A novel systematic absence of cross peaks-based 2D-COS approach for bilinear data

A novel approach to use two-dimensional correlation spectroscopy (2D-COS) to analyze bilinear data is proposed. A phenomenon called Systematic Absence of Cross Peaks (SACPs) is observed in a 2D asynchronous spectrum. Two theorems relevant to SACPs have been derived. The SACP-based 2D-COS method has been successfully applied on analyzing bilinear data from mixed samples (including one model system and two real systems). Implicit isolated peaks can be identified and assigned to different components based on characteristic pattern of SACPs even if the time-related profiles of different components are severely overlapped. Based on the results of SACPs, spectra of pure components can be retrieved. Identification of SACPs can still be achieved in the presence of artifacts. Thus, neither noise nor baseline drift can produce significant influence on the results obtained from the approach described in this paper. We have used several well-established chemometric methods, including N-Findr, VCA, and MCR with various initial settings, on two systems that can be successfully solved using the 2D-COS method. The chemometric methods mentioned above cannot provide correct spectra of pure components because of severe problem of rotational ambiguity derived from severe overlapping of the time-related profiles. Only when the information from SACPs in 2D-COS is used as additional constraints in MCR calculation, correct spectra can be obtained. That is to say, the SACP-based 2D-COS method provides intrinsic information which is crucial in the analysis of chromatographic-spectroscopic and analogous data even if the time-related profiles of different components overlap severely.

A preliminary study on constructing a high-dimensional asynchronous spectrum to analyze bilinear data

A novel approach to constructing high-dimensional asynchronous spectra (nD-Asyn) is proposed. Three theorems relevant to 1D slices of nD-Asyn are revealed. nD-Asyn is used to analyze bilinear data from mixtures containing multiple components obtained via hyphenated techniques. The spectral contribution of different components can be removed in a stepwise manner by increasing the dimensions of asynchronous spectra. As a result, the spectra of different components can be faithfully recovered even if the time-related profiles of different components severely overlap. Moreover, correct results can still be obtained via the nD-Asyn even if a considerable level of noise and baseline drift are present. The nD-Asyn approach is compared with MCR-ALS using different constraints in analyzing the data for a simulated and also for a real system. The nD-Asyn produced correct spectrum of every component. Only when complete constraints obtained from nD-Asyn method is utilized in the MCR-ALS calculation, correct spectra of all the components can be obtained. Thus, nD-Asyn can be used alone or in conjunction with MCR-ALS to analyze bilinear data containing contributions of multiple components.

The use of alteration analysis in supercritical fluid chromatography to monitor changes in a series of chromatograms

Two-dimensional correlation analysis (2DCOR) is a unique chemometric method introduced in spectroscopy which became successful in a wide range of analytical fields. It was applied in chromatography as well but has not gained wide-spread popularity. In our previous work, we introduced an alternative method, Alteration Analysis (ALA), which is built upon the basic properties of 2DCOR, but it is fine-tuned for chromatographic applications and can be used on higher dimensional data sets as well. We explored its merits through computer generated examples. In this study, we present the application of ALA to two various data-sets in chromatography. First, we used a series of samples where the concentrations of the compounds were adjusted according to the changes we studied in our previous in-silico experiments. We compared the alteration maps from the computer generated and measured sources. The results demonstrated that ALA can provide the same properties from measured data as laid down in theory. The second one is a test concerning the effect of sample solvent composition in supercritical fluid chromatography (SFC). ALA maps show the influence of increasing methanol concentration on the peak location and shape of compounds in the chromatogram. With these two examples, we demonstrate that ALA can be used not only in theory, but it has also practical potentials and importance.