Part 1:Skin-Whitening And Anti-Wrinkle Effects Of Bioactive Compounds Isolated From Peanut Shell Using Ultrasound-Assisted Extraction
Mar 25, 2022
Contact: Audrey Hu Whatsapp/hp: 0086 13880143964 Email: audrey.hu@wecistanche.com
Da Hye Gam 1, Ji Woo Hong 1, Jun Hee Kim 1 and Jin-Woo Kim1,2,3,*
1 Department of Food Science, Sunmoon University, Natural Science 118, 70 Sunmoonro 221,
Tangjeong-myeon, Asan-si, Chungnam 336-708, Korea; ank7895@naver.com (D.H.G.);hgw130@naver.com (J.W.H.); jun981014@naver.com (J.H.K.)
2 FlexPro Biotechnology, Natural Science 128,70 Sunmoonro 221, Tangjeong-myeon, Asan-si,
Chungnam 336-708, Korea
3 Center for Next-Generation Semiconductor Technology, Sun Moon University,70Sunmoonro 221,
Tangjeong-myeon, Asan-si, Chungnam 336-708, Korea
*Correspondence: kimjw1028@sunmoon.ac.kr; Tel:+82-41-530-2226
Abstract:
Response surface methodology was employed to optimize the ultrasound-assisted extraction (UAE) conditions for simultaneous optimization of dependent variables, including DPPH radical scavenging activity(RSA), tyrosinase activity inhibition (TAI, and collagenase activity inhibition(CAI) of peanut shell extracts. The effects of the main variables including extraction time (5.0~55.0 min, X),extraction temperature(26.0~94.0℃℃, X2),and ethanol concentration (0.0%~99.5%, X3)were optimized.Based on experimental values from each condition, quadratic regression models were derived for the prediction of optimum conditions. The coefficient of determination (R-)of the independent variable was in the range of 0.89~0.96, which demonstrates that the regression model is suitable for the prediction. In predicting optimal UAE conditions based on the superimposing method, extraction time of 31.2 min, extraction temperature of 36.6°C, and ethanol concentration of 93.2% were identified. Under these conditions, RSA of 74.9%, TAI of 50.6%, and CAI of 86.8%were predicted, showing good agreement with the experimental values. A reverse transcription-polymerase chain reaction showed that peanut shell extract decreased mRNA levels of tyrosinase-related protein-1 and matrix metalloproteinase-3 genes in B16-FO cells. Therefore, we identified the skin-whitening and anti-wrinkle effects of peanut shell extracts at protein as well as gene expression levels, and the results show that peanut shell is an effective cosmetic material for skin-whitening and anti-wrinkle effects. Based on this study, peanut shell, which was considered a byproduct, can be used for the development of healthy foods, medicines, and cosmetics.
Keywords: peanut shell; optimization; skin-whitening; anti-wrinkle; antioxidant; tyrosinase; collage-nase; human tyrosinase-related protein-1(TRP-1); matrix metalloproteinase (MMP)
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1. Introduction
Melanin is a brown-or black-colored polymer pigment that is synthesized from the melanosomes of melanocytes in the epidermis. Its main function is to block ultraviolet (UV)rays in order to protect the skin. Alternatively, its excessive production can cause pigment darkenings, such as melasma, moles, and age spots [1-3]. Tyrosinase is a major enzyme that catalyzes autooxidation and polymerization reactions, through which tyrosine is converted into dopaquinone via dihydroxyphenylalanine and produces melanin through dopachrome during melanin biosynthesis [4]. Thus, it is widely used to reduce or attenuate melanin production through the inhibition of tyrosinase activity in order to enhance the whitening effect of cosmetics [5]. Rapid industrialization and increased use of chlorofluorocarbons severely damaged the earth's protective ozone layer, thus resulting in a greater amount of UV reaching the ground and exposing the skin. This increase in UV radiation consequently induces active generation of reactive oxygen species (ROS) in the human body, such as superoxide anions, hydrogen peroxides, and hydroxyl radicals. Such species have promoted the continuous oxidation of tyrosine, resulting in an increased production of melanin. In this regard, studies are actively conducted on the inhibition of tyrosinase activity as well as the removal of ROS in order to develop skin-whitening agents [6]. Collagen is a major extracellular matrix that comprises 90% of the dermis. Collagen protects and gives elasticity to the skin and is involved in the mechanical rigidity of the skin, resistance, and binding of connective tissues, and proliferation and differentiation of cells [7]. Proteins that make up the extracellular matrix, such as collagen, are decomposed by collagenase, such as matrix metalloproteinase (MMP), causing wrinkles, decreased elasticity, and sagging of the skin [8]. Various types of MMPs that are expressed by ROS hydrolyze the collagen chain, skin connective tissue, and generate its abnormal crosslinking to increase collagen decomposition and accelerate the formation of wrinkles [9]. For this reason, inhibition of melanin production and collagen decomposition through reduction of ROS generation has been the main focus for skin whitening and wrinkle prevention [10]. Arbutin, kojic acid, and linolenic acid, as whitening cosmetics and retinol, gallate, and adenosine, as anti-wrinkle cosmetics, have been widely used in recent years. However, the use of these materials is limited, given their instability in the presence of light and heat as well as to adverse reactions, including skin irritation and contact dermatitis [11]. With growing interest in natural antioxidants for overcoming the shortcomings of conventional whitening and anti-wrinkle ingredients, plant-derived extracts have been actively used to develop bioactive compounds for skin-friendly and safe whitening and anti-wrinkle cosmetics [12,13].
