Optimization of fungi co-fermentation for improving anthraquinone contents and antioxidant activity using artificial neural networks

Food Chemistry 313 (2020) 126138

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Optimization of fungi co-fermentation for improving anthraquinone contents and antioxidant activity using artificial neural networks ⁎

T

⁎

Jiao Yanga,b, Yun Huangb, Haiyu Xua, Dongyu Guc, , Fa Xua, Jintian Tangb, , Chen Fangc, ⁎ Yi Yanga, a

School of Light Industry and Chemical Engineering, Dalian Polytechnic University, Dalian 116034, China Key Laboratory of Particle & Radiation Imaging of Ministry of Education, Department of Engineering Physics, Tsinghua University, Beijing, China c College of Marine Science and Environment, Dalian Ocean University, Dalian, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Edible fungi Co-fermentation Artificial neural networks Anthraquinone RBF neural network Antioxidant activity

The fermentation products of edible fungi are rich in anthraquinones and have a variety of activities, including the antioxidant activity. Because of the large number of combinations, it is very difficult to obtain the optimal multi-strains co-fermentation to improve the yield of anthraquinone. In the present study, an intelligent model based on artificial neural networks (ANNs) using backpropagation (BP) and radial basis function (RBF) algorithms was developed and validated to predict the anthraquinone contents in 136 two fungi and 680 three fungi co-fermented products. After experimental validation of the anthraquinone contents, the mean absolute error and the mean bias error of the results from RBF ANN were lower than those from BP ANN. The results indicated that the anthraquinone contents in A. bisporus, C. comatus and H. erinaceus co-fermentation product was the highest (2.11%). Furthermore, this co-fermentation product showed strong antioxidant activity.

1. Introduction Anthraquinones, as a natural active substances, are widely distributed in more than 30 families of higher plants, such as Polygonaceae, Leguminosae and Rubiaceae (Chen et al., 2018; Dimmer, Montoya, Mendoza, & Cabrera, 2017; Jiang et al., 2019; Yang et al., 2017), but it is also found in a few lower lichens (Bellio et al., 2017) and fungi (Li et al., 2019). In the previous study, anthraquinones with antioxidant activity were found in some edible fungi (Appiah, Boakye, & Agyare, 2017; Fouillaud, Venkatachalam, Girard-Valenciennes, Caro, & Dufossé, 2016; Gessler, Egorova, & Belozerskaia, 2013; Kong et al., 2019; Li et al., 2014). Anthraquinones, as the natural food additives, can be used in oil processing, meat processing, fruit and vegetable insurance, beverage production, which had drawn our interest. Anthraquinones have extensive and important pharmacological effects, such as laxative action (Nadal, Calore, Manzione, Puga, & Perez, 2003), antitumor (Lee, Hsu, Liu, & Wu, 2001), antimutagen (Edenharder et al., 1995), anti-inflammatory (Kuo, Meng, & Tsai, 2001), antivirus (Koyama et al., 2001), antifungal (Manojlovic, Solujic, Sukdolak, & Milosev, 2005) and antioxidative properties (Choi, Chung, Jung, Park, & Yokozawa, 2000; Sheu & Chiang, 1997). Among these activities, the antioxidant activity has attracted much attention

⁎

(Ammar, Miyamoto, Chekir-Ghedira, Ghedira, & Lacaille-Dubois, 2019; Sun et al., 2017). The previous studies indicated that the antioxidant diet can reduce the risk of many chronic diseases, such as cancer and diabetes (Nautiyal, Govindarajan, Lavania, & Pushpangadan, 2008; Singh et al., 2009). The anthraquinone-rich edible fungus is one of the best antioxidant diets. In the present study, 17 edible fungi, including Ganoderma lucidum, Tremellafuciformis, Schizophyllum commune, Morchella esculenta, Flammulina velutipes, Agaricus bisporus, Dictyophora inausiata, Grifola frondosa, Lentinula edodes, Coprinus comatus, Fermentum rubrum, Hericium erinaceus, Sparassis latifolia, Hypsizygus marmoreus, Termitomyces albuminosus, Boletus bicolor and Aspergillus cristatus, were found to contain anthraquinones. Among them, some of the selected fungi, such as T. fuciformis, M. esculenta, F. velutipes, A. bisporus, D. inausiata, G. frondosa, L. edodes, C. comatus, H. erinaceus, S. latifolia, H. marmoreus, T. albuminosus, B. bicolor, are traditional foods and can be eaten directly. Some of the selected fungi, such as G. lucidum, S. commune, G. frondosa, F. rubrum, A. cristatus, are traditional medicines. In the previous study, the extracts of G. lucidum, G. frondosa, H. erinaceus, L. edodes, M. esculenta, showed the absence of toxic effects (Kaur et al., 2018; Nitha & Janardhanan, 2008; Phan, David, Naidu, Wong, & Sabaratnam, 2013; Sharma, Tulsawani, & Agrawal, 2019; Yoshioka,

