Artificial neural network optimization of Althaea rosea seeds polysaccharides and its antioxidant activity

International Journal of Biological Macromolecules 70 (2014) 100–107

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International Journal of Biological Macromolecules journal homepage: www.elsevier.com/locate/ijbiomac

Artificial neural network optimization of Althaea rosea seeds polysaccharides and its antioxidant activity Feng Liu a , Wenhui Liu b , Shuge Tian c,∗ a b c

College of Chemistry and Chemical Engineering, Xinjiang Normal University, Xinjiang 830054, China College of Information Sciences and Technology, Xinjiang Education Institute, Xinjiang 830043, China Central Laboratory of Xinjiang Medical University, Xinjiang 830011, China

a r t i c l e

i n f o

Article history: Received 17 February 2014 Received in revised form 1 June 2014 Accepted 14 June 2014 Available online 28 June 2014 Keywords: Althaea rosea seed Polysaccharide Artificial neural network model Orthogonal test design Antioxidant activities

a b s t r a c t A combination of an orthogonal L16 (4)4 test design and a three-layer artificial neural network (ANN) model was applied to optimize polysaccharides from Althaea rosea seeds extracted by hot water method. The highest optimal experimental yield of A. rosea seed polysaccharides (ARSPs) of 59.85 mg/g was obtained using three extraction numbers, 113 min extraction time, 60.0% ethanol concentration, and 1:41 solid–liquid ratio. Under these optimized conditions, the ARSP experimental yield was very close to the predicted yield of 60.07 mg/g and was higher than the orthogonal test results (40.86 mg/g). Structural characterizations were conducted using physicochemical property and FTIR analysis. In addition, the study of ARSP antioxidant activity demonstrated that polysaccharides exhibited high superoxide dismutase activity, strong reducing power, and positive scavenging activity on superoxide anion, hydroxyl radical, 2,2-diphenyl-1-picrylhydrazyl, and reducing power. Our results indicated that ANNs were efficient quantitative tools for predicting the total ARSP content. © 2014 Published by Elsevier B.V.

1. Introduction Althaea rosea (Family Malvaceae) is a traditional Chinese herb commonly known as hollyhock. This plant is grown as an ornament and is widely distributed in Sichuan, Xinjiang, Shanghai, Jiangsu, and Fujian region of China. A. rosea is also native to China, Central Asian regions, the Middle and Near East, southern Europe, and Mediterranean [1]. The medicinal parts of A. rosea include flowers, roots, and seeds [2]. A. rosea flowers and roots have long been used in traditional Uyghur medicine for the treatment of several diseases; this plant is used as expectorant, coolant, diuretic, anti-inflammatory, febrifuge, demulcent, and astringent agent [3]. A. rosea seeds are regularly used by Uyghur physicians for the treatment of kidney and uterus inflammation. In addition, many ordinary people regularly self-medicate using this plant as diuretic and febrifuge in Xinjiang. According to the present studies, A. rosea seeds are rich in fatty oils compounds, such as linoleic acid (82.197%), oleic acid (9.195%), palmitic acid (4.756%), stearic acid (2.681%), and linolenic acid (0.328%) [4]. However, to our knowledge, few studies were devoted to the extraction of A. rosea seed polysaccharides (ARSPs), especially detailed studies on extraction

∗ Corresponding author. Tel.: +86 15999441880. E-mail address: [email protected] (S. Tian). http://dx.doi.org/10.1016/j.ijbiomac.2014.06.040 0141-8130/© 2014 Published by Elsevier B.V.

procedure and its potential bioactivity mechanism. Therefore, we reported the optimization of extracting parameters for the production and preliminary characterization of ARSPs. Polysaccharides are polymers that are concatenated by a monosaccharide; these substances are widely distributed in cell walls of microorganisms, as well as in plant and animal cell membranes. Studies on polysaccharides can be traced back in 1936 when Shear discovered the antitumor activity of polysaccharides [5]. Until the 1950s, researchers found gradually some fungi polysaccharides, and polysaccharides in higher plants have significant antitumor activity. Recently, numerous polysaccharides in medicine herbs have been found to exhibit hypoglycemic effect [6]. Since the 1970s, scientists discovered polysaccharide and its complexes in vivo as an energy resource. In addition, constituent materials exist in all cell membrane structures, which are involved in various cell activities. Numerous studies show that many plant polysaccharides have biological activities, such as immune regulation, anti-cancer, blood sugar and blood fat reduction, anti-radiation, anti-bacterial, anti-viral, anti-aging, and liver protection [7]. In addition, these activities have almost no side effects on the body. Therefore, many domestic pharmacologists, biologists, and chemists focus on these polysaccharides. Some plant polysaccharides have a vital function in disease prevention and treatment, which increases the concern about polysaccharide research. Numerous research data indicate that polysaccharides

