Development and evaluation of flexible empirical peak functions for processing chromatographic peaks.

This paper describes the results of developing and evaluating extremely flexible empirical peak-shaped functions for processing chromatographic peaks. The proposed peak functions were developed based on transformation of Gaussian function into two-step functions that separately describe the leading and trailing edges. The flexibility and capability of these models were achieved by the combination and empirical modifications of the leading and trailing edge functions. The flexibility of the models was evaluated by fitting them to 10 types of peak shapes generated by literature peak functions possessing asymmetry values from 1 to 2.8. Excellent fits were found between the proposed models and generated peak shapes, showing that the new peak functions are extremely flexible. Furthermore, the capability of the models to smooth noisy peaks was demonstrated by fitting them to noisy, exponentially modified Gaussian peaks with different noise levels (S/N ratio was ranged from 200 to 10). Finally, we conclude that the flexibility of these models can be used to establish "templates" to significantly aid in smoothing noisy peaks and peak deconvolution.