Corresponding authors. E-mail addresses: [email protected] (D. Gu), [email protected] (J. Tang), [email protected] (Y. Yang).

https://doi.org/10.1016/j.foodchem.2019.126138 Received 14 October 2019; Received in revised form 1 December 2019; Accepted 28 December 2019 Available online 07 January 2020 0308-8146/ © 2019 Elsevier Ltd. All rights reserved.

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fermentation products were dissolved in 10 mL sulfuric acid (2.3 mol/ L) and heated in a boiling water bath for 120 min (Wang, Qi, & Zhang, 2006; You et al., 2013). Then, the solution was extracted three times with dichloromethane (10 mL) in a separatory funnel by vigorous shaking and degassing several times. The dichloromethane extracts were combined and evaporated to dryness, which was dissolved in a 0.5% magnesium acetate-methanol solution (10 mL). After 10 min of reaction, the absorbance was measured at 517 nm. The content of total anthraquinones was calculated according to the standard curve of the reference standard 1,8-dihydroxyanthraquinone (Fig. S1) (Pharmacopoeia Commission, 2010).

Tamesada, & Tomi, 2010). The fermentation process was beneficial to produce the anthraquinones (Kong et al., 2019; Zhuang et al., 2019). In addition, the technology had exhibited its superiority in the application (Zhang et al., 2017). Although these fungi have similar culture conditions, how to combine these fungi to obtain the highest anthraquinone content is a challenge due to the large number of combinations. Artificial neural network (ANN) is a computational model based on the structure and functions of biological neural networks. In the last decade, ANN exhibited high accuracy and generalization ability in nonlinear statistical data modeling (Cimpoiu, Cristea, Hosu, Sandru, & Seserman, 2011; Hosu, Cristea, & Cimpoiu, 2014). ANN has achieved great success in the optimization of fermentation conditions (Iyyappan, Bharathiraja, Baskar, & Kamalanaban, 2019; Kumar, Chhabra, & Shukla, 2017; Patil et al., 2017). However, its contribution to co-fermentation is quite limited. In the present study, two common neural networks, back propagation (BP) and radial basis function (RBF) were used to predict the best fungi combination to get the highest yield of anthraquinones, respectively. The anthraquinone contents in 90 groups of co-fermented products with two or three fungi were tested. Among them, 60 groups were used for ANN training and 30 groups were used for ANN verification. A comparison of these models was discussed and the predicted results were verified by the further experiments.

2.5. Antioxidant activity assay

2.1. Material and chemicals

2.5.1. Determination of ABTS+% scavenging activity The 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid (ABTS) solution (7 mmol/L) and the potassium persulfate solution (2.45 mmol/ L) were mixed in equal volumes and stored at room temperature in the dark for 12 h to form an ABTS+% stock solution. The solution was diluted with 50% ethanol to bring the absorbance to 0.70 ± 0.02 (734 nm). 150 μL of ABTS+% working solution and 50 μL of each sample solution were mixed in a 96-well plate and reacted at room temperature for 6 min, before the absorbance was recorded at 734 nm using Varioskan Flash Multi-Mode Microplate Reader (Thermo Scientific Co., Ltd, USA). 50% ethanol solution was used as the blank and ascorbic acid was used as the positive control in this experiment (Re et al., 1999).

Ascorbic acid and 1,8-dihydroxyanthraquinone were obtained from Tianjin Damao Chemical Factory (Tianjin, China) and Sigma-Aldrich (USA), respectively. 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid (ABTS) and 1,1-diphenyl-2-picrylhydrazyl (DPPH) were obtained from Aladdin Chemical Co., Ltd (China). Other chemical reagents (analytical grade) were purchased from Tianjin Damao Chemical Factory (Tianjin, China).

