Abstract.
With the current popularity of second-order (or hyphenated) instruments, there now exists a number of chemometric techniques
for the so-called second-order calibration problem, i.e. that of quantifying an analyte of interest in the presence of one (or more)
unknown interferent(s). Second-order instruments produce data of varying complexity, one particular phenomenon sometimes
encountered being that of rank overlap (or rank deficiency), where the overall rank of the data is not equal to the sum of the
ranks of the contributing species. The purpose of the present work is to evaluate the performance of two second-order
calibration methods, a least squares-based and an eigenvalue-based solution, in terms of their quantitative ability and stability, as
applied to flow injection analysis (FIA) data which exhibits rank overlap. In the presence of high collinearity in the data, the
least squares methods is found to give a more stable solution. Two-mode component analysis (TMCA) is used to investigate
the reasons for this difference in terms of the chemical properties of the species analysed. The success of second-order
calibration of this data is found to depend strongly on the collinearity between the acidic and basic time profiles and the
reproducibility of the pH gradient in the FIA channel, both of which are shown to be related to the pK(a) values of the species.

Keywords.
Flow Injection Analysis; Second-Order Calibration Methods; Two-Mode Component Analysis; Rank Overlap.

Keywords Plus.
Annihilation Factor Analysis; Restricted Tucker Models; 2nd-Order Calibration; Resolution.