模型传递用于解析遥感傅里叶变换红外谱图

基于模型传递的原理，建立了一种可对存在背景干扰，谱峰严重混叠的遥感傅里叶变换红外(remote sensing Fourier transform infrared: RS-FTIR)谱图进行解析的方法。分别用4组分气体混合物的EPA红外标准谱图和RS-FTIR谱图，建立校正和预测模型，经过正交信号校正(OSC)处理后，用EPA红外谱图数据所建立的校正模型，对RS-FTIR谱图数据进行预测，得到的丙酮、甲醇、苯和三氯甲烷的均方根预测误差(RMSEP)分别为：0.008 5，0.018 0，0.064 0，0.002 8。未经OSC处理时的RMSEP依次是0.085 6，0.047 9，1.065 3，0.014 2。经优化，支集选择的方法为Kennard-Stone法，OSC在实现过程中循环次数为3时得到的预测结果最好。研究结果表明，该方法能够克服背景和校正模型给RS-FTIR监测大气污染物带来的制约。

Calibration Transfer Used for the Interpretation of RS-FTIR Spectrum

A technique of analysis for RS-FTIR (Remote Sensing Fourier Transform Infrared) spectrum was built based on the principle of calibration transfer. A system of four-component gas mixtures, whose FTIR spectra were overlapped seriously, was determined as the object in the present research. Its FTIR data from EPA and field measurement were used to set up calibration and prediction models, respectively. With OSC (orthogonal signal correction), the model built with data of EPA predicted values of field measurement. The RMSEP values of acetone, methanol, benzene and chloroform were 0.008 5, 0.018 0, 0.064 0 and 0.002 8, respectively, while without OSC correction the corresponding values were 0.085 6, 0.047 9, 1.065 3 and 0.014 2, respectively. With optimization, the Kennard-Stone method was used for subset selection, and the iteration in the process of OSC was carried out 3 times, yielding the best performance. The result indicates that this technique can overcome the limitation of background and calibration model, which restricts the application of RS-FTIR in air monitoring.