2.5.2. Determination of DPPH% scavenging activity 150 μL of ethanolic 1,1-diphenyl-2-picrylhydrazyl (DPPH) solution (0.20 mmol/L) was added to 50 μL of different concentrations of sample solutions, and the mixtures were incubated for 30 min at room temperature. The absorbance was measured at 510 nm. Ascorbic acid was used as the positive control and ethanol was used as the blank (Çelik, Asfoor, Şenol, & Apak, 2019).

2.2. Fungi and seed culture

2.6. Artificial neural networks

The fungi used in our work were Ganoderma lucidum, Tremella fuciformis, Schizophyllum commune, Morchella esculenta, Flammulina velutipes, Agaricus bisporus, Dictyophora inausiata, Grifola frondosa, Lentinula edodes, Coprinus comatus, Fermentum rubrum, Hericium erinaceus, Sparassis latifolia, Hypsizygus marmoreus, Termitomyces albuminosus, Boletus bicolor and Aspergillus cristatus. They were collected and authenticated by Dr. Xianwei Zou, Tsinghua University. The fungi were cultured on potato dextrose agar at 28 °C for 2–3 days. The mycelial cells prepared from such plates were inoculated in 50 mL of sterile liquid seed culture medium (PDB) in 250 mL conical flasks and incubated for 5–7 days on ZD-85 rotary shaker (Ronghua Instrument Manufacturing Co., Ltd, China) at 140 rpm and 25 °C in the dark.

The artificial neural network (ANN) is a mathematical algorithm inspired by the studies on the brain and nervous systems. It can connect the input and output parameters, learn from examples through iteration with no need of prior knowledge about the relationships between the process variables (Palancar, Aragón, & Torrecilla, 1998). Back Propagation (BP) ANN and radial basis function (RBF) ANN were two typical feed-forward neural network models. BP ANN consisted of layers of parallel processing elements called neurons, with each layer being full connected to the preceding layer by weights (Nourani, Sharghi, & Aminfar, 2012). RBF ANN consisted of three layers. A layer of input neurons fed the feature vectors into the network. A hidden layer calculated the outcome of the basis functions. A layer of output neurons calculated a linear combination of the basis function (Alp & Cigizoglu, 2007). Their main differences were that in the latter the connections between the input and hidden layer were not weighted and that the transfer functions on the hidden layer were radially symmetric in the RBF ANN (Jayawardena, Achela, & Fernando, 1998). In the present study, they were used to predict the contents of anthraquinones. Both of them consisted of the input layer, the hidden layer and the output layer. The contents of anthraquinones in 17 sets of fungal fermentation products were used in the input layer. The numbers of hidden layers, training epochs and activation functions were optimized using a trial and error approach. In the study, the anthraquinone contents of 60 sets of co-fermentative products, including 24 sets of co-fermentation products with two fungi and 36 sets of co-fermentation products with three fungi, were used for training the BP and RBF ANN models. The anthraquinone contents in 30 sets of co-fermentative products, including 9 sets of co-fermentation products with two fungi and 21 sets of co-

2. Materials and methods

2.3. Fermentative conditions The fungus or mixed fungi were cultured in 250 mL conical flasks at 25 °C for 7 days on rotary shaker (140 rpm). The fermentation medium contained 50 g/L of glucose, 2.5 g/L of peptone, 3.0 g/L of KH2PO4, 1.5 g/L of MgSO4·7H2O, 0.25 g/L of FeSO4·7H2O and 20 mg/L of vitamin B1. 2.4. Extraction and determination of anthraquinones The fermentative products were centrifuged at 5000 rpm for 10 min using a Neofuge 1600R centrifuge (Heal Force Bio-Meditech Holdings Limited, China). The supernatant was sterilized at 120 °C and 0.1 MPa for 28 min, and then concentrated to dryness. 100 mg of dried 2

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fermentation products with three fungi, were used for validation of the BP and RBF ANN models. The model performance was evaluated according to the mean absolute error (MAE) in Eq. (1) and the mean bias error (MBE) in Eq. (2) between the predicted and experimental values. The more ANN architectures were optimized，the lower MAE was obtained. The ANN model was developed using the ANN toolbox in Matlab 2018Ra (Mathworks, USA).

