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...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.展开更多
基金Project supported by the National Natural Science Foundation of China （No. 20835002）, and International Science and Technology Cooperation Program of the Ministry of Science and Technology （MOST） of China （No. 2008DFA32250）, as well as the British Columbia Innovation Council and the Natural Sciences and Engineering Research Council of Canada.
文摘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.
