Neural net system for analyzing chromatographic peaks

Abstract

A two-step process for characterization of peaks in a chromatogram is disclosed. Firstly, data corresponding to each peak or pair of peaks in the chromatogram is identified. A unique filter apparatus locates extrema of the curvature of the chromatographic data and a data file is generated containing characteristics of the extrema. A pattern recognition apparatus analyzes the characteristics of the located extrema and classifies the peak or peak combination represented by the data in the file as one peak or peak combination in a set of resolved peaks and selected combinations of resolved peaks. A portion of the chromatographic data, corresponding to the peak or peak combination identified by the pattern recognition apparatus, is identified. This portion includes the signal for said peak and the signal for the baseline upon which the peak is superimposed. In the second step, data for a peak or a peak combination identified as described above is processed and a set of characterizing parameters for the peak or the first peak in the peak combination is generated without a prior baseline correction to the data. The peak data including the baseline level upon which the peak is superimposed is analyzed using one of lookup-tables, neural nets, curve fitting, or combinations thereof. These characterization processes, using information about the peak crest and the peak inflection points, determines a set of characterizing parameters and a baseline estimate that best fit the identified data. The peak characterization is not biased by a prior baseline correction.

Dependencies are inferred from the claim text.