MAE =

MBE =

Table 1 Anthraquinone contents and antioxidant activities in the products from cofermentation with two fungi. No.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

N

1 N

∑ |Hi − Hi,model|

1 N

∑ |Hi − Hi,model|

i=1

(1)

N i=1

(2)

where Hi was the experimental value, Hi,model was the network output value, N represented the number of predictions (data points) that were used to calculate the MAE from the target values. 2.6.1. BP neural network In BP neural network model, the measured anthraquinone contents of different single fungus productions were selected as the input layer, and the output layer was the anthraquinone contents of co-fermented products with two or three fungi. The nodes of hidden layer were selected according to Eq. (3).

m=

(3)

n+1 +α

where m, n and l were the number of the nodes in the hidden layer, input layer and output layer, respectively. α was a constant from 1 to 10. The number of α was input to obtain the best numbers of node in the trial and error method (Ma et al., 2014; Xu et al., 2013). After repeated optimization and calculation, the number of nodes was set at 14. Tangent sigmoid transfer function (tansig) (Eq. (4)) and Log-sigmoid transfer function (logsig) (Eq. (5)) were employed at the hidden and output layer, respectively (Gitifar, Eslamloueyan, & Sarshar, 2013; Vani, Sukumaran, & Savithri, 2015). The training function was traingdx (Nair et al., 2016). MSE was defined as a network performance function (Eq. (6)) (Valim et al., 2017). Epoch was the times that the entire training dataset had been run through for the model training (Kumar et al., 2017). The goal was to maximize the probability that the selected function had a low generalization error. The learn rate (lr) was an important parameter. The low learning rate meant the training was more reliable. However, the optimization would take a lot of time. In contrast, the high learning rate meant the training may not converge or even diverge. The optimized epochs, goal and lr were set at 1000, 0.0001 and 0.1, respectively.

tan sig(x ) =

2 −1 (1 + e−2x )

(4)

log sig(x ) =

1 −1 (1 + e−x )

(5)

Fungi

F8 + F12 F6 + F10 F8 + F10 F1 + F10 F6 + F8 F6 + F9 F6 + F17 F6 + F12 F9 + F12 F6 + F13 F5 + F10 F10 + F12 F3 + F12 F9 + F12 F5 + F12 F6 + F7 F7 + F10 F3 + F10 F5 + F6 F4 + F12 F2 + F10 F7 + F10 F2 + F12 F4 + F10 F2 + F10 F4 + F6 F12 + F17 F5 + F6 F10 + F12 F12 + F13 F8 + F10 F10 + F11 F6 + F11 F4 + F6 F10 + F13 F11 + F12 F10 + F17

Anthraquinone (%)

2.01 1.77 1.75 1.74 1.67 1.54 1.52 1.52 1.48 1.46 1.45 1.33 1.32 1.31 1.30 1.25 1.21 1.19 1.13 1.06 0.98 0.97 0.94 0.90 0.85 0.84 0.84 0.78 0.83 0.83 0.71 0.66 0.66 0.62 0.23 0.22 0.21

Antioxidant activity (IC50, μg/mL) DPPH

ABTS

18.02 ± 0.39 19.14 ± 0.34 19.51 ± 0.27 19.72 ± 0.42 19.77 ± 0.83 20.13 ± 0.23 20.34 ± 0.92 20.45 ± 0.53 20.43 ± 0.33 20.58 ± 0.93 20.73 ± 0.33 20.82 ± 0.23 21.09 ± 0.83 21.63 ± 0.34 22.01 ± 0.73 22.85 ± 0.82 24.22 ± 0.93 25.33 ± 0.92 27.30 ± 0.54 28.07 ± 0.62 28.66 ± 0.83 34.07 ± 0.53 35.74 ± 0.56 43.52 ± 0.34 52.88 ± 0.82 73.65 ± 0.12 60.83 ± 0.85 78.18 ± 0.13 73.71 ± 0.13 78.02 ± 0.67 103.08 ± 0.79 119.43 ± 0.35 123.99 ± 0.54 120.62 ± 0.68 164.03 ± 0.12 163.74 ± 0.15 168.45 ± 0.33

16.14 ± 0.32 18.24 ± 0.83 18.35 ± 0.93 18.76 ± 0.83 18.78 ± 0.63 19.13 ± 0.93 19.29 ± 0.62 19.40 ± 0.93 19.47 ± 0.53 19.75 ± 0.72 19.74 ± 0.92 19.83 ± 0.93 20.09 ± 0.72 20.02 ± 0.92 20.38 ± 0.92 20.68 ± 0.72 22.04 ± 0.52 22.49 ± 0.43 23.68 ± 0.52 23.73 ± 0.73 24.52 ± 0.93 28.58 ± 0.93 29.10 ± 0.93 34.45 ± 0.82 35.88 ± 0.72 35.67 ± 0.72 36.05 ± 0.63 72.42 ± 0.43 40.14 ± 0.39 48.33 ± 0.30 78.39 ± 0.93 81.34 ± 0.93 85.63 ± 0.83 101.78 ± 0.72 63.72 ± 0.27 61.37 ± 0.26 65.48 ± 0.46

