Cubic smoothing splines background correction in on-line liquid chromatography–Fourier transform infrared spectrometry

Toward absolute chemical composition distribution measurement of polyolefins by high-temperature liquid chromatography hyphenated with infrared absorbance and light scattering detectors

Chemical composition distribution (CCD) is a fundamental metric for representing molecular structures of copolymers in addition to molecular weight distribution (MWD). Solvent gradient interaction chromatography (SGIC) is commonly used to separate copolymers by chemical composition in order to obtain CCD. The separation of polymer in SGIC is, however, not only affected by chemical composition but also by molecular weight and architecture. The ability to measure composition and MW simultaneously after separation would be beneficial for understanding the impact of different factors and deriving true CCD. In this study, comprehensive two-dimensional chromatography (2D) was coupled with infrared absorbance (IR5) and light scattering (LS) detectors for characterization of ethylene-propylene copolymers. Polymers were first separated by SGIC as the first dimension chromatography (D1). The separated fractions were then characterized by the second dimension (D2) size exclusion chromatography (SEC) with IR5 and LS detectors. The concentrations and compositions of the separated fractions were measured online using the IR5 detector. The MWs of the fractions were measured by the ratio of LS to IR5 signals. A metric was derived from online concentration and composition data to represent CCD breadth. The metric was shown to be independent of separation gradients for an "absolute" measurement of CCD breadth. By combining online composition and MW data, the relationship of MW as a function of chemical composition was obtained. This relationship was qualitatively consistent with the results by SEC coupled to IR5, which measures chemical composition as a function of logMW. The simultaneous measurements of composition and MW give the opportunity to study the SGIC separation mechanism and derive chain architectural characteristics of polymer chains.

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.