Li Zhongwei, Xin Yuezhen, Cui Xuerong, Liu Xin, Wang Leiquan, Zhang Weishan, Lu Qinghua, Zhu Hu
College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, China.
College of Chemistry and Materials, Fujian Normal University, Fuzhou 350007, China.
PLoS One. 2017 Sep 28;12(9):e0185444. doi: 10.1371/journal.pone.0185444. eCollection 2017.
Phellinus is a kind of fungus and known as one of the elemental components in drugs to avoid cancer. With the purpose of finding optimized culture conditions for Phellinus production in the lab, plenty of experiments focusing on single factor were operated and large scale of experimental data was generated. In previous work, we used regression analysis and GA Gene-set based Genetic Algorithm (GA) to predict the production, but the data we used depended on experimental experience and only little part of the data was used. In this work we use the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time and rotation speed, to establish a high yield and a low yield classification model. Subsequently, a prediction model of BP neural network is established for high yield data set. GA is used to find the best culture conditions. The forecast accuracy rate more than 90% and the yield we got have a slight increase than the real yield.
桑黄是一种真菌，被誉为抗癌药物的基本成分之一。为了在实验室中找到优化的桑黄生产培养条件，我们进行了大量的单因素实验，并产生了大量的实验数据。在之前的工作中，我们使用回归分析和基于GA基因集的遗传算法（GA）来预测产量，但我们使用的数据依赖于实验经验，且只使用了一小部分数据。在这项工作中，我们利用培养条件中涉及的参数值，包括接种量、pH值、初始液体体积、温度、种龄、发酵时间和转速，建立了高产和低产分类模型。随后，针对高产数据集建立了BP神经网络预测模型。使用GA来寻找最佳培养条件。预测准确率超过90%，我们得到的产量比实际产量略有提高。
