Bioactive Compounds / Food Microbiology | Food Biotechnology
Microbiol. Biotechnol. Lett. 2020; 48(3): 267-275
https://doi.org/10.4014/mbl.1912.12003
My Dong Lieu *, Thi Thuy Hang Hoang , Huyen Nguyet Tran Nguyen and Thi Kim Thuy Dang
1Faculty of Food Science and Technology, Ho Chi Minh City University of Food Industry, 1Department of Plain Cell Technology, Institute of Tropical Biology, 9/621 Ha Noi highway, Ho Chi Minh City, Vietnam
Anthocyanins are antioxidant compounds susceptible to environmental factors. Anthocyanin encapsulation into yeast cells is a viable solution to overcome this problem. In this study, the optimal factors for anthocyanin encapsulation were investigated, including anthocyanin concentration, plasmolysis contraction agent, and ethanol concentration, and response surface methodology was evaluated, for the first time. Anthocyanin from Hibiscus sabdariffa L. flowers was encapsulated into Saccharomyces cerevisiae using plasmolysis contraction agent (B: 3%–20% w/v), ethanol concentration (C: 3%–20% v/v), and anthocyanin concentration (A: 0.15–0.45 g/ml). The encapsulation yield and anthocyanin loss rate were determined using a spectrometer (520 nm), and color stability evaluation of the capsules was performed at 80℃ for 30 min. The results of the study showed that these factors have a significant impact on the encapsulation of anthocyanin, in which ethanol agents have the highest encapsulation yield compared to other factors in the study. Statistical analysis shows that the independent variables (A, B, C), their squares (A2, B2, C2), and the interaction between B and C have a significant effect on the encapsulation yield. The optimized factors were anthocyanin, 0.25 g/ml; NaCl, 9.5% (w/v); and ethanol, 11% (v/v) with an encapsulation yield of 36.56% ± 0.55% and anthocyanin loss rate of 15.15% ± 0.98%; This is consistent with the expected encapsulation yield of 35.46% and loss rate of 13.2%.
Keywords: Anthocyanin, encapsulation, ethanol, plasmolysis, Saccharomyces cerevisiae, response surface methodology
Currently, the biological function of anthocyanins such as antioxidant capacity, atherosclerosis prevention, and anticancer activity has been shown to be beneficial in treating diseases [1]. Besides, the replacement of natural food colors is receiving a lot of attention due to the health benefits they bring [2]. However, anthocyanin is easily degraded by many factors such as pH, temperature, light, oxygen, etc. [3]. This motivates studies to find methods to protect bioactive compounds from food, in which encapsulation is considered one of the most effective solutions. The main aim of these microcapsules is to protect sensitive compounds from adverse conditions such as light, moisture, oxygen, etc. [4]. Therefore, there have been many studies suggesting encapsulation methods such as using spray drying and freeze-drying methods [5, 6]. However, spray drying controls difficult, heterogeneous particle sizes, capsules are easily dissolved in water [5]. Moreover, the process of making products from the spray drying method can affect natural color compounds, leading to the need to search for encapsulation methods more effectively. Recently, there has been considerable interest in the use of encapsulation yeast cells. Because the yeast structure with their presence in human nutrition makes them attractive and a new means of encapsulation for the food industry [7]. Previous studies have shown that yeast cells are used to encapsulate bioactive compounds such as essential oil encapsulated [8], limonene flavor [9], fish oil [4], or enzymes [10]. In the study of Bishop
Plasmolysis treatment. The biomass of yeast was plasmolyzed with 5% NaCl, and the solid fraction obtained after centrifugation was mix with anthocyanin (0.15; 0.3; 0.45 g/ml) in 1 h, 120 rpm at 30°C. Biomass after encapsulated and determined encapsulation yield by measure infrared spectrum.
The effect of NaCl concentration on encapsulation efficiency into
The effectof ethanol concentration on encapsulation efficiency into
Yeast biomass (0.5 gram) after treatment plasmolysis in different NaCl concentrations is mixed with anthocyanin color solution for 1 h, 120 rpm at 30°C. Biomass was determined encapsulation yield and thermal stability of anthocyanin.
Yeast biomass (0.5 gram) treated ethanol at different concentrations was mixed with anthocyanin color solution for 1 h, 120 rpm at 30°C. Biomass was determined encapsulation yield and thermal stability of anthocyanin.
Yeast biomass (0.5 gram) after treatment plasmolysis at different NaCl concentrations was mixed with anthocyanin color solution in ethanol solution at concentrations of 1 h, 120 rpm at 30°C. Biomass was determined encapsulation yield and thermal stability of anthocyanin.
