Advanced Preprocessing Methods for Optimization of Chemometric Algorithms for Gas Chromatography.

摘要

Preprocessing algorithms have a huge impact on the analytical precision and accuracy of chemometric methods used to reduce the data collected on chromatographic instrumentation to chemical information that is useful to the analyst. Piecewise alignment is one preprocessing tool to maximize the retention time precision of chromatographic data and improve the chemometric analysis results. The algorithm is used with Gas chromatography with a flame ionization detector (GC-FID) and gas chromatography with mass spectral detection (GC-MS). These instruments are used to study how well the data can not only be aligned, but how much information can be gleaned from the data and how fast the process can be run. Parallel factor analysis (PARAFAC) can be used in conjunction with alignment to extract information from stacked GC-MS data Separations of 13C labeled signals from 12C standard signals for metabalomic data show that even with almost no separation in the chromatographic dimension, a chromatographic data stacking procedure used to increase the dimensionality of the GC-MS data can be used to separate these components as long as there is proper experimental design and adequate retention time precision. Alignment algorithms can take a significant portion of the total computation time of the data analysis process. Therefore, optimization of computational time is performed by minimizing the number of data points for the separation. This minimization of data is performed using boxcar averaging as a form of data reduction, which impacts the signal to noise ratio (S/N), the size of the data and the speed of the algorithms for alignment. Results indicate that data can be reduced to as low as 15 points defining the peak width at the base prior to alignment without significant loss of chemical information. Data reduction can also be used to increase the S/N of isothermal separations. Since isothermal GC separations have linearly increasing peak widths, an increasing boxcar averaging algorithm is developed to reduce the last peak width to be the same width as the first peak, with a concurrent S/N increase with increasing boxcar size. The method for isothermal GC separations, referred to as temporally increasing boxcar summation (TIBS) uses calibration standards to calculate the boxcar window size to be adjusted as a function of the retention time. All of these data analysis tools are designed to increase the speed, precision and accuracy of chromatographic data analysis.