Note: F1-F17 represent G. lucidum, T. fuciformis, S. commune, M. esculenta, F. velutipes, A. bisporus, D. inausiata, G. frondosa, L. edodes, C. comatus, F. rubrum, H. erinaceus, S. latifolia, H. marmoreus, T. albuminosus, B. bicolor, and A. cristatus, respectively.

where σj was the width of the jth neuron, and xi and cj were the input and the center of RBF unit, respectively. αj was the notation for the output of jth RBF unit. The operation of the output layer was linear, which was given in Eq (8). The number of nodes in hidden layer was set at 76. Maximum number of neurons was MN. Number of neurons to add between displays was DF. Mean squared error goal was goal. The optimized MN, DF and goal were 60, 1 and 0.01, respectively. N

MSE =

1 N

yk (x) =

N

∑ (Hi − Hi,model )2 i=1

∑ wjk αj (x ) + bk j=1

(6)

(8)

3. Results and discussion 2.6.2. RBF neural network The RBF neural network consisted of input layer, hidden layer and output layer. The anthraquinone contents of different single fungus were also selected as the input layer similar to BP ANN. Radial basis activation function and linear function were used at the hidden layer and output layer, respectively. In hidden layer, as a common Gaussian function, the transfer function was defined as Eq. (7) (Ghritlahre & Prasad, 2018).

αj (x) = exp ⎛⎜ ⎝

3.1. Anthraquinone contents and antioxidant activity of single fermented products 17 fungi were selected, including G. lucidum, T. fuciformis, S. commune, M. esculenta, F. velutipes, A. bisporus, D. inausiata, G. frondosa, L. edodes, C. comatus, F. rubrum, H. erinaceus, S. latifolia, H. marmoreus, T. albuminosus, B. Bicolor, and A. cristatus. The anthraquinone contents of single fermented fungus were investigated and listed in Table S1. The results showed that the contents of anthraquinone ranged from 0.04% to 0.97%, and the anthraquinone content of A. bisporus was the highest.

∥x i − cj ∥2 ⎞ 2σ j2

⎟

⎠

(7) 3

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Table 2 Anthraquinone contents and antioxidant activities in the products from co-fermentation with three fungi. No.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

Fungi

F1 F1 F6 F5 F1 F1 F3 F1 F1 F5 F5 F1 F2 F1 F6 F3 F5 F5 F1 F5 F6 F2 F6 F2 F1 F5 F4 F1 F2 F1 F7 F2 F1 F1 F1 F1 F4 F1 F2 F3 F3 F6 F7 F5 F2 F3 F3 F3 F6 F7 F2 F1 F6

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Anthraquinone (%)

F4 + F12 F9 + F12 F7 + F10 F6 + F10 F6 + F9 F2 + F12 F7 + F10 F4 + F12 F9 + F10 F8 + F12 F6 + F9 F4 + F10 F6 + F10 F2 + F10 F7 + F12 F10 + F12 F8 + F10 F6 + F8 F9 + F12 F7 + F10 F10 + F12 F4 + F6 F8 + F10 F6 + F12 F9 + F10 F7 + F12 F5 + F6 F10 + F12 F10 + F12 F4 + F10 F10 + F12 F6 + F12 F6 + F7 F5 + F6 F3 + F6 F6 + F8 F6 + F7 F4 + F6 F5 + F6 F10 + F12 F6 + F13 F10 + F11 F11 + F12 F12 + F13 F10 + F11 F11 + F12 F5 + F10 F10 + F11 F8 + F13 F10 + F11 F6 + F10 F2 + F10 F7 + F13

1.64 1.32 1.31 1.30 1.27 1.25 1.24 1.22 1.22 1.21 1.20 1.19 1.16 1.15 1.15 1.15 1.14 1.10 1.07 1.05 1.01 1.00 0.99 0.94 0.92 0.92 0.91 0.90 0.89 0.89 0.86 0.84 0.83 0.82 0.75 0.74 0.57 0.54 0.45 0.42 0.37 0.34 0.34 0.31 0.28 0.26 0.23 0.21 0.20 0.19 0.17 0.17 0.12

