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A model is presented for examining the noise filtering effect of eigenvector reconstruction for 2D correlation analysis, based on the noise perturbation in functional principal component analysis (NPFPCA). The resultant reconstructed data from the smooth eigenvectors gave more consistent 2D correlation spectra by removing the correlation artifacts from noise, which in turn enable more accurate interpretation of the spectral variation.

A robust and accurate analytical methodology for low-content (<0.1%) quantification in the solid-state using Raman spectroscopy, subsampling, and chemometrics was demonstrated using a piracetam-proline model. The method involved a 5-step process: collection of a relatively large number of spectra (8410) from each sample by Raman mapping, meticulous data pretreatment to remove spectral artifacts, use of a 0-100% concentration range partial least-squares (PLS) regression model to estimate concentration at each pixel, use of a more accurate, reduced concentration range PLS model to calculate analyte concentration at each pixel, and finally statistical analysis of all 8000+ concentration predictions to produce an accurate overall sample concentration. The relative prediction accuracy was ∼2.4% for a 0.05-1.0% concentration range, and the limit of detection was comparable to high performance liquid chromatography (0.03% versus 0.041%). For data pretreatment, we developed a unique cosmic ray removal method and used an automated baseline correction method, neither of which required subjective user intervention and thus were fully automatable. The method is applicable to systems which cannot be easily analyzed chromatographically, such as hydrate, polymorph, or solvate contamination. [ABSTRACT FROM AUTHOR]

Cell culture media used in industrial mammalian cell culture are complex aqueous solutions that are inherently difficult to analyze comprehensively. The analysis of media quality and variance is of utmost importance in efficient manufacturing. We are exploring the use of rapid "holistic" analytical methods that can be used for routine screening of cell culture media used in industrial biotechnology. The application of rapid fluorescence spectroscopic techniques to the routine analysis of cell culture media (Chinese hamster ovary cell-based manufacture) was investigated. We have developed robust methods which can be used to identify compositional changes and ultimately predict the efficacy of individual fed batch media in terms of downstream protein product yield with an accuracy of ±0.13 g/L. This is achieved through the implementation of chemometric methods such as multi-way robust principal component analysis (MROBPCA), and n-way partial least-squares-discriminant analysis and regression (NPLS-DA and NPLS). This ability to observe compositional changes and predict product yield before media use has enormous potential and should permit the effective elimination of one of the major process variables leading to more consistent product quality and improved yield. These robust and reliable methods have the potential to become an important part of upstream biopharmaceutical quality control and analysis. [ABSTRACT FROM AUTHOR]

A method based on noise perturbation in functional principal component analysis(NPFPCA) is for the first time introduced to overcome the noise interference problem in two-dimensional correlation spectroscopy (2D-COS). By the systematic addition of synthetic noise to the dynamic multivariate spectral data, the functional principal component analysis (FPCA) described in this report is able to accurately determine which eigenvectors are representing significant signals instead of noise in the original data. This feature is especially useful for the data reconstruction and noise filtering. Reconstructed data resulted from the smooth eigenvectors can produce much more reliable 2D correlation spectra by removing the correlation artifacts from noise, which in turn enable more accurate interpretation of the spectral variations. The usefulness of this method is demonstrated with a theoretical framework and applications to the 2D correlation analyses of both simulated data and temperature-dependent reflection−absorption infrared spectra of a poly(3-hydroxybutyrate) (PHB) thin film. [ABSTRACT FROM AUTHOR]