Anthocyanin encapsulated yeast cells were incubated in the water bath at 80°C for 30 min and evaluated for the thermal stability of anthocyanin.
The control sample (+) is a sample that did not take any treatment steps. Yeast cells (0.5 gram) after were cultured, which was mixed with anthocyanin fluid (0.3 g/ ml) for 1 h, 120 rpm at 30°C. The biomass was harvested by centrifugation, evaluated for encapsulation yield, and thermal stability of anthocyanin
The response surface method uses Box-Behnken design to optimize factors: color concentration (A), NaCl (B), ethanol (C). The experimental design consists of 17 experiments of three variables (A, B, C) at three levels (-1; 0; 1). Independent variables are coded -1 and 1 at low and high levels. The scope of implementation and the value are shown in Table 1. All experiments were performed three times and the average performance was obtained as the dependent variable (Y). The following quadratic polynomial equation is used to study the effect of variables on encapsulation yield and color loss rate:
Table 1 . Experimental range, level, and code of independent variables.
Independent | Unit variables | Symbol coded | Range and levels | ||
---|---|---|---|---|---|
-1 | 0 | +1 | |||
Anthocyanin | g/ml | A | 0.15 | 0.3 | 0.45 |
NaCl | % | B | 3 | 11.5 | 20 |
Ethanol | % | C | 3 | 11.5 | 20 |
where Y is the encapsulation yield and color loss rate, β0 is the constant term; β1, β2, and β3 are the coefficient of linear terms; β11, β22 and β33 are the coefficient of quadratic terms; and β12, β13 and β23 are the coefficient of cross-product terms, respectively.
Independent variables are optimized by the desired function criteria available in Design-Expert software (version 7.1.5). The goal is to maximize encapsulation yield and color loss rate while keeping variables in the corresponding test range.
The method of determining the content of anthocyanin is based on the description of Nguyen
Abs: Absorbance of the diluted solution (λmax= 520 nm).
M: Molecular weight: 465.2 (g.mol 1).
D: Dilution factor.
ε: (Delphinidin-3-glucosides): 23,700 23,700 (L.mole-1 cm-1)
l: Length of the optical path in the cuvette (1 cm)
QE: was the amount (g) of anthocyanin encapsulated
QT: amount (g) of anthocyanin in the original sample
All data are expressed in ameaningful form ± standard deviation (SD), at least 3 repetitions for each treatment. The difference between the variables is checked by using the ANOVA test. Design-Expert 7.0 software used for evaluating the effect of factors agents on the anthocyanin encapsulation yield by response surface methods.
Encapsulation yield of anthocyanin into
The concentration of encapsulation-compounds has significant effects on encapsulation yield. A high concentration of encapsulation-compound increases encapsulation yield (Fig. 2A). However, the high concentration of encapsulation compounds is also a factor in the reduction of cell loading, leading to do not increase in encapsulation yield %EY [11]. High anthocyanin concentrations (0.45 g/ml) increase the viscosity of color fluids. This makes it possible to increase the color to 0.45 g/ml by 1.5 times 0.3 g/ml, but the encapsulation yield does not increase accordingly (Fig. 2A).
The effect of plasmolysis on the encapsulation yield of encapsulation compounds into yeast cells has been published in previous studies. Plasmolysis is the phenomenon of draining water out of cells, in which cells are incubated in high saline or sugar solution [18]. Plasmolysis has the ability to increase intracellular space or rate of packaging and reduce protein, nucleic acid in cellular components [7]. Paramera
Besides NaCl, ethanol has also been shown to play a role in the physicochemical and physiological functions of widely studied cell membranes [20]. Under the action of ethanol, the double lipid layer undergoes a phase transition that significantly reduces its thickness [3]. Thus, ethanol alters the area and thickness of the lipid layer resulting in changes in the mechanical properties of permeability and diffusion of cell membranes [21]. In addition, ethanol forms a layer on the membrane, compounds that can be used to increase fluidity. These effects make anthocyanin encapsulated into yeast cells more effective than not treated by ethanol (Fig. 2C). However, soluble molecules in membranes do not always increase but can reduce fluidity (due to increased viscosity) [18]. In previous studies, a high concentration of ethanol (50% v/v) caused curcumin encapsulation yield decreased double from 33.8% to 16.6% [1], the encapsulated resveratrol only achieved 4.52% efficiency [14]. The results of this study show that increasing the concentration of ethanol increases encapsulation yield (Fig. 2C). However, when the concentration increases to 30%, the encapsulation yield increases but when affected by high temperatures, the color molecules are easily dispersed leading to a large color loss rate compared to the remaining concentrations (Fig. 2C). This can be explained by the large concentration of ethanol which completely removes the permeability of cell membranes. The results from the study show that the factors in the survey have a significant impact on the anthocyanin encapsulation yield of yeast cells (Fig. 2). As a result, finding the correlation between factors is necessary to determine the interactions for the highest encapsulation yield with the lowest color loss rate.
