中文摘要：

采用小波包变换潜变量回归（WPLVR）方法，同时测定了钐和钇。该法结合小波包变换和潜变量回归改进了除噪质量。信号的小波包表述提供了一个局部时频描述，因此在小波域，除噪质量可以改善。潜变量是把小波包处理过的信号投影到正交基本征矢量上获得。潜变量可由原始变量的线性组合来表示。使用该法，人们可从没有选择性的全光谱数据中获得极有选择性的信息。通过最佳化，选择了小波函数及小波包分解水平（L）。编制了两个程序（PWPLVR）和（PFTLVR）执行WPLVR和傅里叶变换潜变量回归（FTLVR）法计算。试验结果表明两种方法都是成功的，且WPLVR法更优于FTLVR法。

英文摘要：

A wavelet packet transform latent variable regression （WPLVR）method was developed to perform simultaneous quantitative analysis of Sin（ Ⅲ ） and Y（ Ⅲ ）. The quality of the noise removal was improved by combining wavelet packet transform with latent variable regression （VLR）. Wavelet packet representations of signals provided a local time-frequency description, thus in the wavelet domain, the quality of the noise removal can be improved. The latent variables were made by projecting the wavelet packet processed signals onto orthogonal basis eigenvectors. The latent variable is expressible in term of linear combination of the original signals. By this method one can obtain highly selective information from unselective full-spectrum data. Through optimization, the wavelet function and wavelet packet decomposition levels （L） were selected. Two programs, PWPLVR and PFTLVR, were designed to perform WPLVR and Fourier transform latent variable regression （FTLVR） calculations. Experimental results showed that both methods were successful, but the WPLVR methed was better than FTLVR.