机器学习算法用于智能决策识别和计量Cr (III) 在规格中的Cr (III)
Yunfei Lu1,2, Xin Li1, Long Yu1
1School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China.
Analytical chemistry
|December 8, 2023
概括
机器学习模型可以准确地检测和量化环境样本中的三价 (Cr(III)). 这项研究增强了用于工业和环境监测的分类分析.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 数据科学数据科学数据科学
背景情况:
- 三价 (Cr(III)) 在自然和工业来源的环境中普遍存在.
- 准确的Cr(III) 检测和量化对于环境监测和工业过程至关重要.
- 分类分析需要强大的方法来获得可靠的结果.
研究的目的:
- 应用机器学习算法用于智能决策识别和Cr (III) 的量化.
- 评估决策树 (DT),PCA-SVM和LDA模型在特种化中的有效性.
- 用光反应来准确的Cr (III) 分类和量化.
主要方法:
- 实施决策树 (DT) 模型进行分类.
- 主要组件分析-支持矢量机 (PCA-SVM) 模型的应用.
- 使用线性区分分析 (LDA) 进行度分类.
- 使用渐进的多重线性回归来通过光可视化进行精确的Cr(III) 量化.
主要成果:
- 机器学习模型在基于光的度分类方面表现出高准确度.
- PCA-SVM和LDA模型显示了对Cr (III) 检测的显著潜力.
- 逐步的多重线性回归允许使用光数据精确量化Cr.
结论:
- 机器学习算法提供了一个强大的工具,用于在规格中准确检测和量化Cr(III).
- 开发的模型对环境和工业应用具有重大影响.
- 这项研究支持在现实世界中实施用于监测的先进分析技术.
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