摘要
经典最小二乘法(CLS)是傅里叶红外光谱定量分析软件中的一种常用算法。统计学中,当待测信号中的误差为正态分布零均值等方差时,CLS法可以得到最优无偏估计,但对异方差误差则不然。傅里叶红外光谱仪(FTIR)吸光度谱中的噪声相当于异方差误差,通过对噪声源的理论分析和实验统计计算获得了噪声的方差分布,并以此对CLS方法进行加权修正。计算表明,在分析气体中挥发性有机化合物(VOCs)污染浓度时,相比CLS方法,采用仪器噪声方差分布修正后的加权最小二乘法(WLS)可以显著降低噪声对定量精度的影响,提高了分析结果的准确性和可靠性。
The Classical Least Square regression（CLS）is one of the most popular regression methods in FTIR quantitative estimation.However,CLS is the best unbiased estimator only under the assumption that error（noise）in the spectrum has equal variance,which usually is not the case in FTIR.This paper proposed a noise calibration method for FTIR spectrum analysis.Based on measured variance of noise in the FTIR spectrum by computer,the Weighted Least Square regression（WLS）method is used in quantitative estimation.The experiment results showed that the WLS performs much better than CLS in quantitative estimation of VOCs pollution.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2015年第5期1199-1202,共4页
Spectroscopy and Spectral Analysis
基金
国家重大仪器专项(2013YQ060615)资助
关键词
傅里叶红外光谱仪
噪声
最小二乘法
加权最小二乘法
Fourier transform infrared spectroscopy
Classical least square regression
Weighted least square regression
Noise to signal ratio
