Research of extraction behavior of heavy metal Cd in tea based on backpropagation neural network

Abstract In order to meet the increasing demand for food and beverage safety and quality, this study focused on the application of a back propagation (BP) neural network to determine the leaching rate of heavy metal in tea to improve the scientific health of tea drinking. The evaluation index and target expectations have been determined based on the extraction experiment of heavy metal Cd in tea soaking, with 3 evaluation index values taken as input layer parameters and the heavy metal extraction rate taken as output layer parameter. Then, employ the sample data standardized by min‐max linearization method to train and test the network model and get the satisfactory results, which showed that the constructed BP neural network expressed a fast convergence speed and the systematic error was as low as 0.0003509. Additionally, there was no significance between Cd leaching rate of experimental results and neural network model results by reliability testing with a correlation coefficient was .9895. These results revealed that the network model established possessed an outstanding training accuracy and generalization performance, which effectively reflected the extraction rate of heavy metal in tea soaking and improved the safety of tea drinking.

. In addition, Ma et al. (2015) studied the health risk levels through the ingestion pathway of five heavy metals in tea, namely Mn, Cu, Zn, Pb, and Cd, in which Cd had the greatest impact and the health risk value was close to the limit. Therefore, it is of great value and practical significance to study and evaluate the leaching rate of heavy metal Cd during tea soaking.
Actually, the leaching amount and leaching rate of heavy metals in tea are related to many factors such as tea type, brewing temperature, and brewing frequency (Ma et al., 2015). Moreover, the dissolution of heavy metals in tea is a dynamic process, and there are many factors that affect this process. The influence of each element is of different degree and cannot be studied simply using linear evaluation methods such as fuzzy comprehensive evaluation method (Ying & Liu, 2015). So we have to do the complicated single experiment to find the optimal value firstly and then through a large number of orthogonal experiment to seek optimal values under the influence of many factors. This method is too complicated to daily application.
These works indicate that a promising method which requires less effort and labor is needed for the research of heavy metals in tea soaking. And the information science approach as a nonlinear simulation technology, that is an artificial neural network (ANN) (Fan, He, & Lin, 2014;Hattori & Kito, 1995;Yang, Yu, & Guan, 2018) including Hopfield neural network, adaptive resonance technology, fuzzy neural network, and backpropagation (BP) neural network, is structured to study the relationship between a set of factors and the results. According to research of Wang et al. (2013), BP neural network is most widely used in various evaluation fields (Chen, 2014;Ding & Jia, 2012;Fan, Piao, & Chen, 2012;Ju, Tade, & Zhu, 2004) for its good performance of self-learning, self-adapting, self-organizing, and self-reasoning (Maier, Jain, & Dandy, 2010;Miao, Zhao, & Gao, 2010;Palani, Liong, & Tkalich, 2008), which was proposed by Rumelhart, Hintont, and Williams (1986). Yang et al. (2018)  In this reported work, in order to better understand the extraction behavior of heavy metals in tea soaking, the leaching rate of heavy metal Cd was studied and simulated with a BP neural network model established on the basis of the experimental data.
To the best of our knowledge, this is the first trial to predict the safety performance of heavy metals for tea under different process conditions through the BP neural network. This study could offer a new path for the investigation of tea brewing, and the results could be useful for guiding future experimentation and application processes of drinking quality safety to produce the desired effect.

| Chemicals
All the chemical reagents used in the experiments were of analytical grade. The tea used in the study was green tea with food production license, which was purchased from the local market of Yangjiang City, Guangdong Province. Cadmium chloride was used to make the standard working curve of heavy metal Cd. Nitric acid and hydrogen peroxide are used as auxiliary reagents for microwave digestion of tea. The fresh ultrapure water was used to prepare all solutions.