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Table 1 Orthogonal experimental design factors and levels. Levels

(A) Extraction number

(B) Extraction time (min)

(C) Solid–liquid ratio (g/mL)

(D) Ethanol concentration (%)

1 2 3 4

1 2 3 4

30 60 90 120

1:20 1:30 1:40 1:50

60 70 80 90

that are extracted from herbs have a vital function in anti-oxidation. In addition, such polysaccharides can be explored as novel potential antioxidants. Artificial neural network (ANN) is a mathematical model or computational model that mimics biological neural network structure and function. ANNs uses a large number of coupled neurons for calculation. ANN is an adaptive system. In most cases, this system can change its internal structure based on external information. Modern neural network is a non-linear statistical data modeling tool that is used to complex relationship model between inputs and outputs, or to explore their data patterns. Currently, ANN is widely used in chemical nonlinear calibration, pattern recognition, quantitative structure/activity relationships (QSARs), optimization, process control, sensors, modeling, parameter estimation, forecasting, and other aspects. In most of the previous studies, the hot water extraction of polysaccharides process conditions have been merely optimized by the one-factor-at-a-time method. These experiments are not reflected the actual changes in the environment, because they ignore the interaction between factors presented simultaneously. Traditional single variation methods are not good for finding optimums, so more advanced optimization approaches such as the multivariable approach should be applied. Nowadays, ANN approaches are commonly applied for modeling and optimization processes. The objective of this study was to optimize extracting parameters of the total polysaccharide content from A. rosea seeds using orthogonal experimental design and artificial neural network. The preliminary structural characterization of ARSPs was estimated by Fourier transform infrared (FTIR) spectroscopy analysis. Then, in vitro antioxidant activities of ARSPs were evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH), superoxide radicals, hydroxyl radicals, reducing power activities and superoxide dismutase (SOD).

with some modifications [8,9]. The residues were dried at room temperature. Each sample was extracted by deionized water in a designed extraction number, extraction time, ethanol concentration, and solid–liquid ratio based on previously reported refluxed method with some modifications [10]. Each extract was cooled at room temperature, and was subsequently filtered. The supernatant solutions were condensed to about 100 mL. The protein was removed by crude polysaccharide extraction using sevage method (chloroform:butyl alcohol = 4:1) thrice. The polysaccharide solutions were subsequently precipitated by the addition of ethanol and were kept overnight at 4 ◦ C. The precipitates were isolated by centrifugation (4000 rpm, 5 min), washed with anhydrous ethanol thrice, and dried under vacuum freezing dryer at −45 ◦ C to obtain crude ARSPs. The polysaccharide content in A. rosea seeds was determined by colorimetry of anthrone-sulfuric acid method using glucose as the standard [11]. 2.2. Orthogonal test design extraction The single-factor experiment confirmed that four main effective factors (i.e., extraction number, extraction time, ethanol concentration, and solid–liquid ratio) affect ARSP extraction yield. An orthogonal L16 (4)4 test design (Table 1) in the extraction mode was used for the optimization of ARSP extraction conditions. Table 2 shows the nine reacting conditions used in polysaccharide extraction from A. rosea seeds were designed according to the orthogonal test. 2.3. ANN modeling

A. rosea seeds were purchased from Xinjiang Cicon Habo Uyghur Medicine Ltd Corporation. All specimens were stored in the Traditional Chinese Medicine Ethnical Herbs Specimen Museum of Xinjiang Medical University. The plant materials were identified by Yonghe Li, a chief apothecary of the Chinese Medicine Hospital of Xinjiang. A. rosea seeds were obtained from the air-dried fruit, pulverized by a disintegrator, and screened through an 80 mesh sieve. The powder was stored at 4 ◦ C until use. 2,2-Diphenyl-1-picrylhydrazyl (DPPH), pyrogallic acid was purchased from Sigma Co.(USA), Superoxide dismutase (SOD) kit was purchased from Nanjing Jiancheng Bioengineering Institute (China, 20091201). Glucose was obtained from the Tianjin Reagent Co. (Tianjin, China). All other solvents and chemicals were analytically graded and purchased from Tianjin Fu-Yu Chemical Ltd., Co. (Tianjin, China).