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There are various extraction methods that are currently used for the extraction of bioactive compounds from plants. However, the extraction of bioactive compounds from natural sources, especially plants, has mainly been conducted through solvent, hot-water, and Soxhlet extraction methods, which have shown various drawbacks, including low extraction efficiency, ingredient decomposition, low stability, and high cost of operation [14]. Thus, extraction methods have recently been tested, including ultrasound-assisted, microwave-assisted, and supercritical extraction [15]. In particular, ultrasound is a soundwave with a frequency of approximately 20 kHz or more, which results in compression, cavitation, and rarefaction of liquid, thereby maximizing the molecular motion in a short time to obtain high extraction efficiency [16]. Furthermore, ultrasound is advantageous, in that its short extraction time minimizes the decomposition of bioactive compounds and it is evaluated as an effective method for extracting natural ingredients with antioxidant, whitening, and anti-wrinkling properties from many plants and herbs [17]. Extraction condition optimization is essential to increase the efficiency of ultrasound-assisted extraction (UAE), and the optimization process can be performed either by experimental or statistical methods. The traditional one-factor-at-a-time method, with all variables remaining constant and changing only one factor at a time, has limitations in determining the interactive effects if it is a multivariate experiment. On the other hand, RSM provides statistical information on the correlation between variables in multivariate experiments, along with effective experiments using a minimal number of samples, as well as important mathematical and statistical techniques for evaluating the effectiveness and suitability of the regression model. For statistically-based optimization, various RSM designs, such as full factorial design, Box-Behnken design, and central composite design (CCD), have been widely used. Among them, CCD is very efficient and thus provides much information on experiment variable effects and overall experimental error, with a minimum number of required runs [18]. Therefore, in many existing studies, CCD has been widely used to develop, improve, and optimize the process conditions for extracting various antioxidants and other metabolites from natural products.
Peanut (Arachis hypogaea) is an annual plant belonging to the legume family. It is grown in more than 50 countries around the world, including South Korea, India, China, and the United States [19]. Peanuts are rich sources of protein (25%), lipids (47%), and carbohydrates (16%), as well as minerals, vitamins, niacin, unsaturated fatty acids, and oleic acids [20]. They are consumed as either unprocessed or processed products, including nut, butter, and cooking oil. It is estimated that the world’s annual peanut production amounts to 4.1 million tons in total and that the peanut‘s shell accounts for 35%~40% of the total weight of the peanut [21]. It is estimated that more than 1.5 million tons of peanut shells are discarded annually as byproducts. However, given that only a portion of peanut shells are used as animal feed and that most of them are incinerated or landfilled, causing disposal cost and environmental problems, it is necessary to produce high value-added materials using peanut shells to overcome the problem of byproducts [22]. Previous studies on antioxidants have shown that anti-inflammatory and anti-obesity activities of peanut skin extracts have been reported [23,24]. However, so far, there is no research on the production of functional cosmetic materials for improving whitening and anti-wrinkling effects using bioactive compounds from peanut shells. Therefore, this study extracted bioactive compounds from a peanut shell by employing the ultrasound-assisted extraction (UAE) to confirm their antioxidant, whitening, and anti-wrinkle effects and further presented an optimal UAE condition using the response surface method (RSM) and increased the functionality of extracts in order to confirm the possibility of its use as food, cosmetics, and medical ingredients.
