Morphological weighted penalized least squares for background correction.

Backgrounds existing in the analytical signal always impair the effectiveness of signals and compromise selectivity and sensitivity of analytical methods. In order to perform further qualitative or quantitative analysis, the background should be corrected with a reasonable method. For this purpose, a new automatic method for background correction, which is based on morphological operations and weighted penalized least squares (MPLS), has been developed in this paper. It requires neither prior knowledge about the background nor an iteration procedure or manual selection of a suitable local minimum value. The method has been successfully applied to simulated datasets as well as experimental datasets from different instruments. The results show that the method is quite flexible and could handle different kinds of backgrounds. The proposed MPLS method is implemented and available as an open source package at http://code.google.com/p/mpls.

Nonlinear alignment of chromatograms by means of moving window fast Fourier transfrom cross-correlation.

The preprocessing of chromatograms is essential to modern chromatography for further qualitative and quantitative analysis, especially when chromatographic instruments are used for herb products analysis involving large number of samples. To accurately compare and analyze the obtained chromatograms, it is necessary to preprocess, especially align retention time shifts. Here moving window fast Fourier transform (FFT) cross-correlation is introduced to perform nonlinear alignment of high-throughput chromatograms. Since elution characteristics of chromatograms will produce local similarity in retention time shifts, moving window procedure seems to be a better substitute of segmentation steps. The retention time shifts can be calculated and accelerated by FFT cross-correlation. The artifacts can be detected and eliminated from the retention time shifts profile since the continuity of moving window procedure. The proposed method is demonstrated in comparison with recursive alignment by FFT on chromatographic datasets from herb products analysis. It is shown that the proposed method can address nonlinear retention time shift problem in chromatograms with the simple moving window procedure, which will not introduce segments size optimization problem. In additional, the parameters are intuitive and easy to adjust, which makes it off-the-shelf toolbox for alignment of chromatograms.

Application of fast Fourier transform cross-correlation and mass spectrometry data for accurate alignment of chromatograms.

Chromatography has been established as one of the most important analytical methods in the modern analytical laboratory. However, preprocessing of the chromatograms, especially peak alignment, is usually a time-consuming task prior to extracting useful information from the datasets because of the small unavoidable differences in the experimental conditions caused by minor changes and drift. Most of the alignment algorithms are performed on reduced datasets using only the detected peaks in the chromatograms, which means a loss of data and introduces the problem of extraction of peak data from the chromatographic profiles. These disadvantages can be overcome by using the full chromatographic information that is generated from hyphenated chromatographic instruments. A new alignment algorithm called CAMS (Chromatogram Alignment via Mass Spectra) is present here to correct the retention time shifts among chromatograms accurately and rapidly. In this report, peaks of each chromatogram were detected based on Continuous Wavelet Transform (CWT) with Haar wavelet and were aligned against the reference chromatogram via the correlation of mass spectra. The aligning procedure was accelerated by Fast Fourier Transform cross correlation (FFT cross correlation). This approach has been compared with several well-known alignment methods on real chromatographic datasets, which demonstrates that CAMS can preserve the shape of peaks and achieve a high quality alignment result. Furthermore, the CAMS method was implemented in the Matlab language and available as an open source package at http://www.github.com/matchcoder/CAMS.