Table 2 shows the process variables and test data 17 runs containing 5 iterations at the central point. By applying the analysis of test data, the model for the variable is represented by the following quadratic equation in the form of encoded values:
Table 2 . Box Behnken design for independent variables encapsulation yield, and color loss rate.
Std | Run | Color (g/ml) | NaCl (%) | Ethanol (%) | EY (%) | Color loss (%) |
---|---|---|---|---|---|---|
1 | 14 | 0.15 | 3 | 11.5 | 24.45 | 10.11 |
3 | 11 | 0.15 | 20 | 11.5 | 28.15 | 15.45 |
5 | 7 | 0.15 | 11.5 | 3 | 21.15 | 11.65 |
7 | 13 | 0.15 | 11.5 | 20 | 27.98 | 13.88 |
9 | 12 | 0.3 | 3 | 3 | 18.85 | 7.5 |
10 | 1 | 0.3 | 20 | 3 | 28.55 | 16.22 |
11 | 2 | 0.3 | 3 | 20 | 26.65 | 15 |
12 | 16 | 0.3 | 20 | 20 | 25.34 | 25.05 |
13 | 6 | 0.3 | 11.5 | 11.5 | 38.75 | 13.88 |
14 | 15 | 0.3 | 11.5 | 11.5 | 37.45 | 13.21 |
15 | 5 | 0.3 | 11.5 | 11.5 | 37.45 | 14.52 |
16 | 4 | 0.3 | 11.5 | 11.5 | 35.5 | 12 |
17 | 10 | 0.3 | 11.5 | 11.5 | 36.99 | 12.78 |
2 | 3 | 0.45 | 3 | 11.5 | 27.87 | 15.41 |
4 | 8 | 0.45 | 20 | 11.5 | 29.68 | 20.45 |
6 | 9 | 0.45 | 11.5 | 3 | 29.11 | 17.68 |
8 | 17 | 0.45 | 11.5 | 20 | 31.14 | 19.88 |
In which, Y1 is the encapsulation yield, Y2 is the color loss rate, the values A, B, C are color, NaCl and ethanol respectively.
ANOVA variance analysis for the model is presented in Table 3. For the objective function Y1, the coefficient of determination (R2 = 0.9753) indicates that 2.47% of the total variation is not explained by the model. For a good statistical model, the adjusted coefficient of determination R2adj must be close to R2. As adjusted Table 3 R2adj (0.9436) near R2. Furthermore, R2pred (0.7487) matches R2adj and confirms this model significantly. Similarly, the objective function Y2 coefficient determines R2adj (0.7653) close to R2 (0.8093), R2pred (0.6651) in accordance with R2adj and confirms this model significantly. The “Lack of fit’’ 0.2602 > 0.05 and 0.0546 > 0.05, with no significance compared to pure error and indicates a suitable model to describe the test data. Inconsistent accuracy is a measure of signal/noise ratio, a ratio greater than 4 is desirable. The value of the appropriate accuracy is 17,397 and 13,216 indicates an adequate signal. Therefore, the full prediction model is within the scope of experimental variables. The importance of each coefficient is measured by the p-value and the F value is listed in Table 3. The p-value of the model is less than 0.0001 which indicates that the model is significant and can be used for darkening optimization of variables.
Table 3 . Analysis of variance for the fitted quadratic model.