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2. Results and Discussion
2.1. Fitting the RSM Models
In this work, extraction temperature, extraction time, and ethanol concentration were selected as the main variables of CCD using the preliminary one-factor-at-a-time experiment to determine the significant variables affecting UAE (Table 1). Level of each variable was established based on preliminary experiments based on the one-factor-at-a-time method. The distance of the axial points from the center point was o1.68. Then, 17 experimental runs were constructed, including 3 replicates at the center point using 3-variables and 5 level CCD. Experimental errors were minimized by randomizing the experimental order in order to minimize the impact of unexplained variability. The experimental and predicted results for the DPPH radical scavenging activity (RSA), tyrosinase activity inhibition (TAI), and collagenase activity inhibition (CAI) are shown in Table 2.
Table 1. The central composite design (CCD) for optimization of ultrasound-assisted extraction (UAE) conditions of peanut shells.
To determine the correlation between the 17 experimental runs of CCD experimental conditions and the experimental results, multiple regression models were proposed to predict the optimum levels of these 3 variables. By applying multiple regression analysis to the experimental data, dependent variables (Y) and tested variables were related by the following quadratic regression equations (Table 3).
Table 2. Experimental and predicted data on radical scavenging activity (RSA), TAI, and CAI of peanut shell extract by CCD.
No.: randomly selected experimental number; X1 : Extraction time; X2 : Extraction temperature; X3: Ethanol concentration; RSA (DPPH radical scavenging activity); TAI (tyrosinase activity inhibition); CAI (collagenase activity inhibition).
Table 3. Polynomial regression equations calculated by CCD for the optimization of UAE conditions of peanut shells.
*
*R2 (coefficient of determination), P (probability value of model); Y is the predicted response.
Analysis of variance (ANOVA) is a statistical test for analyzing experimental data. It subdivides the total variation in a dataset into component parts that are associated with specific sources of variation in order to test a hypothesis on the variables of the model or to estimate variance components [25]. Response surface analysis and ANOVA were employed to determine the coefficients, evaluate statistical significance of the model terms, and fit the mathematical models of the experimental data that aimed to optimize the overall region for response variables [26]. As established by the model, the correlation coefficients (R2) used to determine the relationship between the experimental and predicted responses by regression models were in the range of 0.8862~0.9622. This suggests that the process variables analyzed explain more than 88.6% of the independent variables. The Design Expert software was used to calculate the coefficients of the quadratic regression equations and model suitability was tested by ANOVA. According to the monomial coefficient value of quadratic regression equations are listed in Table 4and the order of priority among the main effect of independent variables is ethanol concentration (X3) > extraction temperature (X2) > extraction time (X1).
Table 4. ANOVA of the experimental results of CCD for full quadratic models.
X1: Extraction time; X2 : Extraction temperature; X3 : Ethanol concentration; Y1 : RSA; Y2 : TAI; Y3: CAI.
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2.2. Effect of Extraction Conditions on RSA
Table 2shows the experimental data of RSA according to different UAE conditions. RSA of peanut shell extract was determined in the range of 7.6%~89.9%. The highest RSA was identified under the following extraction conditions: extraction time of 55.0 min, extraction temperature of 60.0 。C, an ethanol concentration of 50.0% (Run #10). The lowest RSA of 7.6%, under an extraction time of 30.0 min, extraction temperature of 60.0。C, and ethanol concentration of 0.0%, was identified as the experimental value (Run #13). By applying multiple regression analysis, the experimental data and responses were related by quadratic regression equations (Table 3). Statistical analysis revealed that R2 of the regression model was 0.9308 (p = 0.0027), which indicates that this equation could explain 93.0% of the experimental conditions results, implying that the model was highly significant and could be used to accurately predict the response function.
