响应表面方法和人工智能方法,用于建模环境因素对绿素a在Isochrysis galbana中的影响
Linlin Zhang1, Jie Liu1, Xin Shen1
1College of Chemistry and Environment Engineering, Shandong University of Science & Technology, Qingdao 266510, China.
Microorganisms
|August 26, 2023
概括
从Isochrysis galbana中优化甲基 (Chl a) 的条件,在特定的盐度,pH值和水平下获得最高的Chl a含量. 人工智能模型准确地预测了儿童的水平,超过了传统方法.
科学领域:
- 海洋生物学 海洋生物学
- 生物技术是生物技术.
- 计算科学 计算科学
背景情况:
- 叶绿素a (Chl a) 是微藻中的一种重要色素.
- 伊索克里西斯加尔巴纳是一种常见的微藻,用于各种应用.
- 优化的生产对于最大限度地提高其效用至关重要.
研究的目的:
- 优化培养条件以最大限度地提高Isochrysis galbana中的Chl a含量.
- 为了比较响应表面方法 (RSM) 与人工智能 (AI) 模型对 Chl a 内容的预测准确度.
- 确定高效的计算智能方法用于Chl的内容估计.
主要方法:
- 单因素实验以确定影响Chl a含量的关键参数.
- 设计用于优化盐度,pH值和度.
- 开发和评估RSM,人工神经网络 (ANN),支持向量机 (SVM) 和辐射基函数神经网络 (RBFNN) 模型.
主要成果:
- 对于最高Chl a含量 (0.51毫克/升) 的最佳条件是盐度30‰,pH值8.0和度72.1毫克/升.
- 人工智能模型 (ANN,SVM,RBFNN) 显示出高预测精度,R2值分别为0.9113,0.9127和0.9185.
- 人工智能模型在预测 Chl a 的内容方面表现优于 RSM 模型.
结论:
- 优化的条件显著提高了Isochrysis galbana中的Chl a产量.
- SVM 和 RBFNN 模型对于估计 Chl a 含量是高效和准确的.
- 计算智能方法为验证微藻色素生产的实验工作提供了有价值的工具.
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