中文摘要：

与 multivariate chemometrics 信号处理相结合的分光镜的技术非破坏地，与很少或不为代谢物的快速的多维的分析答应新工具的拉曼的申请样品准备和很少敏感到水。然而，散布的瑞利，荧光和不受管束的变化在生物学上改变样品在生理的层次为代谢物的精确定量分析提出实质的挑战。有效策略为减少拉曼包括 chemometrics 预告的处理的申请光谱干扰。然而，单个或联合的预告的处理过程的任意的申请能显著地改变大小的结果，从而复杂化光谱分析。这份报纸评估并且为改正基线变化比较六个信号预告的处理方法，都在基于部分最少的广场的 multivariate 刻度模型的上下文以内，和为消除 uninformative 变量的三个可变选择方法(请) 回归。有在在生理附近的集中的八尿代谢物的 90 件人工的简历液体样品的拉曼系列被用来测试这些模型。联合趋于增加散布修正(MSC ) ，连续小浪变换(享特威) ，随机化测试(RT ) 并且请当模特儿为所有代谢物介绍了最好的表演。在象 0.96 一样高到达的预言并且准备的集中之间的关联系数(R) 。

英文摘要：

The application of Raman spectroscopic techniques combined with multivariate chemometrics signal processing promise new means for the rapid multidimensional analysis of metabolites non-destructively, with little or no sample preparation and little sensitivity to water. However, Rayleigh scattering, fluorescence and uncontrolled variance present substantial challenges for the accurate quantitative analysis of metabolites at physiological levels in bio- logically varying samples. Effective strategies include the application of chemometrics pretreatments for reducing Raman spectral interference. However, the arbitrary application of individual or combined pretreatment procedures can significantly alter the outcome of a measurement, thereby complicating spectral analysis. This paper evaluates and compares six signal pretreatment methods for correcting the baseline variances, together with three variable se- lection methods for eliminating uninformative variables, all within the context of multivariate calibration models based on partial least squares （PLS） regression. Raman spectra of 90 artificial bio-fluid samples with eight urine metabolites at near-physiological concentrations were used to test these models. The combination of multiplicative scatter correction （MSC）, continuous wavelet transform （CWT）, randomization test （RT） and PLS modeling pre- sented the best performance for all the metabolites. The correlation coefficient （R） between predicted and prepared concentration reached as high as 0.96.