In recent years, ANN has been widely used as a powerful tool in simulation and optimization of extraction processes in the field of medicine herb. The back-propagation (BP) algorithm is one of the most widely used ANNs for multilayered feed-forward networks, which is proposed by a team of scientists led by Rumelhart and McCelland in 1986 [12]. BP network can obtain and store a large amount of input–output model mapping without prior determination of mathematical equations that describe the mapping relationships [13]. The learning rule of this network is to use the steepest descent method through the BP network to continuously adjust the weights and thresholds. This process will determine the mean squared error (MSE) of the network. GA is a search algorithm that emulates the adaptive processes of natural biological systems. This tool can automatically acquire and accumulate knowledge about the search space during the search and adaptive control of the search process to achieve optimal solution. In this work, a multilayer feed-forward neural network trained with an error BP algorithm was employed in constructing and training the artificial network model of the ARSP extraction (Fig. 1). The most popular network ANNs-GA was used to optimize the conditions needed to obtain the highest extraction yield of ARSPs.

2.1. Extraction and quantification of ARSPs

2.4. Physicochemical property analysis

The dried seeds of A. rosea were refluxed with petroleum ether for 6 h to remove colored materials, oligosaccharides, and some small molecular impurities according to Wu et al. and Zhang et al.

Physicochemical properties of ARSPs were determined in triplicate using the following methods: color observation, solubility test, phenol–sulfuric acid test, ␣-naphthol reaction, iodination reaction,

2. Materials and methods

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Table 2 Results and analysis of orthogonal experimental. No.

(A) Extraction number

(B) Extraction time (min)

(C) Solid–liquid ratio (g/mL)

(D) Ethanol concentration (%)

Extraction yield (mg/g)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 K1 K2 K3 K4 R

1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 15.615 18.270 16.245 16.672 2.655

30 60 90 120 30 60 90 120 30 60 90 120 30 60 90 120 15.052 15.683 18.907 17.168 3.848

1:20 1:30 1:40 1:50 1:30 1:20 1:50 1:40 1:40 1:50 1:20 1:30 1:50 1:40 1:30 1:20 15.187 16.110 16.707 17.797 2.610

60 70 80 90 80 90 60 70 90 80 70 60 70 60 90 80 15.840 16.560 16.560 17.843 2.003

23.40 27.00 37.80 36.72 31.86 24.20 40.68 39.42 32.76 32.58 33.66 30.86 32.40 31.68 39.06 30.24

Fehling’s test, carbazole reaction, FeCl3 reaction, and Coomassie brilliant blue reaction [14,15]. 2.5. FTIR spectroscopy One milligram of ARSP powder was incorporated into 300 mg KBr powder and pressed into a 1.0 mm transparent disk by 8 MPa pressure for transmission infrared spectroscopy. The organic functional groups of the ARSPs were analyzed at a 4 cm−1 phase

resolution at an average of 30 scans/min. This process was performed using a TENSOR-27 IR spectrometer in the frequency range of 4000 cm−1 to 400 cm−1 . Analysis of FTIR spectroscopy data was carried out using origin (Version 8.0) programmer. 2.6. Antioxidant activity test in vitro Antioxidant activity was determined according to the scavenging activities of ABTS radicals, DPPH radicals, superoxide anion, hydroxyl, reducing power and SOD. 2.6.1. DPPH radical scavenging activity The scavenging activity of ARSPs on DPPH radical was measured according to the method of Ge et al. with some modifications [16]. ARSP powder was dissolved in distilled water to form different concentrations of sample solutions (i.e., 40, 80, 120, 160, 200, 240, 280, 320, 360, and 400 ␮g/mL). Two-milliliter sample solutions were mixed with 2 mL 0.2 mmol/L freshly prepared DPPH ethanol solution, and Vc was used as positive control. The mixture solution was incubated for 30 min at room temperature in the dark, and the absorbance of the mixture was determined at 517 nm absorbance against a blank by spectrophotometer. The antioxidant activity of ARSPs on DPPH radical was calculated as follows: scavenging activity (%) =

Ablank517 − Asample517 Ablank517

× 100

where Ablank517 and Asample517 were the absorbance of ARSPs/Vc and DPPH solution, respectively.