Antioxidant activity (IC50, μg/mL) DPPH

ABTS

19.94 ± 0.63 20.84 ± 0.52 21.66 ± 0.92 21.75 ± 0.93 22.14 ± 0.63 22.85 ± 0.52 23.02 ± 0.93 23.45 ± 0.82 23.96 ± 0.93 24.22 ± 0.93 24.33 ± 0.62 24.85 ± 0.91 25.76 ± 0.94 26.67 ± 0.92 27.28 ± 0.72 27.33 ± 0.73 27.24 ± 0.93 27.30 ± 0.84 27.91 ± 0.72 27.73 ± 0.52 28.44 ± 0.82 28.65 ± 0.82 28.56 ± 0.92 35.11 ± 0.53 39.73 ± 0.93 39.94 ± 0.83 43.35 ± 0.73 44.46 ± 0.93 44.47 ± 0.82 48.58 ± 0.33 52.47 ± 0.57 61.08 ± 0.83 73.64 ± 0.61 78.17 ± 0.41 90.88 ± 0.17 90.84 ± 0.35 128.23 ± 0.62 144.86 ± 0.61 145.53 ± 0.81 145.71 ± 0.12 146.67 ± 0.51 147.28 ± 0.12 147.33 ± 0.42 147.21 ± 0.10 147.63 ± 0.12 159.55 ± 0.83 159.97 ± 0.91 172.61 ± 0.65 184.65 ± 0.81 188.63 ± 0.22 190.51 ± 0.66 191.28 ± 0.13 198.45 ± 0.35

19.02 ± 0.82 20.08 ± 0.91 20.03 ± 0.83 20.17 ± 0.13 20.45 ± 0.92 20.64 ± 0.53 21.09 ± 0.53 21.37 ± 0.73 21.68 ± 0.63 21.71 ± 0.92 22.09 ± 0.72 22.55 ± 0.63 22.17 ± 0.92 22.71 ± 0.82 22.57 ± 0.52 22.68 ± 0.72 23.25 ± 0.92 23.38 ± 0.63 23.72 ± 0.72 23.77 ± 0.93 23.91 ± 0.63 24.46 ± 0.43 23.96 ± 0.72 29.80 ± 0.92 33.14 ± 0.33 33.89 ± 0.35 34.33 ± 0.32 35.41 ± 0.73 35.74 ± 0.83 35.87 ± 0.92 35.97 ± 0.23 38.09 ± 0.63 42.87 ± 0.23 54.64 ± 0.73 77.80 ± 0.92 77.14 ± 0.92 114.09 ± 0.34 34.59 ± 0.51 38.54 ± 0.72 38.19 ± 0.23 38.74 ± 0.83 38.85 ± 0.92 38.69 ± 0.12 43.82 ± 0.83 63.16 ± 0.93 63.38 ± 0.72 63.73 ± 0.92 67.83 ± 0.81 70.19 ± 0.73 75.09 ± 0.82 79.65 ± 0.63 79.16 ± 0.93 102.65 ± 0.82

Note: F1-F17 represent G. lucidum, T. fuciformis, S. commune, M. esculenta, F. velutipes, A. bisporus, D. inausiata, G. frondosa, L. edodes, C. comatus, F. rubrum, H. erinaceus, S. latifolia, H. marmoreus, T. albuminosus, B. bicolor, and A. cristatus, respectively.

product. In order to improve the antioxidant activity of the fungi, it was necessary to increase the content of anthraquinones. However, the combinations of two or three fungi were 136 and 680, respectively. If the anthraquinone content was determined one by one through experiments, the workload was very large. To cope with this problem, the method of ANN was introduced into our study. ANN had the capability of screening the best fungi combination through a small number of experiments and mathematical models, so as to improve the yield of anthraquinones. The ANN method was established by the anthraquinone contents data obtained from 90 random co-fermentation products (Tables 1 and 2). 90 combinations were randomly selected for fermentation, and anthraquinone content and antioxidant activity in the fermentation products were determined,

Table S1 also listed the DPPH and ABTS free radical scavenging activities of the 17 single fermented fungus. The results showed that the antioxidant activities increased as the anthraquinone contents increased (Table S1). The fermentative product of A. bisporus showed the best antioxidant activity. The IC50 of DPPH and ABTS free radical scavenging activities were 21.21 ± 0.34 μg/mL and 20.43 ± 0.43 μg/ mL, respectively. 3.2. Anthraquinone contents and antioxidant activity of random cofermented products with two or three fungi The above results indicated that the antioxidant activity was positively correlated with the anthraquinone contents in the fermented 4

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Fig. 1. Training database (A) and error (B) of BP and RBF neural networks.