| Measurement of content of heavy metal Cd
First, the stratified sampling method was used to divide the purchased tea into 3 layers. About 1.00 g of tea randomly weighed in each layer, which was dried at 105℃ for 2 hr and with constant weight, was added to a microwave digestion tank with a capacity of 100 ml for microwave digestion by mixing in 5.0 ml HNO 3 and 2.0 ml H 2 O 2 . Along with digestion, the pressure was stable at 1.5 MPa and the digestion time was 20 min. After that, the absorbance of heavy metal Cd in the digested liquid was determined by flame atomic absorption spectrophotometry at a measurement wavelength of 228.8 nm, a lamp current of 3.0 mA, and a burner height of 6.5 mm (Shi, Ma, & Han, 2008). And the concentration of Cd was calculated according to the standard working curve of heavy metal Cd. The content of heavy metal Cd was calculated with the following equation: where m 1 (mg) is the content of heavy metal Cd in the tea added, c (mg/L) and V (L) are the concentration of Cd in the digestion solution and the solution volume, respectively. Moreover, to ensure data reliability, three independent parallel experiments were performed each time and the arithmetic mean was taken.

| Measurement of leaching rate of heavy metal Cd
According to the public's habits and behaviors of tea drinking, the batch leaching rate experiments about the effects of the number of tea soaking (from 1 to 3), soaking time (for 1, 5, 10, 20, 30, 60, and 120 min, respectively), and soaking temperature (at 50, 60, 70, 80, 90, and 100℃, respectively) on the leaching rate of heavy metal Cd were conducted. First, add 1.00 g of tea sample to a series of 100 ml beakers, respectively, followed by the addition of 50 ml fresh ultrapure water. Then, place the beakers in a water bath at m 1 = c × V the temperature from 50 to 100℃ for the time from 1 to 120 min.
Finally, the prepared supernatant solution after centrifugation at 5,000 r/min for 5 min was taken to measure the content of heavy metal Cd. To determine the effect of the number of the tea soaking on the leaching rate of Cd, the primary filter residue obtained from the first immersion would be soaked in accordance with the above conditions, and the second immersion and filter residue were produced. By analogy, the third leaching solution was obtained. The total amount of leaching is the sum of the leaching amount of three times. And based on the three influencing factors, 32 sets of experimental data were obtained. The leaching rate of heavy metal Cd can be expressed as below: where m 2 (mg) was the content of heavy metal Cd in soaking solution.

| Prediction method of neural network
The error backpropagation (BP) feedforward network is a typical feedforward network and is currently the most widely used.
The information flow of the feedforward network is transmitted from the input layer to the lower layer, and there is no feedback information flow. After the network processing, the target signal is gained from the output layer. And in the multi-layer feedforward network, the input and output neurons are connected with the outside world and directly feel the external stimulus. While the middle layer has no direct connection with the outside world, it is called the hidden layer. Since multiple hidden layers were added to the multi-layer feedforward network in the structure, the ability of artificial neural network to process complex information is greatly improved (Ding, Su, & Yu, 2011). Through training, the multi-layer network can realize the nonlinear mapping of M → N due to the introduction of hidden layer and nonlinear transfer function. A schematic diagram of the topological structure of the neural network is shown as Figure 1. Just because the mapping of any nonlinear function from input layer to output layer can be obtained by choosing appropriate network structure and hidden layers, a BP neural network model is established to predict and analyze the leaching rate of heavy metal Cd in tea by using MATLAB 7.1 software combined with experimental analysis in this paper. And the running environment is Windows XP system, and the processor is Intel Core i5 with 4.00 GB RAM.
Suppose the input layer is M with m neurons, the hidden layer is T with t inner neurons, and the output layer is N with n neurons, the transfer function of the hidden layer is f 1 , the corresponding activation function of output layer is f 2 , the output of the network is A, and the desired output is S. Then, the forward propagation process of the information is as follows: Output in the hidden layer: The error function is defined as: In addition, the back propagation of the error and the process of weight change are as follows: the weight change of the output layer from the i-th input to the k-th output: The weight change of the hidden layer from the j-th input to the i-th output is as follows: Shanker, Hu, and Hung (1996) studied the influence of data standardization on network training and proved that it has a good effect on the running of the neural network model. Generally, the method of data normalization is to transform the original data into the range of [0, 1] in order to improve the accuracy of the model, and so it is convenient for data simulation. In this work, for the sake of limiting the input and output range of sample data, all data are converted to the range of [0.1, 1] by linearization method. Therefore, there would be no data overflow or no data into the activation range with a very small slope. The linear form is given by:

| Preparation of sample data
where x min and x max are the minimum and maximum value of the sample data, respectively; x i is the sample data before conversion, and m i is the corresponding converted data. According to the experiments of the leaching rate of heavy metal, 32 groups of data about the leaching rate of heavy metal Cd in green tea were obtained. And the standardized sample data are shown in Table 1.