Fig. 1. The flow chart of BP neural network-genetic algorithm.

2.6.2. Superoxide anion radical scavenging activity The superoxide radical scavenging abilities of all ARSPs were assessed based on the pyrogallol autoxidation method described by Popov et al. [17]. In the present study, 3 mL 0.2 mol/L Tris–HCl buffer (pH 8.2) was mixed with 3.5 mL sample solution at different concentrations (40, 80, 120, 160, 200, 240, 280, 320, 360, and 400 ␮g/mL). The mixture was then incubated at 30 ◦ C for 20 min. The reaction was initiated by adding 3 mL pyrogallic acid to the mixture with subsequent rapid shaking. The mixture was incubated at room temperature for 5 min, and the reaction was terminated by adding 1 mL concentrated HCl. The absorbance was determined at 320 nm by a spectrophotometer against blank samples, and Vc

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Table 3 Variance analysis results. Factor

Sum of square of deviations

Degrees of freedom

F-ratio

F critical value

A. Extraction number B. Extraction time (min) C. Solid–liquid ratio (g/mL) D. Ethanol concentration (%) Error

15.400 35.232 19.421 8.336 79.17

3 3 3 3 15

0.973 2.225 1.227 0.526 –

3.29 3.29 3.29 3.29 –

was used as positive control. The antioxidant activity of ARSPs on superoxide anion radical was calculated as follows: scavenging activity (%) =

Ablank320 − Asample320 Ablank320

× 100

where Ablank320 and Asample320 were the absorbance of ARSPs/Vc and Tris–HCl buffer solution, respectively. 2.6.3. Hydroxyl radical scavenging activity The hydroxyl radical-scavenging activity of polysaccharides was determined according to Fenton method described by Zhong et al. [18]. The reaction mixture contained 1 mL FeSO4 (9 mmol/L), 1 mL salicylic acid–ethanol solution (9 mol/L), 1 mL sample solution at different concentrations (40, 80, 120, 160, 200, 240, 280, 320, 360, and 400 ␮g/mL) and 1 mL H2 O2 . Vc was used as positive control. The mixture solution was incubated at 37 ◦ C for 30 min, and the absorbance of the mixture was determined at 510 nm absorbance against a blank using a spectrophotometer. The antioxidant activity of ARSPs on hydroxyl radical was calculated as follows: scavenging activity (%) =

Ablank510 − Asample510 Ablank510

× 100

where Ablank510 and Asample510 were the absorbance of ARSPs/Vc and hydroxyl radical solution, respectively. 2.6.4. Reducing power The reducing power of ARSPs was determined by the method described by Yap et al. [19]. Briefly, 1 mL sample solution at different concentrations (40, 80, 120, 160, 200, 240, 280, 320, 360, and 400 ␮g/mL) was mixed with phosphate buffer (2.5 mL, 0.2 mol/L, pH = 6.6) and potassium ferricyanide [K3 Fe(CN)6 ] (5 mL, 1%). After incubation at 50 ◦ C for 20 min, 5.0 mL trichloroacetic acid (10%, w/v) was added to the mixture to terminate the reaction, which was then subjected to centrifugation (10 min, 5000 r/min). Approximately 2.5 mL supernatant was removed and mixed with distilled water (2.5 mL) and FeCl3 (0.5 mL, 0.1%). After the reaction at room temperature for 10 min, the absorbance was measured at 700 nm, and Vc was used as positive control. 2.6.5. SOD activity SOD on the organism oxidation and antioxidation balance has a vital function [20]. In addition, this enzyme is capable of scavenging superoxide anion free radical, thus, protect cells from damage. Superoxide radicals are generated by xanthine and xanthine oxidase reaction system, which is the latter form of hydroxylamine nitrite. The color of nitrite is purple under the action of chromogenic agent, and the absorbance is measured using a visible spectrophotometer. SOD enzyme in the ARSP sample has a specific inhibitory effect on superoxide anion free radical, which results in nitrite reduction. This process lead to the absorbance value of the measuring tube lower than the control tube, and the SOD activities was calculated by the formula as follows: activity rate (%) =