Fig. 3. The predicted anthraquinone contents using RBF neural network. (A) Heat-map of co-fermentation with two fungi; (B) 3D response surface curve of co-fermentation with three fungi.

the accuracy of models. Therefore, the data used to build the model in this study was twice as much as the data used for validation. Besides the anthraquinone contents, the corresponding antioxidant activities, DPPH and ABTS scavenging abilities were also measured and listed in Tables 1 and 2. The results verified again that the content of anthraquinone was proportional to the antioxidant activity of the products.

3.3. Establishment of neural networks 3.3.1. Training of BP and RBF neural networks The BP and RBF neural networks were trained by 60 sets of cofermented fungi. Fig. 1 showed the anthraquinone contents obtained from the experiments and neural networks. The data were listed in Tables S2 and S3. As a result, six predictive values obtained from BP network were significantly higher than the experimental values (Fig. 1A and Table S2) and the absolute errors were ranged from −11.01 to −6.81 (Fig. 1B and Table S2). MAE and MBE from BP network were −1.02 and 1.23, respectively, whereas MAE and MBE from RBF network were 0.01 and 0.06, respectively. So the predictive results from

Fig. 2. Validation database (A) and Error (B) of BP and RBF neural networks.

which was used to establish the ANN models. Among them, 60 data were used to train ANN and the other 30 data were used to validate ANN. In general, more data need to be used to build the model to obtain 5

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Fig. 4. Experimental validation of the partial predicted results.

3.4. Optimization of co-fermentation with two or three fungi

Table 3 RBF neural network random validation results. No.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Fungi

F6 + F10 + F12 F6 + F12 + F17 F8 + F12 + F17 F6 + F8 + F12 F6 + F9 + F12 F8 + F10 + F12 F5 + F6 + F12 F1 + F8 + F12 F5 + F9 + F12 F6 + F12 + F13 F4 + F12 F1 + F6 + F12 F3 + F6 + F12 F5 + F10 + F12 F1 + F6 F8 + F11 F14 + F15 F6 + F14 F8 + F13 F14 + F15 + F17

3.4.1. Prediction of fungi combination by RBF network RBF network was used to predict the anthraquinone contents in 136 sets of two fungi co-fermented products (Fig. 3A and Table S6) and 680 sets of three fungi co-fermented products (Fig. 3B and Table S7). The predictive contents of anthraquinone ranged from 0 to 2.01% in the two fungi co-fermented products. Among them, the anthraquinone content in the co-fermented products of G. frondosa and H. erinaceus was the highest. On the whole, the contents of anthraquinone can be increased by co-fermentation of two fungi. For example, the anthraquinone contents were 0.97% and 0.07% in single fungus fermentation using A. bisporus and D. inausiata, respectively, while the anthraquinone content can be increased to 1.25% in their coupled fermentation. But, there were also some fungi whose anthraquinone contents decreased after cofermentation, which indicated that these fungi inhibited the anthraquinone production each other. For example, the anthraquinone content in the co-fermented product of T. fuciformis and F. Rubrum was approximately 0 (Table S1). The predicted anthraquinone contents in 136 sets of two fungi co-fermented products were listed in Table S6. In 680 sets of three fungi co-fermented products, the predicted anthraquinone contents were from 0 to 2.11% (Fig. S2 and Table S7). In order to show the best combination clearly, some data with low anthraquinone contents were removed and the results were showed in Fig. 3B. The best combination was A. bisporus, C. comatus and H. erinaceus (Fig. 3B and Table S7), which contained 2.11% anthraquinone (Table S1). It is noteworthy that the content of anthraquinones fermented by three fungi was higher than that by two fungi. Some specific mix of three fungi, such as A. bisporus, C. comatus and H. erinaceus, can promote the production of anthraquinone when they were fermented together.

Anthraquinone (%) Predictive values

Experimental values

Error

2.11 1.74 1.63 1.42 1.41 1.13 1.18 1.06 1.06 1.06 1.29 1.05 1.02 1.02 1.27 0.64 1.20 0.61 0.65 1.42

2.11 1.74 1.63 1.41 1.41 1.135 1.18 1.06 1.07 1.06 1.36 1.05 1.02 1.03 1.40 0.57 1.16 0.43 0.48 1.59

0 0 0 0 0 0 0 0 −0.01 0 −0.07 0 −0.01 −0.01 −0.13 0.07 0.04 0.18 0.17 −0.17

RBF network were more accurate than that from BP network.

