Modeling the Growth State of Pleurotus Eryngii Stipe with Genetic Algorithm–Artificial Neural Network

P. eryngii is commonly known as king oyster mushroom or king trumpet mushroom, highly valued for its superior texture, flavour and quality [1]. It is commonly cultivated in Europe, Middle East, and North America as well as in parts of Asia [2]. It is both rare medicinal and edible mushroom [3-5]. Modern pharmacological studies indicate that P. eryngii contain highly valued nutritional ingredients [6,7], as well as medicinal ingredients with medical activities including antioxidant [7-9]; cholesterol-lowering, cardiovascular diseases preventative [10-12] and antitumor activities [13-15]. Moreover, the polysaccharide of P. eryngii combined with bifidobacteria can not only improve the digestion function but also beautify human faces [16]. Therefore, P. eryngii are welcome to many consumers. However, in the process of cultivating P. eryngii under the influence of climate and geography, the output and quality are obviously unstable. In addition, the different culture conditions in the incubation room have a significant impact on the growth of P. eryngii stipe. And most companies have no scientific methods but only depend on experience to control cultivate conditions of the P. eryngii. In the process of production, we found that the culture conditions (temperature, wind speed, carbon dioxide concentration and relative humidity) of P. eryngii have a significant effect on the growth length of P. eryngii stipe. In order to reduce the production cost and raise production, the conditions of cultivating P. eryngii must be scientifically and reasonably controlled.

Moreover, the polysaccharide of P. eryngii combined with bifidobacteria can not only improve the digestion function but also beautify human faces [16]. Therefore, P. eryngii are welcome to many consumers. However, in the process of cultivating P. eryngii under the influence of climate and geography, the output and quality are obviously unstable. In addition, the different culture conditions in the incubation room have a significant impact on the growth of P. eryngii stipe. And most companies have no scientific methods but only depend on experience to control cultivate conditions of the P. eryngii. In the process of production, we found that the culture conditions (temperature, wind speed, carbon dioxide concentration and relative humidity) of P. eryngii have a significant effect on the growth length of P. eryngii stipe. In order to reduce the production cost and raise production, the conditions of cultivating P. eryngii must be scientifically and reasonably controlled.
ANN has the capacity to handle the linear and nonlinear information automatically [17,18] and is composed of a large number of processing units. ANN is used to process information and store knowledge by simulating human brain [19]. But it does not truly portray human brain network, only to simplify and simulate the information and knowledge. At present, the ANN model as a mathematical tool has been widely used in chemistry, engineering, economic administration, transportation and other fields. For instance, [20] has applied recurrent neural networks and profiles method to predict furfural and 5-hydroxymethylfurfural content of fermented lotus root the [20]. And [21] has used ANN to assessment computational aesthetics of photos quality. Topuz used ANN to predict moisture content of some agricultural products such as hazelnut, bean and chickpea [22]. Panagou and Kodogiannis made use of neural networks to model microbial growth in food and animal feed [23]. However, the application of mathematical models based on ANN in predicting the growth length of P. eryngii is relatively fewer than in other processing fields.
Genetic algorithm is a new random search and optimization method developed rapidly in recent years, its basic idea is based on Mendel's genetics and Darwin's theory of evolution by natural selection and genetic mechanisms to search for the optimal solution. Genetic algorithm is characterized by self-organizing, self-adaption and intelligence [24,25]. Therefore, it can be used to solve complex and unstructured problems and effected by the corresponding fitness and objective function. Genetic algorithms attempt to find an appropriate algorithm to correct adverse conditions for optimizing the neural network. Based on the self-adaption of genetic algorithm, neural networks would be designed in order to obtain a satisfactory convergence and adaptability.
Due to the growth of P. eryngii is a nonlinear process and is affected by many factors. Therefore, the main purpose of this study is to develop a model to predict the growth length of P. eryngii stipe.
It provided guidance for P. eryngii industrial production, reducing costs and increasing production.

Preparation of Spawn
Sawdust spawn was prepared by inoculating sawdust substrate consisting of sawdust and wheat at a 3:2 ratio with P. eryngii (Accc no.50894.) preserved in the microbiology laboratory in China Microbial Culture Collection. Culturing was performed at 22 ℃ until spawn running was apparent on the substrate.

Artificial Neural Networks
Multilayer artificial neural network consists of input layer, hidden layer and output layer [26]. The input layer is responsible for receiving information from the outside world, the output layer is responsible for the results of post-processing the output of the system, the hidden layer has a greater impact on the prediction accuracy of ANN model. Each neuron receives the information from the data processing of the previous layer, and neurons of each layer, have the same transfer function but each neuron is calculated independently. After calculation is completed, the results were passed to the next layer. Subsequently, the weights and thresholds are adjusted during the training process using gradient descent algorithm and the optimal number of iterations is determined by minimum average absolute error. Genetic algorithms is applied to optimize the artificial neural networks, which is an evolutionary algorithm and a method of searching in a simulated natural evolution process.
In this process, through genetic manipulation, selection, crossover and mutation are conducted to select fitness environment for the individuals of the population, optimizing generation to generation and the fittest surviving, until the optimal performance parameter set is selected. Before the start of the training, the back propagation neural network parameters of step length, momentum and the number of hidden layer are set as 0-1, 0-1 and 3-21 respectively [27]. The trained goal of genetic algorithms is to find minimum mean square error between experimental value and predictive value and make the performance of the neural network to achieve the best. In this study, 256 samples are divided into three Until you get a satisfactory result before this process is the need to be repeated screening [28].

Bp-Artificial Neural Network
Based on repeated BP-artificial neural network testing and training of artificial neural network, the structural parameters were to achieve optimal. The predicted values of the length of P. eryngii stipe for training set obtained using the optimal architecture of the that the method has high fitting accuracy and is a very promising prediction method [30]. Some researchers have even used ANN to build prediction and classification models for wine and soy sauce [31,32]. Among these similar conclusions, it is not difficult to find that ANN is a very promising prediction tool.

Optimized Ann Model Performance Data Set
Currently there have been no uniform standard for the current set of relevant parameters specific ANN mathematical model [33], therefore, the use of ANN to predict the relevant parameters ANN mathematical model needs to repeat test and optimization.
By repeated selection and experiments, the step, weights and the number of neurons is set, so that to achieve optimal ANN. A good mathematical model has a high R2 and a low MSE [34]. The

Sensitivity Analysis
The purpose of analysis the sensitivity of artificial neural network is to investigate the relationship between the network input and output variables. The most important factors for growth length can be quickly and effectively screened out by the known input conditions. This analysis suggests that how to optimize different models from the known input variables [35]. Among four input factors temperature is the most important factors. In the previous study, it was also proved that temperature is the most important factor affecting the growth of P. eryngii [36][37][38]. In these four input variables of temperature, wind speed, carbon dioxide concentration and relative humidity, the growth length of P. eryngii stipe is the most sensitive to temperature and following wind speed, carbon dioxide concentration and relative humidity.

Conclusion
The above study indicates that artificial neural network has proven to be a powerful tool in learning the relations between the culture conditions as input and the P. eryngii stipe length as output.
Therefore, it is possible to use BP-based artificial neural network to design better culture environment of growth of the P. eryngii stipe.
It provides guidance for increasing the production of P. eryngii and saving costs of culture process.