...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Hazardous Materials >Spectral fluorescence signatures and partial least squares regression: model to predict dissolved organic carbon in water
【24h】

Spectral fluorescence signatures and partial least squares regression: model to predict dissolved organic carbon in water

机译：光谱荧光特征和偏最小二乘回归：预测水中溶解有机碳的模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Spectro-fluorescence signature (SFS) of water samples contains information that may be used to quantify dissolved organic carbon (DOC) if combined with multivariate analyses. A model was built through SFS and partial least squared (PLS) regression. The SFSs of 219 samples of natural water along the Raritan River and Millstone River watersheds located in central New Jersey, and their corresponding DOC concentrations were used to build the model. Calibration, full cross-validation, and prediction performances of various models were statistically compared before optimal model selection. The final selected model, tested on the Passaic River watershed in northern New Jersey, provided a bias of 0.028 mg/l and a root mean squared error of prediction (RMSEP) of 0.35 mg/l. Linked to PLS, SFS can be a quality and cost effective method to perform on-line rapid DOC measurements.
机译：如果与多变量分析相结合，水样品的光谱荧光特征（SFS）包含可用于量化溶解有机碳（DOC）的信息。通过SFS和偏最小二乘（PLS）回归建立了模型。使用位于新泽西州中部的Raritan河和Millstone河流域的219个天然水样本的SFS及其相应的DOC浓度来构建模型。在选择最佳模型之前，对各种模型的校准，完全交叉验证和预测性能进行了统计比较。最终选择的模型在新泽西州北部的Passaic河流域上进行了测试，其偏差为0.028 mg / l，预测均方根误差（RMSEP）为0.35 mg / l。与PLS链接后，SFS可以成为执行在线快速DOC测量的一种优质且经济高效的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号