使用双循环收缩和分类青优化来确定海水COD光谱.
Shiwei Hou1, Yingying Zhang2, Da Yuan1
1Qilu University of Technology (Shandong Academy of Sciences), Institute of Oceanographic Instrumentation, Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061 Qingdao, China.
概括
一个新的算法,DC-CRF,通过UV-Vis光谱学改进了海水中的化学氧需求 (COD) 分析. 它显著减少了光谱数据,同时提高了复杂水样的预测准确性.
科学领域:
- 分析化学 分析化学
- 环境科学 环境科学
背景情况:
- 海水的复杂离子组成干扰了UV-Vis光谱用于化学氧气需求 (COD) 分析.
- 准确的COD量化对于监测海水质量至关重要.
研究的目的:
- 开发一种新的算法,双合约性认知随机场 (DC-CRF),用于使用UV-Vis光谱在海水中进行强大的COD确定.
- 通过减轻盐和离子的干扰来提高光谱数据分析的效率和准确性.
主要方法:
- 开发DC-CRF算法,结合基于C值的变量重要性和用于随机跳跃和收缩减弱的双循环结构.
- 应用DC-CRF与部分最小平方回归 (PLSR) 结合在州湾海水样本的UV-Vis光谱上.
- 与全频谱PLSR,RF-PLSR和CRF-PLSR进行比较,以减少变量和预测性能.
主要成果:
- 与全频谱PLSR相比,DC-CRF-PLSR在1600次代后实现了97.5%的输入变量减少.
- 该模型表现出高预测准确度,测试R平方为0.943和根平均平方误差为1.603.
- 与其他方法相比,观察到预测准确度和效率的显著改善.
结论:
- DC-CRF-PLSR方法有效地增强了UV-Vis光谱,用于复杂海水矩阵中的快速COD分析.
- 这种方法为光谱优化和在具有挑战性的环境样本中精确量化提供了有效的解决方案.
- 该研究强调了先进算法在改善水质监测分析技术方面的潜力.
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