Ablank550 − Asample550 Ablank550

× 100

where Ablank550 and Asample550 were the absorbance of ARSPs/Vc and SOD solution, respectively. All analyses were performed according to the operating instructions. 2.7. Statistical analysis All the experiments were repeated in triplicate, and the data were reported as mean ± standard deviation and evaluated by oneway ANOVA. Statistical analysis was performed using MATLAB mathematical software (Version 7.9) and SPSS (Version 17.0) statistical software. 3. Results and discussion 3.1. Orthogonal test design extraction In the present study, to verify whether the effect of individual factors on ARSP extraction yield was statistically significant, ANOVA was used to interpret the experimental data obtained from the orthogonal experiment L16 (4)4 optimization. The selected four factors and four levels are presented in Table 1. The analysis results of orthogonal test and the variance are listed in Tables 2 and 3. As seen from Table 2, the influence of the parameters on the ARSP extraction yields decreased in the following order: B (extraction time) > A (extraction number) > C (ethanol concentration) > D (solid–liquid ratio) according to the R values. The optimum extraction conditions obtained from the statistical analysis were A2 B3 C4 D4 . The results of experiments indicated that extraction time was the important factor in ARSP extraction, and the maximum ARSP extraction yield was 40.68 mg/g according to the ninth single factor experiment. Based on this analysis, while considering the polysaccharide extraction efficiency, 2 extraction number, 90 min extraction time, 1:50 ethanol concentration, and 90% solid–liquid ratio were the optimum extraction yield conditions of the polysaccharides. The F value of extraction number, extraction time, ethanol concentration, and solid–liquid ratio were 0.973, 2.225, 1.227, and 0.526, respectively. By verifying the optimal conditions, the ARSP extraction yield was 40.86 mg/g in triplicate higher than the maximum extraction rate of single factor. 3.2. ANN construction and optimization 3.2.1. Construction and training of the ANNs ANN is composed of an input layer (independent variables), a number of hidden layers, and an output layer (dependent variables). Single factors and the orthogonal test identified that four factors (namely, extraction number, extraction time, ethanol concentration, and solid–liquid ratio) affect the ARSP extraction yield as four input layer nodes, as well as the polysaccharide content extraction yield value as one output layer nodes, preferably established BP-ANNs (Fig. 2). According to Kolmogorov theorem, a three-layer network is sufficient to complete any n-dimensional to m-dimensional nonlinear mapping, thereby setting only one hidden layer structure. The number of hidden neurons was established based on the minimum value in the MSE of 0.005 and the maximum value in the correlation plots (R) of 0.9991. The consistency

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Fig. 2. The optimal architecture of ANN model used in this study.

between the actual value and the predicted value given by the ANNs is shown in Fig. 3. In addition, the correlation coefficient value is 0.9991, which indicates that the ANN predicted data of the experiment is very accurate. Based on the empirical formula, combined with the input and output layer nodes, hidden nodes were set from 2 to 12. Using trial-and-error method, the training time and the total error were used as indicators, which select the best node number of hidden layer for the test. By contrast, different neuron network trainings found that when the hidden layer node network is four, the network has sufficient generalization ability and output accuracy and requires less network training steps. Therefore, the network’s hidden layer nodes are found to be four. To facilitate network generalization and to avoid over-fitting, normalizing the orthogonal test L16 (4)4 data is necessary prior to training. The normalization from 0 to 1 was performed since the logistic function employed for ANNs. In the present work, the equation below was used for data normalization: Xnorm =

X − Xmin Xmax − Xmin

Considering the error convergence speed and stability of the network model, the learning rate, training objective squared error, and training steps were found to be 0.5, 0.001, and 1000, respectively. 3.2.2. Simulation and optimization of experimental conditions Based on the orthogonal test conditions, using the trained neural network models, changing one factor range, fixing the values of other factors, simulating the model parameter space using the method of genetic algorithms, analyzing the results of simulation and optimization, and simulation predictions for the optimal conditions were performed. By applying ANN-GA, the optimum extraction conditions for the highest ARSP yield were 3 extraction number, 113 min extraction time, 60.0% ethanol concentration, and 1:41 solid–liquid ratio. By verifying the optimal conditions, the ARSP experimental yield (59.85) was very close to the predicted yield of 60.07 mg/g and was higher than the orthogonal test results (40.86 mg/g). Therefore, the ANNs described in this work have good prediction.

