摘要
使用近红外光谱仪获取由高岭土、白云母和蒙脱石三种岩石矿物粉末混合成的模拟天然岩石样本的近红外漫反射光谱信息,通过标准归一化(standard normal variable)的方法对光谱数据进行预处理,采用随机森林(random forest)进行数学建模,对岩石样本的组成成分进行预测,预测得到三种岩石成分最小均方根误差分别为:0.088 0,0.095 6,0.121 2。实验结果表明应用近红外漫反射光谱来测定天然岩石中各种矿物成分的含量是可行的,为今后岩石成分的快速检测提供了理论依据。
The infrared reflectance spectroscopy from the sample simulating natural-rock prepared by kaolin,muscovite and montmorillonite mixed-powders was obtained by a spectrometer.Spectral data preprocessing was done using SNV.Random forest mathematical modeling was used for predicting the components of rock samples.The smallest root mean square error of the predicted three types of rock composition were 0.088 0,0.095 6 and 0.121 2 respectively.The predictive studies showed that the application of near infrared diffuse reflectance spectroscopy to determining the content of the natural rocks and minerals of various rock composition is feasible.The study provides a theoretical basis for the rapid detection of the rock composition in the future.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2013年第1期85-88,共4页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(60972130)
国家重点实验室项目(PLC200902)资助
关键词
近红外漫反射光谱
标准归一化
随机森林
最小均方根误差
岩石矿物
Near-infrared diffuse reflectance spectroscopy
SNV
Random forest
Minimum root mean square error
Minerals of rock
