Accuracy of peak deconvolution algorithms within chromatographic integrators

The soundness of present-day algorithms to deconvolve overlapping skewed peaks was investigated. From simulated studies based on the exponentially modified Gaussian model (EMG), chromatographic peak area inaccuracies for unresolved peaks are presented for the two deconvolution methods, the tangent skim and the perpendicular drop method. These inherent inaccuracies, in many cases exceeding 50%, are much greater than those calculated from ideal Gaussian profiles. Multiple linear regression (MLR) was used to build models that predict the relative error for either peak deconvolution method. MLR also provided a means for determining influential independent variables, defining the required chromatographic relationships needed for prediction. Once forecasted errors for both methods are calculated, selection of either peak deconvolution method can be made by minimum errors. These selection boundaries are contrasted to method selection criteria of present data systems' algorithms.