(6)

where Xnorm is the normalized value, X is the actual value, Xmin is the minimum value, and Xmax is the maximum value.

Fig. 3. Experimental data plotted against the predicted data given by ANN model.

3.3. Physicochemical property analysis Table 4 shows the physicochemical properties of the ARSPs in triplicate. The colors of the ARSPs extracted by hot water methods

Fig. 4. FT-IR spectrum of ARSPs.

F. Liu et al. / International Journal of Biological Macromolecules 70 (2014) 100–107 Table 4 The physicochemical properties of the ARSPs in triplicate. Method

ARSPs

Color observation Solubility test Phenol-sulfuric acid ␣-Naphthol reaction Iodination reaction Fehling’s test Carbazole reaction FeCl3 reaction Coomassie brilliant blue reaction

Brown Water soluble + + − − − − +

“+” indicates “present”, “−” indicates “absent”.

105

are brown and the ARSPs were water soluble. As shown in Table 4, the results from the phenol–sulfuric acid, ␣-naphthol, iodination, Fehling’s, carbazole, FeCl3 , and Coomassie brilliant blue tests indicated that the three extractions were polysaccharides that contained some proteins, but did not contain starch, reducing sugar, uronic acid, or polyphenols. 3.4. Infrared spectroscopy analysis The FTIR spectra of carbohydrates are typically used for determination of their structural features by KBr pellets. Fig. 4 shows the FTIR spectra of the samples, which indicated the typical ARSP peaks. IR spectra of ARSPs showed absorption bands at 3409.42, 2927.31, 1627.22, 1420.37, 1137.94, 1094.19, 754.29,

Fig. 5. Antioxidant effects of ARSPs: (a) DPPH radical scavenging activity, (b) superoxide anion radical scavenging activity, (c) hydroxyl radical scavenging activity, (d) reducing power, (e) SOD activity.

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652.11, and 602.02 cm−1 . The strong and wide stretching signals at 3409.42 cm−1 were the characteristic peaks of hydrogen bonded O H (symmetrical deformation of CH3 and CH2 ) stretching vibration, and a weak absorption peak at 2927.31 cm−1 indicated C H stretching vibrations. The relatively strong absorption peaks at 1627.22 and 1420.37 cm−1 were demonstrated to be asymmetric and symmetric stretching of the carboxylate anion group (C O). The absorption bands between 1000 and 1200 cm−1 region were dominated by the characteristic of C O C glycosidic bond vibrations and ring vibrations overlapped with stretching vibrations of side group C O H link bonds. The absorption peak at 1094.19 cm−1 indicated that the polysaccharides contained pyrene monomers in their structures. The absorption peak data demonstrated that the product samples were mainly composed of polysaccharides. 3.5. Antioxidant activity 3.5.1. DPPH radical scavenging activity The DPPH radical scavenging activity of the ARSPs was determined according to the method described by Nanjo et al. The scavenging activity ability of the polysaccharide sample and Vc against the DPPH radical is shown in Fig. 5a. The scavenging activity increased with increase in polysaccharide sample and Vc concentration. The scavenging ability of the polysaccharide was higher than that of Vc at each concentration point. As seen from Fig. 5a, the DPPH radical scavenging increased from 59.33% to 89.21% when the polysaccharide concentration increased from 40 ␮g/mL to 400 ␮g/mL. At 40 ␮g/mL, the scavenging activities of polysaccharides and Vc were 59.33% and 54.92%, respectively. At 400 ␮g/mL, the scavenging activities of polysaccharides and Vc were 89.21% and 80.99%, respectively. In this assay, the results suggested that the ARSPs can be used as effective DPPH free radical scavengers according to strong scavenging ability on DPPH radical. 3.5.2. Superoxide radical scavenging activity Superoxide radical, which is a one-electron reductant of molecular dioxygen, is harmful to cellular components. This radical functions as a precursor of other ROS, such as singlet oxygen and hydroxyl radical. As shown in Fig. 5b, when the concentration was below 130 ␮g/mL, ARSPs were observed to possess obviously higher free radical scavenging activity than that of Vc. On the contrary, ARSPs exhibit radical-scavenging activities lower than Vc. In particular, at 130 mg/mL concentration, the scavenging ability of ARSPs on superoxide radicals was equivalent to that of Vc. At 400 ␮g/mL, the scavenging activity of ARSPs was 72.03%, which suggests that ARSPs have remarkable result on its superoxide radical scavenging activity. Therefore, this result indicated that the ARSPs were good scavengers for superoxide radicals. 3.5.3. Hydroxyl radical scavenging activity Hydroxyl radicals are one of representative reactive oxygen species (ROS) and can readily react with most biomacromolecules that function in living cells through the Fenton reaction. In contrast to superoxide, which can be detoxified by superoxide dismutase, the hydroxyl radical cannot be eliminated by an enzymatic reaction. Fenton reaction has been widely used for estimating the free radical scavenging activities of living systems. The results of hydroxyl radical scavenging assay are described in Fig. 5c. The hydroxyl radical scavenging increased from 79.78 to 92.52% when the concentration of the polysaccharides increased from 40 to 400 ␮g/mL, which indicated that the scavenging activity of ARSPs against hydroxyl radical was higher than that of Vc at each concentration point. At 40 ␮g/mL, the scavenging activities of polysaccharides and Vc were 79.78 and 25.93%, respectively. At 400 ␮g/mL, the scavenging activities of polysaccharides and Vc were 92.52 and 81.77%, respectively.

