基线对傅里叶变换红外光谱的目标因子分析结果的影响研究  

Influence of Baseline on Target Factor Analysis Results of Fourier Transform Infrared Spectroscopy

作　　者：王丽 邵利民[1] WANG Li;SHAO Li-Min(Department of Chemistry,University of Science and Technology of China,Hefei 230026,China)

机构地区：[1]中国科学技术大学化学与材料科学学院,合肥230026

出　　处：《分析化学》2021年第3期474-481,共8页Chinese Journal of Analytical Chemistry

摘　　要：目标因子分析(TFA)在不经过任何预处理的情况下,能否成功地从实验数据矩阵中提取目标光谱主要取决于相应组分浓度的方差。当氨的浓度方差较低、基线漂移较严重时,TFA可能给出假阴性结果,因此,需要校正基线,以提高TFA性能。本研究采用6组不同浓度方差、不同基线漂移程度的大气开放光路傅里叶变换红外(OP/FT-IR)光谱数据,比较标准正态变换(SNV)、多元散射校正(MSC)、线性拟合、小波变换(WT)和自适应迭代重加权惩罚最小二乘(airPLS)法去除基线的效果。结果表明,小波变换能够有效去除基线,从而使氨和甲烷光谱信息提前出现在PCA的特征向量中,显著提高了目标因子分析的定性能力。The successful extraction of target spectra from the experimental data matrix by target factor analysis(TFA)without any preprocessing mainly depends on the concentration variance of the corresponding component.When the variance of ammonia concentrations is low and the baseline drift is serious,TFA may yield false negative results,so baseline correction is applied to improve TFA performance.In this study,six sets of data with different concentration variances and baseline drift were used to compare the baseline removal effects of standard normal variate(SNV),multiplicative scatter correction(MSC),linear fitting,wavelet transform(WT)and adaptive iterative reweighted penalty least squares(airPLS).The results showed that WT could effectively remove the baseline,so that more spectral information of ammonia and methane was allocated in the first eigenvectors of principal component analysis(PCA).The allocated information of ammonia and methane could be successfully extracted by TFA.

关 键 词：目标因子分析 主因子数 基线校正 小波变换 

分 类 号：O657.33[理学—分析化学]