摘要
对食用药用真菌灰树花发酵进行建模,获得使目标发酵产物达到最大产量的培养条件。运用支持向量机(support vector machine,SVM)方法进行非线性拟合,并采用遗传算法预测优化培养基成分,结果表明其能够较好预测灰树花发酵过程。运用此方法可在灰树花发酵生产过程中根据所需产物控制发酵条件与时间,具有较高指导意义。
To obtain the best medium constituents and culture conditions for maximum production of exopolysaccharides(EPS) by Grifola frondosa, nonlinear fitting was done using support vector machine(SVM) and the response variables, EPS production and mycelial biomass, were predicted using genetic algorithm. The results showed that the nonlinear model performed well in predicting the growth and EPS production of Grifola frondosa. The approach proposed in this study can provide a significant guideline to control culture conditions and time for the production of desired products by Grifola frondosa.
出处
《食品科学》
EI
CAS
CSCD
北大核心
2016年第11期143-146,共4页
Food Science
基金
安徽大学现代生物制造协同中心开放课题(20150455)
安徽大学重点教学研究项目(ZLT52015038)
关键词
支持向量机
遗传算法
发酵模型
灰树花
support vector machine(SVM)
genetic algorithm
fermentation model
Grifola frondosa
