Deconvolution method for the analysis of data resulting from analytical separation processes

A signal processing method in a signal processor for identifying sample zones in a signal including multiple overlapping sample zones resulting from a separation process comprising the steps of:

• receiving a signal generated by a detector which monitors the results of an analytical separation process, such signal representing a distribution of partially separated sample zones;
• providing a plurality of possible point-spread-functions of the signal;
• converting the signal and each of the point-spread functions from a time domain representation to a frequency domain representation by taking the Fourier transform of each;
• providing an estimate of a noise component of the signal;
• calculating a result signal A(f) for each point-spread-function, where ##EQU11## where D(f) is the Fourier transform of the signal, P(f) is the Fourier transform of the point-spread-function, P*(f) is the complex conjugate of P(f), and n is the noise component of the signal;
• converting the result signal from the frequency domain to the time domain by taking the inverse Fourier transform of the result signal A(f) for each value of the point-spread function to give a result signal in the time domain A(t);
• reporting the result signal A(t) in the time domain calculated using the largest value of the point-spread-function that provides a non-negative value of the result signal A(t).

A program storage device readable by a machine, tangibly embodying a program of instructions executable by a machine to perform method steps to identify sample zones resulting from a signal representing a plurality of partially separated sample zones, said method steps comprising:

• receiving a signal generated by a detector which monitors the results of an analytical separation process, such signal representing a plurality of partially separated sample zones;
• determining a point-spread-function of the signal;
• converting the signal and the point-spread function from a time domain representation to a frequency domain representation by taking the Fourier transform of each;
• determining a noise component of the signal;
• calculating the value of a result signal A(f) using the formula ##EQU12## where D(f) is the Fourier transform of the signal, P(f) is the Fourier transform of the point-spread-function, P*(f) is the complex conjugate of P(f), and n is the noise component of the signal;
• and
• converting the result signal A(f) from a frequency domain representation to a time domain representation by taking the inverse Fourier transform of the result signal.