四种多变量校准方法在FTIR多组分分析中的性能比较

本文对四种多变量校准方法－－经典最小二乘法（CLS），偏最小二乘法（PLS），卡尔曼滤波法（KFM）以及人工神经网络法（ANN）－－在多组分浓度分析方面的性能进行了比较。选择五种红外谱图严重混叠的大气有机毒物－－1，3－丁二烯，苯，邻二甲苯，氯苯和丙烯醛－－作为分析对象。分别计算各种方法对该5组分体系的平均预测误差MPE和平均相对误差MRE进行比较。结果表明，偏最小二乘法在处理这类问题中是最稳健的方法。

Comparison of Four Multivariate Calibration Methods in Simultaneous Determination of Air Toxic Organic Compounds with FTIR Spectroscopy

The concentration determination abilities of four multivariate calibration methods--classical least squares (CLS), partial least squares (PLS), kalman filter method (KFM) and artificial neural network (ANN) were compared in this paper. Five air toxic organic compounds--1,3-butadiene ,benzene,o-xylen,chlorobenzene,and acrolein--whose FTIR spectra seriously overlap each other were selected to compose the analytical objects. The evaluation criterion was according to the mean prediction error (MPE) and mean relative error (MRE). Results showed that PLS was superior to other methods when treating multicomponent analysis problem, while there was no comparable difference between CLS, KFM and ANN.