| Establishment of network model
The neural network is a parallel and distributed information processing network (Li, Zeng, & Shui, 2008). The network structure = m 2 ∕m 1 × 100% is composed of many neurons in general. Each neuron has a single output, which can be connected to many other neurons. The input of the network has multiple connection paths, and each connection path corresponds to a connection weight coefficient. The structure of BP neural network system directly affects its function map- Consequently, a single hidden layer in the network model was set up in this study.

| Operating platform of BP neural network
The prediction system of BP neural network is designed by using the graphical user interfaces (GUI) provided by MATLAB. For this reason, the users can employ the neural network toolbox of MATLAB more simply and conveniently. Almost all the command can be accomplished graphically in the GUI such as data input and output, network establishment, initialization, training, and simulation. It can not only avoid the relatively complicated and intuitive command program, but also reduce the time of program editing and debugging, and improve the efficiency and quality of problem-solving. The prediction system constructed in this paper has good performance for model training and predicting. And the "training" button is taken as an example to illustrate the implementation of the button control function:

| Training and predicting process
The BP neural network model is trained and simulated on the grounds of the flow chart shown in Figure 3. Operating the model, the main interface of the GUI appears. And the first work is to set up the parameters of the BP neural network. In conformity with the practice, "tansig" is used as the transfer function of hidden layer, "pureline" as the transfer function of output layer, "trainlm" as the training function, "learngd" as the learning function, and "mse" as the performance function in this model, respectively.
Besides, there is another parameter affecting the applicability of the model, that is the training accuracy which is the target value of the error performance of the network training. If it is too small, the network fits the training set well, but the training time is too long to reach the prediction result; while if it is too large, it cannot approximate the training set very well, let alone predict. So through the comparison, the training accuracy is taken to 0.001 in this work to ensure that the relative error between training set and prediction set is close.
Second, make use of the known sample data to train the network model. When the training of BP neural network is accomplished, the curve of the training process is presented. And press the button of "Draw Training Results," a training result graph will pop up. It is worth noting that when training or predicting is carried out, a warning dialog box will appear when there is a disagreement between the setting of BP neural network and the sample data input. In this case, the 27 groups of sample data randomly chosen in Table 1

| CON CLUS IONS
In this reported work, we successfully established a BP neural network based on MATLAB software and applied the network to conduct the exploratory research on the leaching rate of heavy metal Cd for tea soaking. The results of network operation showed that the systematic error reached the predetermined requirement of training accuracy as low as 0.0003509, and the correlation coefficient of the simulated data and experimental data was 0.9895, which indicated that the BP neural network expressed the capability to exactly simulate the experimental results.
This work carried out the first attempt to predict the leaching rate of heavy metal for tea soaking under different brewing conditions by employing a BP neural network. And we utilized the network model to find out the optimal process conditions of tea soaking overcoming the complicated and nonlinear influence of evaluation factors. It demonstrated that the BP neural network is scientific and objective for simulating the safety evaluation research for tea drinking by judging the leaching rate of heavy metal accurately with a reduced number of trial-and-error experimental cycles. Furthermore, it provides effective suggestions for the development of tea industry and food industry.

This work was financially supported by Guangdong Provincial Key
Laboratory of Atmospheric Environment and Pollution Control,

Department of Education of Guangdong Province, Department of
Science and Technology of Yangjiang and Yangjiang Polytechnic.

CO N FLI C T O F I NTE R E S T
The authors declare that they do not have any conflict of interest.

E TH I C A L A PPROVA L
This study does not involve any human or animal testing.