The effect of an individual UAE variable at fixed levels of other variables on RSA is
predicted and shown in Figure 1a. RSA tends to increase and then decrease as all UAE
variables increase. Ethanol concentration had the greatest effect on RSA among the three UAE variables, whereas extraction time and extraction temperature had the least effect on RSA. This result is consistent with the ANOVA results in which ethanol concentration
showed a more significant effect (p = 0.0002) on RSA as shown in Table 4. The interaction effect between independent variables on RSA was visualized using 3D response surface curves. The extraction temperature and extraction time were changed simultaneously at the fixed level of ethanol concentration (Figure 2A). As the two variables (extraction temperature and time) increased, RSA increased to the maximum level and then decreased again. The highest RSA was obtained at an extraction temperature of 56.1 。 C, which therefore suggests the extraction of bioactive compounds with antioxidant potentials, such as polyphenols, increases with the destruction of plant wall components, such as lignin, at temperatures up to 56.1。 C; however, at higher temperatures, RSA was decreased due to the decomposing or polymerization of antioxidant ingredients. Figures 2B, C shows that RSA was not significantly affected by the extraction time or temperature, whereas RSA was significantly affected by ethanol concentration, which was the highest at the ethanol concentration of 61.0% and which also declined again. This result is consistent with that of the hot-water extraction experiment of Lespedeza cuneata by Kim et al. in which RSA was more affected by ethanol concentration than extraction temperature, and RSA was the maximum at the ethanol concentration range of 60%~70% [27]. These results indicate that the extraction efficiency of the binary solvent (water and ethanol) is more effective for single solvent extraction in the UAE of peanut shells.
Figure 1. Perturbation plots for effects of extraction time (X1 ), extraction temperature (X2 ), and ethanol concentration (X3 ) on the RSA, TAI, and CAI of peanut shell extract. Perturbation plots for RSA, TAI, and CAI of peanut shell extract shows all factors at a center point by changing one factor over its range while the other factors were fixed. Perturbation plots for RSA of peanut shell extract (a), Perturbation plots for TAI of peanut shell extract (b), Perturbation plots for CAI of peanut shell extract (c).
Figure 2. Response surface plots for RSA of peanut shell extract according to extraction time, extraction temperature, and ethanol concentration. RSA as a function of extraction temperature and extraction time (A), extraction time and ethanol concentration (B), and extraction temperature and ethanol concentration (C).
2.3. Effect of Extraction Conditions on TAI
Tyrosinase is an enzyme that promotes melanin production by oxidizing tyrosine in the basement layer of the epidermis and inhibition of this enzyme is essential for the enhancement of skin-whitening [28]. The TAI of peanut shells extracted via UAE, according to 17 extraction conditions, ranged from 0.34% to 51.8% (Table 2). Based on experimental values, the relationship between independent variables (X1, X2, X3) and the dependent variable (TAI) was modeled using quadratic regression equations as shown in Table 3. To evaluate the agreement between the experimental and predicted values derived by the quadratic regression models, the goodness-of-fit of the model was evaluated based on ANOVA. The R2 was 0.9622, which is close to 1 and indicates a high degree of correlation between the experimental and predicted values. the p-value is used as a tool to evaluate the significance of each coefficient and interactions between each independent variable. The UAE variables will be more significant if the p-value becomes smaller and significance was confirmed at the level of p < 0.05 [29,30]. In evaluating the effects of independent variables, the significance was determined in the order of ethanol concentration (p < 0.0001) > extraction temperature (p < 0.0598) > extraction time (p < 0.4329), which confirmed that the effect of the ethanol concentration was the most significant in TAI.
To compare the effect of UAE conditions on TAI, the perturbation plot was used to evaluate the effect of individual variables on TAI by fixing two variables at the center point. As shown in Figure 1b, TAI showed a different pattern compared to the previous RSA experiment; it increased as the ethanol concentration increased, while the extraction time did not significantly affect TAI. The significant proportional increase of TAI with ethanol concentration can be explained by the ANOVA results. TAI was significantly affected by the primary term of ethanol concentration (X3), and (p < 0.05) the quadratic term is not statistically significant, thus, showing a strong proportional relationship between TAI and ethanol concentration. The 3D response surface curve is the graphical representation of the quadratic regression equation and results of TAI, as affected by the extraction temperature (X1), extraction time (X2), and ethanol concentration (X3). Figure 3A visualizes the interaction effect of extraction time and ethanol concentration on TAI. The result confirmed that the extraction time showed no significant effect on TAI, whereas the ethanol concentration had a strong proportional relationship with TAI. Similarly, as shown in Figure 3B, TAI was more dependent on ethanol concentration than on extraction temperature and the highest TAI was attained as ethanol concentration increased to 99.5%. In exploring UAE conditions for maximum TAI, the maximum TAI conditions values were predicted to be 30.0 min, 26.3 C, and 99.5%. This result is similar to that reported by Nakamura et al. [31] In a study on the biological activity of citron leaves, when 20.0%~80.0% of ethanol was used as an extraction solvent, TAI increased in proportion in response to the increase in ethanol concentration and showed maximum value in extraction using 80% ethanol. This suggests that using a higher concentration of ethanol is advantageous in extracting bioactive compounds with skin-whitening effects from peanut shells or other plants.
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