function is determined by Eq. (12). This method should search for response variable values where D tends to 1. This value is completely dependent on how closely the lower and upper limits are set relative to the actual optimum. D = (d1.d2.d3……. .dm)1/m

(12)

Generally, DF with high WHC and OHC could be used as a functional ingredient to avoid syneresis of formulated products and to stabilize foods with a high percentage of fat and emulsion, respectively (Grigelmo-Miguel et al. 1999). Also, addition of lignocellulosic materials into food products remarkably reduces the WI of products which limits

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incorporation of them in food formulations. Accordingly, we looked for objectives such as the maximum of WHC, OHC, WI and PR, besides the minimum of loss for optimization of DF extraction from CS. As seen in Table 4, the optimal conditions which extracted by Design Expert software were: mixing time of 9.25 h, HP concentration of 10%v/v, pH of 10.50, and the temperature of 37.50 °C. The desirability function was 0.89, indicating the distance of the lower and upper limits relative to the actual optimum were highly appropriate. AHP procedure was done with the optimized condition and showed any significant difference between the estimated and observed values (p < 0.05); suggesting an appropriate correlation between the models and experimental data.

4. Conclusion

AHP treatment was able to effectively extract DF from CS and improve the physical properties of the final products. The RSM was confirmed to be a useful tool for the optimization of the DF extraction from CS. The optimal conditions (extraction time 9.25 h, extraction temperature 37.34◦C, HP concentration 10%v/v, & pH 10.54) for the DF isolation were estimated using numerical optimization. The desirability function was 0.89, reflecting the distance of the lower and upper limits relative to the actual optimum were highly appropriate. AHP procedure was done with the optimized condition and showed any significant difference between the estimated and observed values (p<0.05). Results suggested, under AHP treatment, CS could be used as a source of insoluble DF, especially cellulose, in food systems.

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Acknowledgement This project was funded by the Deputy of Research and Technology, Ferdowsi University of Mashhad, Iran. This research was conducted under grant #22305 by Department of Food Science and Technology. The financial support is gratefully acknowledged.

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Figure caption Fig 1. Response surface plots of combined effects: (a) temperature-time on the %loss (pH=10.5 & 6%v/v), (b) temperature- pH on the %protein removal (12.5h & 6%v/v), (c) temperature-pH on the % cellulose content (12.5h & 6%v/v), (d) time-concentration on the %water holding capacity (40 oС & pH=10.5), (e) time-pH on the %oil holding capacity (40 oC & 6%v/v), (f) time-pH on the % WI (40 o

C & 6%v/v).

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Table 1. The RSM variables used for the extraction optimization of dietary fibre from coffee silverskin Variable

Level

Temperature (ºC)

x1

25 35 45 55

H2O2 concentration (% v/v)

x2

2

6

pH

x3

9

10 11 12

Reaction time (h)

x4

1

6

10

12 18 24

Table 2. Experimental design of alkaline hydrogen peroxide treatment for the extraction of dietary fibre from coffee silverskin Code

Independent variable x1

x2

x3

x4

1

25

2

9

18

2

25

6

9

1

3

25

6

12

18

4

25

10

9

24

5

25

10

12

1

6

35

2

9

6

7

35

2

11

1

8

35

2

11

24

9

35

10

10

12

10

35

10

10

12

11

35

10

10

12

12

45

2

9

24

13

45

6

9

12

14

45

6

10

24

15

45

6

10

24

16

45

6

12

12

17

45

6

12

12

18

45

10

9

1

19

45

10

12

1

23

20

55

2

9

12

21

55

2

12

24

22

55

6

11

6

23

55

6

11

6

24

55

10

9

18

25

55

10

12

24

Table 3. The effect of change of temperature (1 oC), pH (0.1 unit), hydrogen peroxide concentration (1%), and time (1 hour), in various range, on the change of loss, protein removal (PR), cellulose content, water holding capacity (WHC), oil holding capacity (OHC) and whiteness index (WI) of coffee silverskin’s dietary fiber, and the activation energy of these characteristics. Variable

Range

Loss (%)

PR (%)

Cellulose (%)

WHC (%)

WI (%)

OHC (%)

Time (h)

1-6

8.12

8.09

5.72

6.03

0.82

4.46

Time (h)

6-12

1.09

-4.03

0.85

1.90

1.05

-0.65

Time (h)

12-18

-3.47

3.26

-1.74

2.27

3.26

-2.81

Time (h)

18-24

7.26

6.05

4.02

-1.60

-0.37

-0.45

pH

9-10

8.12

10.23

5.22

2.58

0.84

-0.66

pH

10-11

-0.68

-0.76

-0.96

-1.30

0.24

1.97

pH

11-12

2.21

1.33

1.16

-0.03

-0.34

-0.97

%H2O2

2-6

14.66

15.30

9.16

1.78

0.94

-2.75

%H2O2

6-10

-0.97

-0.21

-2.36

2.10

-0.24

1.78

Temperature (°C)

25-35

1.47

1.39

0.02

1.19

0.42

1.58

Temperature (°C)

35-45

1.33

0.18

0.97

-0.61

-0.26

-1.05

Temperature (°C)

45-55

1.02

1.77

0.17

1.29

0.20

2.98

-

134.30

85.26

45.75

54.87

18.28

79.07

Ea (kJ/kg)

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Table 4. Optimization data of alkaline hydrogen peroxide treatment parameters by response surface methodology used for the extraction of dietary fiber from coffee silverskin. parameter

Optimum value

Actual value

Goal

Lower limit

Upper limit

Temperature (°С)

37.50

37.50

In range

25

55

Time (h)

9.25

9.25

In range

1

24

10

10

In range

2

10

pH

10.50

10.50

In range

9

12

WHC

8.18

7.85

Max

3.25

9.54

OHC

5.27

5.01

Max

3.07

5.81

loss

40.05

45.27

Min

25.20

76.93

PR

89.04

83.58

Max

19.08

93.74

WI

80.30

77.54

Max

70.46

79.55

H2O2 (%v/v)

25

(b)

(a)

(c)

(d)

(e)

(f)

Fig 1. Response surface plots of combined effects: (a) temperature-time on the %loss (pH=10.5 & 6%v/v), (b) temperature- pH on the %protein removal (12.5h & 6%v/v), (c) temperature-pH on the % cellulose content (12.5h & 6%v/v), (d) time-concentration on the %water holding capacity (40 oС & pH=10.5), (e) time-pH on the %oil holding capacity (40 oC & 6%v/v), (f) time-pH on the % WI (40 o

C & 6%v/v).

26

Graphical abstract

Characterization of dietary fibre from coffee silverskin: An Optimization study using response surface methodology Fataneh Behrouzian, Asad Mohammad A

mini, Ali Alghooneh, Seyed Mohammad Ali Razavi

The desirability function values of target responses in optimized extraction process of dietary fiber from silver skin.

Research highlights  Coffee silverskin dietary fiber (DF) isolated by alkaline hydrogen peroxide method.  DF’s loss, protein, cellulose, colour, water and oil holding capacity were assessed.  The extraction procedure optimized using response surface methodology.  The desirability function approach which used for multiple optimizations was 0.89.

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