The present result proved that ARSPs had an appreciable power on hydroxyl radicals. 3.5.4. Reducing power According to the report, the reducing capacity of a compound or extract may be a significant indicator of its potential antioxidant activity. In this assay, the reducing power (absorbance at 700 nm) increased with increasing ARSPs and Vc concentration (Fig. 5d). The absorbance was from 0.073 to 0.374 for ARSRs and from 0.551 to 2.402 for Vc from 40 to 400 ␮g/mL. At 4 ␮g/mL concentration, the Vc reducing ability is 7.5 times that of the ARSPs. At 400 ␮g/mL concentration, the Vc reducing ability is 6.4 times that of the ARSPs. Although the ARSP activity was weaker than that of Vc, these substances showed slightly higher activity in higher concentrations. These data on ferric reducing power of the tested polysaccharides indicated that a direct correlation can exist between the antioxidant activity and reducing capacity of ARSPs. 3.5.5. SOD activity SOD belongs to an organic body’s own antioxidant enzymes (such as proteases and salivary amylase enzymes), and antioxidant supplement is a single line agent. With regard to the absorption and onset of action based on the chemical reaction of the organism and the low bioavailability of antioxidants, enzymes involved in the metabolism of all organisms must be converted in the body for absorption. Therefore, the same dose of antioxidant capacity is 1000 times that of the enzymes. The present study on SOD of ARSPs in the chromogenic agent was purple, and its absorbance was measured using a spectrophotometer (550 nm). Fig. 5e shows the SOD activity of ARSPs, and such activity is found to be lower than that of Vc at each concentration point. At 40 ␮g/mL concentration, the antioxidant enzyme activity of ARSPs is 1.33%. At 400 ␮g/mL concentration, the antioxidant enzyme activity of ARSPs is 35.67%. These data on enzymes activity assay indicated that ARSPs are rich in antioxidant enzyme resources. 4. Conclusion In the present study, a combination of an orthogonal L16 (4)4 test design and ANN model was applied to the optimization of polysaccharides from A. rosea seeds that were extracted by hot water method. The trained network had the minimum value in the MSE of 0.005 and the maximum value in the R2 of 0.9991. These results suggested a good fit and generalization of the ANN. Through ANNGA application, the highest optimal experimental yield of ARSPs of 59.85 mg/g was obtained at three extraction numbers, 113 min extraction time, 60.0% ethanol concentration, and 1:41 solid–liquid ratio. Under these optimized conditions, the ARSP experimental yield was very close to the predicted yield of 60.07 mg/g and was higher than the orthogonal test results (40.86 mg/g). Structural characterizations were conducted using physicochemical property and FTIR analysis. In addition, the study of ARSP antioxidant activity demonstrated that polysaccharides exhibited high active enzyme SOD content, strong reducing power, and positive scavenging activity on superoxide anion assay, hydroxyl radical assay, DPPH assay, and reducing power assay. This study provided important information, which suggested that the ANN model described in this work is an efficient quantitative tool in predicting the total polysaccharide content of A. rosea seeds, and that the ARSPs may be used as potential functional medicine. Acknowledgement This work was supported by Xinjiang Science and Technology Fund (2014211C011).

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