Source | Sum of Squares | df | Mean Square | F-Value | p-value Prob > F | |
---|---|---|---|---|---|---|
Encapsulation yield (%EY) | ||||||
Model | 534.43 | 9 | 59.38 | 30.72 | < 0.0001 | significant |
A-Color | 32.28 | 1 | 32.28 | 16.70 | 0.0047 | |
B-Nacl | 24.15 | 1 | 24.15 | 12.49 | 0.0095 | |
C-Ethanol | 22.61 | 1 | 22.61 | 11.70 | 0.0111 | |
AB | 0.89 | 1 | 0.89 | 0.46 | 0.5186 | |
AC | 5.76 | 1 | 5.76 | 2.98 | 0.1280 | |
BC | 30.31 | 1 | 30.31 | 15.68 | 0.0055 | |
A2 | 54.46 | 1 | 54.46 | 28.17 | 0.0011 | |
B2 | 156.37 | 1 | 156.37 | 80.89 | < 0.0001 | |
C2 | 166.40 | 1 | 166.40 | 86.08 | < 0.0001 | |
Residual | 13.53 | 7 | 1.93 | |||
Lack of Fit | 8.07 | 3 | 2.69 | 1.97 | 0.2602 | not significant |
Color loss (%) | ||||||
Model | 222.42 | 3 | 74.14 | 18.4 | < 0.0001 | significant |
A-Color | 62.33 | 1 | 62.33 | 15.4 | 0.0017 | |
B-Nacl | 106.22 | 1 | 106.22 | 26.35 | 0.0002 | |
C-Ethanol | 53.87 | 1 | 53.87 | 13.37 | 0.0029 | |
Residual | 52.39 | 13 | 4.03 | |||
Lack of Fit | 48.60 | 9 | 5.4 | 5.7 | 0.0546 | not significant |
Y1: = R2 = 0.9753 R2adj = 0.9436 R2pred = 0.7487 Adeq Precision = 17.397 | ||||||
Y2: = R2 = 0.8093 R2adj = 0.7653 R2pred = 0.6651 Adeq Precision = 13.218 |
3D response surface and 2D contour plot lines are graphical representations of the regression equation which are useful for adjusting the relationship between independent variables and dependent variables. Different shapes of contour lines indicate whether the interaction between variables is significant. The 3D response surface and 2D contour plots created by the model are shown in Fig. 3. In these three variables, when the two variables are described in a three-dimensional surface cell, the third variable is tried set at 0.
From the above model and from the results of variance analysis in Table 3, it was found that the three factors of color, NaCl and ethanol have a significant influence on the objective function Y1 as the encapsulation yield (
As seen in Fig. 3A, the color interaction pairs (A) and NaCl (B) have no significant effect on the target function Y1. In this case, the color (A) and NaCl (B) do not have much interaction in accordance with the contour plots in Fig. 3A and Table 3 results when the coefficient AB is not significant (
Through 3D models and their respective contour plots, the fit of the model equation to predict the optimal response values has been checked by the selected optimal conditions. Results Table 4 shows the optimum color conditions of 0.26 g/ml, NaCl 9.38%, and ethanol 10.82%. Under such conditions, packaging efficiency was 35.46%, and the color loss rate was 13.20%. However, considering the ability to operate in actual production, the optimal conditions can be modified as follows: anthocyanin concentration 0.25 g/ml, NaCl 9.5%, ethanol 11%. Under actual modification conditions, packaging performance reached 36.56 ± 0.55% and the color loss rate was 15.15 ± 0.98% close to the predicted value (Table 4). Previous studies have shown that the concentration of color compounds, NaCl, and ethanol significantly affect encapsulation yield in yeast cells (Paramera
Table 4 . Optimal conditions, predictive values and tests at optimal conditions.
Anthocyanin color g/ml | NaCl % (w/v) | Ethanol % (v/v) | Encapsulation yield (%) | Color loss rate (%) | |
---|---|---|---|---|---|
Optimal conditions (predictions) | 0.26 | 9.38 | 10.82 | 35.46 | 13.20 |
Actual conditions (revised) | 0.25 | 9.5 | 11 | 36.56 ± 0.55 | 15.15 ± 0.98 |
Results from the study showed that anthocyanin, NaCl and ethanol concentrations both significantly affected anthocyanin encapsulation yield into yeast cells. The interaction of anthocyanin concentration and NaCl concentration as well as anthocyanin concentration and ethanol concentration are not significant. On the other hand, the interaction between NaCl and ethanol was significant and had a positive impact on encapsulation yield. Pretreatment encapsulation as plasmolysis with NaCl significantly increased encapsulation yield. In addition to combining the effects of ethanol with increased permeability, anthocyanin color molecules easily diffuse into yeast cells. The use of response surface methodology has been shown to be effective in finding concentrations of encapsulating conditions to improve encapsulation yield while keeping color loss rate at a lowest level. The conditions of the impact factors for optimal encapsulation yield and color loss rate were as follows: Color concentration 0.25 g/ml, NaCl 9.5%, ethanol 11%. Under these conditions, the test encapsulation yield was 36.56 ± 0.55% and the color loss rate was 15.15 ± 0.98%, which is close to the expected value of encapsulation yield of 35.46% and color loss rate of 13.2%. Moreover, the encapsulation stabilizes color degradation by high temperatures. Yeast cells are capable of promising applications for color compound encapsulation in food products and the application potential in food products that hightemperature requesting.
The authors have no financial conflicts of interest to declare.
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