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Selectivity in separation science is defined as the extent to which a method can determine the target analyte free of interference. It is the backbone of any method and can be enhanced at various steps, including sample preparation, separation optimization and detection. Significant improvement in selectivity can also be achieved in the data analysis step with the mathematical treatment of the signals. In this manuscript, we present a new approach that uses mathematical functions to model chromatographic peaks. However, unlike classical peak fitting approaches where the fitting parameters are optimized with a single profile (one-way data), the parameters are optimized over multiple profiles (two-way data). Thus, it allows high confidence and robustness. Furthermore, an iterative approach where the number of peaks is increased at each step until convergence is developed in this manuscript. It is demonstrated with simulated and real data that this algorithm is: (1) capable of mathematically separating each component with minimal user input and (2) that the peak areas can be accurately measured even with resolution as low as 0.5 if the peak’s intensities does not differ by more than a factor 10. This was conclusively demonstrated with the quantification of diterpene esters in standard mixtures.

Iterative Multivariate Peaks Fitting—A Robust Approach for The Analysis of Non-Baseline Resolved Chromatographic Peaks