SUMMARYThe direct trilinear decomposition method (DTDM) is an algorithm for performing quantitative curve resolution of three-dimensional data that follow the so-called trilinear model, e.g. chromatography-spectroscopy or emission-excitation fluorescence. Under certain conditions complex eigenvalues and eigenvectors emerge when the generalized eigenproblem is solved in DTDM. Previous publications never treated those cases. In this paper we show how similarity transformations can be used to eliminate the imaginary part of the complex eigenvalues and eigenvectors, thereby increasing the usefulness of DTDM in practical applications. The similarity transformation technique was first used by our laboratory to solve the similar problem in the generalized rank annihilation method (GRAM). Because unique elution profiles and spectra can be derived by using data matrices from three or more samples simultaneously, DTDM with similarity transformations is more efficient than GRAM in the case where there are many samples to be investigated.

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The multiplex polymerase chain reaction (PCR) technique was applied to detect the SARS-CoV (severe acute respiratory syndrome-associated coronavirus) specific target cDNA fragments in the present study. The target cDNA fragments of SARS-CoV were synthesized artificially according to the genome sequence of SARS-CoV in GenBank submitted by The Chinese University of Hong Kong, and were used as simulated positive samples. Five primers recommended by World Health Organization (WHO) were used to amplify the fragments by single PCR and multiplex PCR. Three target cDNA fragments (121, 182 and 302 bp), as well as the three different combinations of any two of these fragments, were amplified by single PCR. The combination of these three fragments was amplified by multiplex PCR. The results indicated that the multiplex PCR technique could be applied to detect the SARS-CoV specific target cDNA fragments successfully.