应用支持向量机预测绞股蓝茶浸提液中药用成分的含量 |
| |
引用本文: | 孙彬,娄和强,姜翌,吕洪飞.应用支持向量机预测绞股蓝茶浸提液中药用成分的含量[J].浙江师范大学学报(自然科学版),2013(4):450-458. |
| |
作者姓名: | 孙彬 娄和强 姜翌 吕洪飞 |
| |
作者单位: | 浙江师范大学化学与生命科学学院,浙江金华321004 |
| |
基金项目: | 金华市科技项目(2010-3-078;2010-3-079) |
| |
摘 要: | 通过支持向量机数学模型预测了绞股蓝茶浸提液中的总酚含量、总黄酮含量及其自由基清除能力,其中浸提条件包括11个浸提温度、5个浸提时间及3个料液比.在4个涉及的支持向量机基本核函数中,径向基核函数表现出最佳的预测效果.在径向基核函数下,用3种参数优化方法:网格参数优化法、遗传优化算法和粒子群优化算法,分别对其模型进行了参数优化.通过比较发现,用粒子群优化算法的径向基核函数支持向量机模型达到了最佳的预测效果,其测试集的相关系数分别达到了0.9628(预测总酚含量),0.9797(预测总黄酮含量)和0.9513(预测自由基清除能力).
|
关 键 词: | 绞股蓝 支持向量机 总酚 总黄酮 自由基清除能力 |
Using support vector machine to predict the medicinal ingredients of water extracts of Gynostemma pentaphyllum |
| |
Institution: | SUN Bin, LOU Heqiang, JIANG Zhao, Lü Hongfei ( College of Chemistry and Life Science, Zhejiang Normal University, Jinhua Zhejiang 321004, China } |
| |
Abstract: | The total phenols, flavonoids, and antioxidant activity of Gynostemma pentaphyllurn water extracts were predicted by using support vector machine ( SVM ). The extraction conditions included 11 incubation tem-peratures, five incubation times and three different materials to water ratios. Among the four basic kernel func-tions, namely, linear, polynomial, RBF and sigmoid, used in the study, the RBF had the best performance. Three parameter optimization methods, namely, grid search, genetic algorithm, and particle swarm optimiza- tion, were investigated. The results showed that the RBF SVM optimized by particle swarm optimization pro-duced the best correlation coefficients for the testing sets ( 0.962 8 for total phenol content, 0. 979 7 for total flavonoids content, and 0.951 3 for DPPH radical scavenging activity ). |
| |
Keywords: | Gynostemma pentaphyllum support vector machine ( SVM ) phenols flavonoids scavenging ac-tivity |
本文献已被 维普 等数据库收录! |
|