Abstract: Based on the principal of immune algorithm, a novel algorithm for chemical factor analysis was proposed, and called immune principal component analysis(IPCA). The basic idea of the proposed method is that it takes a data matrix as antigen, the retrieving eigen vector as antibody, and the orthogonal decomposition of the matrix can be achieved by an iteration of subtracting the principal component represented by the eigen vector, simulating the process of the interaction between antibody and antigen in an immune system. Comparing with the conventional principal component analysis, similar results were obtained. But if we combine the IPCAalgorithm with window factor analysis(WFA) technique, it will be more suitable for resolution of overlapping HPLC-DADsignals than the conventional WFAtechnique. Both simulated and experimental data sets were investigated. Similar results are obtained by the IPCAand PCAfor principal component analysis, but the IPCA-WFAis superior to the conventional WFAin resolution of the multicomponent overlapping HPLC-DADsignals.

Key words: Immune algorithm, Principal component analysis, Factor analysis, Resolution of overlapping signal