摘要
在近红外光谱定量分析技术中,多元散射校正(MSC)算法可以有效地剔除由于样品颗粒度、装填密度、湿度等物理因素所导致的散射影响,有效地提高了光谱的信噪比。相关光谱法反映了样品待测成分光谱信息和浓度信息之间的线性相关性,在定标波长优选过程中发挥了重要作用。然而采用单一波长通道一元线性回归计算得到的相关光谱极易受到散射的影响,掩盖了待测成分的特征线性信息,将多元散射校正技术用于相关光谱的信息提取和噪声压制,克服了上述的困难,并通过人参样品的定标实验验证,得到了良好的效果和满意的定标结果。
Multiple scattering correction（MSC） algorithm can be used effectively to remove the effect of scattering due to the physical factors such as the density and humidity of sample granule, and as a result the ratio of signal to noise is improved greatly. Meantime correlation spectrum plays a important role in the choice of optimum wavelength set because it describes the linear correlationship between the absorbance and concentration of the sample＇s ingredient under analysis. However, the correlation spectrum obtained by unitary linear regression（ULR） at single wavelength channel can be easily affected by the scattering so as to cover up the characteristic linear information of the sample. In order to solve the problem in the present paper MSC was applied to obtain useful signal and suppress noise of correlation spectrum. Through the careful calibration experiment of ginseng sample this idea has proved to be correct, and satisfactory result was obtained.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2007年第1期58-61,共4页
Spectroscopy and Spectral Analysis
基金
吉林省科技发展计划项目(20040324-2)
辽宁省自然科学基金项目(001064)
大连民族学院博士启动基金项目(20056207)资助
关键词
近红外
多元散射校正
相关
定标
NIR
Mutiple scattering correction（MSC）
Correlation
Calibration
