Resolution Of Overlapping GC-MS Signals For Multicomponent Analysis

Analysis of multicomponent systems is one of the most challenging problems in analytical chemistry. Hyphenated chromatographic techniques, such as gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography with diode array detector (HPLC-DAD), have been the powerful tools in the analysis of complex samples. With these analytical instruments, large amounts of high-dimensional and complex data were produced conveniently. For extraction of the information of components from overlapping multicomponent signals, it is very necessary to build new methods.Chemometric methods have been proposed for resolution of multicomponent overlapping signals. However, there are different problems in these methods. For example, the information of interested components can be extracted with the aid of target factor analysis (TFA) and non-negative IA (NNIA). However, the method can be used only in a limited number of cases. It is very important for window factor analysis (WFA) and heuristic evolving latent projection (HELP) to indentify the selective informations of components. Immune algorithm (IA) can extract the information of the components by iteratively eliminating standard information (chromatogram or mass spectrum) from overlapping GC-MS signals. The resolved chromatograms may not be correct, however, when there are differences between the measured signals of the standards and the mixture. In this dissertation, all complex samples were analysed by using GC-MS, and four methods based on IA and NNIA were proposed for resolution of overlapping GC-MS signals. All these works provide new ways for analysis of complex samples. The main contexts are as follows:(1) The information of interested components can be extracted with the aid of target factor analysis (TFA) and non-negative IA (NNIA) in a limited number of cases. A method based on IA for rapid analysis of mixture samples by using GC-MS was developed. All information of compoents can be extracted by the method with more standard signals than the components. According to the theory of IA, only the information of the components that really exist in the sample can be extract from the overlapping GC-MS signals. In the method, the measurement of GC-MS was achieved with a very fast temperature program, and then IA was adopted to obtain the information of each component in the overlapping signal. GC-MS data of16-organophosphorus pesticides mixture was investigated. The results show that the chromatographic information of all the components can be extracted from the overlapping signal eluted in10minutes.(2) When there are differences between the measured signals of the standards and the mixture, distortion and negative values will appear in the resolved chromatograms by using IA. In order to conquer the problem, an algorithm based on an alternative iteration of least squares fitting and IA was proposed in this work. In the method, the measurement of GC-MS was achieved with a very fast temperature program to make the analytes to elute within a short retention time period, and then the chromatographic and mass spectral information of the components in the overlapping signal is calculated with the proposed algorithm. In the calculation, the algorithm takes random mass spectra of the components as the starting input, and then the information of each component is obtained with an alternating iterative process. In the iteration, the mass spectra are calculated by using the least squares fitting and the chromatographic profiles are resolved by IA. The iteration stops when the remaining signal does not change. The method was proved by resolving the GC-MS data of the40-pesticide mixture, and the results show that both the mass spectra and the chromatographic information of the components were extracted from the overlapping signals.(3) The information of interest component can be extract from overlapping multicomponent signals with the selective ion. However, determination of selective ion is very difficulty. A chemometric method to determine selective ion by using NNIA for resolution of overlapping GC-MS signals was proposed in this work. In the method, the mutual projections of the chromatographic profiles at different m/z channel are calculated using NNIA, thus, n extracted mass spectra can be obtained. If the chromatographic profile at a selective mass channel is used, the extracted mass spectrum will be a correct one. Therefore, by comparing the extracted mass spectrum with a reference spectrum, the selective ion can be identified, and the corresponding chromatographic profile can be obtained at the same time. GC-MS data of40-pesticide mixture was investigated by the method. The results show that both the mass spectral and the chromatographic information of the interested components can be extracted from the overlapping signals.(4) Under different instruments and environments conditions, the library mass spectra may be different from the experimental mass spectra. With the incorrect mass information from the mass spectral library as the input of NNIA, the resolved chromatographic profiles may be distorted. A method based on iterative target transformation factor analysis (ITTFA) for correction of mass sepectra to resolve the overlapping GC-MS signals was proposed. In this work, ITTFA was employed for correction of the library spectra, and the corrected mass spectra were taken as the input of NNIA for resolution of overlapping GC-MS signals. Rapid analysis of16phthalic acid esters in water eluted within13minutes was achieved by the method. The results show that the match ratios between the corrected and experimental mass spectra are above800%o. Compared with the chromatographic information obtained with library spectra, the results obtained with the corrected mass spectra are more reliable.