3.3.2. Validation and comparison of neural networks To verify the established neural networks, the anthraquinone contents of another 30 sets of co-fermented fungi were predicted by BP and RBF networks, and compared with the experimental data (Fig. 2, Tables S4 and S5). Similarly, the deviations of the predicted values from BP network were obviously larger than those from RBF network (Fig. 2A), and the absolute errors of 4 sets of data from BP network were particularly large (Fig. 2B). MAE and MBE from BP network were −0.38 and 0.68, respectively, which were lower than the previous training data. However, the data obtained from BP network were still not as accurate as the data obtained from RBF network, the MAE and MBE from RBF network were −0.00 and 0.06 respectively. Based on the training and validation results, RBF neural network was selected to optimize the best combinations.

3.4.2. Validation of co-fermentation results In order to verify the accuracy of the predictive values of RBF neural network, 20 sets of combinations, including A. bisporus, C. comatus and H. erinaceus combination, were selected for further experimental verification (Fig. 4). The results showed that the absolute errors of the predictive data and the experimental data of the 20 samples were approximately 0 (Table 3), and MAE and MBE were approximately 0.00 and 0.04, respectively. 6

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3.5. Antioxidant activity of optimum co-fermented product

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Fig. S3 showed the DPPH and ABTS free radical scavenging activities of the co-fermented product of A. bisporus, C. comatus and H. erinaceus. When the concentration was 1 μg/mL, DPPH and ABTS free radical scavenging rates were 17.91% and 16.13%, respectively. As the concentration increased, the scavenging rates were increased significantly. When the concentration was 100 μg/mL, the scavenging rates reached to 83.70% and 80.55%, respectively. The IC50 of DPPH and ABTS free radical scavenging activities were 10.96 ± 0.74 μg/mL and 13.37 ± 0.41 μg/mL, respectively. 4. Conclusions A co-fermentative method was developed and established with the help of artificial neural networks to increase the yield of anthraquinone and enhance the antioxidant activity of fungi. The anthraquinone contents of 816 co-fermentation products of two and three fungi were predicted by the BP and RBF neural networks, and the results showed that the anthraquinone content in co-fermented product of A. bisporus, C. comatus and H. erinaceus was the highest (2.11%). Furthermore, the DPPH and ABTS assay indicated that the co-fermented product possessed the strongest antioxidant activities. The present strategy is useful to optimize the mix of fungi for co-fermentation. CRediT authorship contribution statement Jiao Yang: Data curation, Formal analysis, Writing - original draft. Yun Huang: Software, Data curation. Haiyu Xu: Investigation. Dongyu Gu: Project administration, Funding acquisition. Fa Xu: Methodology. Jintian Tang: Resources, Supervision. Chen Fang: Investigation, Writing - review & editing. Yi Yang: Conceptualization, Data curation, Writing - original draft. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement This work was supported by grants from Natural Science Foundation of Liaoning Province (No. 2019-MS-033). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foodchem.2019.126138. References Alp, M., & Cigizoglu, H. K. (2007). Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environmental Engineering & Software, 22, 2–13. Ammar, R. B., Miyamoto, T., Chekir-Ghedira, L., Ghedira, K., & Lacaille-Dubois, M. A. (2019). Isolation and identification of new anthraquinones from Rhamnus alaternus L and evaluation of their free radical scavenging activity. Natural Product Research, 33, 280–286. Appiah, T., Boakye, Y. D., & Agyare, C. (2017). Antimicrobial activities and time-kill kinetics of extracts of selected Ghanaian mushrooms. Evidence-based Complementary and Alternative Medicine, 2017, 4534350. Bellio, P., Pietro, L. D., Mancini, A., Piovano, M., Nicoletti, M., Brisdelli, F., et al. (2017). SOS response in bacteria: Inhibitory activity of lichen secondary metabolites against Escherichia coli RecA protein. Phytomedicine, 29, 11–18. Çelik, S. E., Asfoor, A., Şenol, O., & Apak, R. (2019). A new screening method for argan oil adulteration with vegetable oils: An on-line HPLC assay with post-column detection utilizing chemometric multi-data analysis. Journal of Agricultural and Food Chemistry, 67, 8279–8289. Chen, A., Sun, L., Yuan, H., Wu, A., Lu, J., & Ma, S. (2018). Simultaneous qualitative and

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