High-throughput approach for analysis of multicomponent gas chromatographic-mass spectrometric signals

Hyphenated techniques such as gas chromatography-mass spectrometry (GC-MS) or high-performance liquid chromatography-mass spectrometry (LC-MS) produce a large amount of data in a form of two-way data matrix. It has been a great challenge to furthest extract the useful information from the data. In this work, a chemometric approach based on a modification of adaptive immune algorithm (AIA) was proposed for a high-throughput analysis of the multicomponent overlapping GC-MS signals. With the proposed method, the chromatographic profile of each component in an overlapping signal can be extracted independently and sequentially along the retention time. In order to show the efficiency of the method, a stimulated GC-MS data of six components with background and an experimental GC-MS data of 40 pesticides were investigated. It was found that the multicomponent overlapping GC-MS signals could be fast and accurately resolved. Furthermore, the quantitative property of the extracted information was also investigated. The correlation coefficients (r) between the peak area and the added volumes of the sample are in the range 0.9658-0.9953.