基于机器学习的LIBS量化优化的剩余补偿算法
在PubMed上查看摘要
概括
此摘要是机器生成的。这项研究引入了一种新的激光诱导分解光谱 (LIBS) 算法,可以提高定量预测的准确性. 通过结合环境和样本数据,残余补偿方法显著减少了合金的预测误差.
科学领域
- 分析化学
- 光谱学
- 材料科学
背景情况
- 使用激光诱导分解光谱 (LIBS) 的定量分析经常面临预测准确性的挑战.
- 环境和样本特定的参数可以显著影响LIBS光谱数据和随后的定量预测.
- 现有的模型可能无法完全解释这些变量,导致预测错误.
研究的目的
- 开发和验证基于剩余补偿的LIBS新型定量预测算法.
- 通过将环境和样本参数整合到LIBS定量模型中,提高预测准确度.
- 在不同的回归模型和样本类型中评估拟议的算法的有效性.
主要方法
- 为LIBS定量预测开发了一种剩余补偿算法.
- 该算法与支持向量机回归 (SVR),部分最小平方回归 (PLSR),随机森林回归 (RFR) 和K-最近邻居回归 (KNNR) 模型集成.
- 使用10个元素的合金样本采用了10倍交叉验证方法.
主要成果
- 剩余补偿算法显著降低了预测的平均绝对误差 (MAEP) 和预测的平均相对误差 (MREP).
- 与最初的PLSR模型相比,MAEP和MREP的平均降低分别为51. 8%和64. 8%.
- 对于基于SVR的模型,MAEP和MREP分别减少了43.0%和51.1%,表明了广泛的适用性.
结论
- 拟议的剩余补偿算法有效地提高了LIBS定量预测的准确性.
- 纳入环境和样本参数对于改善LIBS分析性能至关重要.
- 这种方法为使用LIBS的合金提供了更可靠的元素分析方法.
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