Comprehensive two-dimensional liquid chromatography (LC× LC) generates information-rich but complex peak patterns that require automated processing for rapid chemical identification and classification. This paper describes a powerful approach and specific methods for peak pattern matching to identify and classify constituent peaks in data from LC× LC and other multidimensional chemical separations. The approach records a prototypical pattern of peaks with retention times and associated metadata, such as chemical identities and classes, in a template. Then, the template pattern is matched to the detected peaks in subsequent data and the metadata are copied from the template to identify and classify the matched peaks. Smart Templates employ rule-based constraints (e.g., multispectral matching) to increase matching accuracy. Experimental results demonstrate Smart Templates, with the combination of retention-time pattern matching and multispectral constraints, are accurate and robust with respect to changes in peak patterns associated with variable chromatographic conditions.
Bibliographical noteFunding Information:
The research described was supported by Grant Number 0450540 from the National Science Foundation and Grant Number 5R44RR20256 from National Center for Research Resources of the National Institutes of Health (NIH) and by a fellowship from the American Chemical Society to D.R. Stoll. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NSF nor the NIH.
- Chemical identification and classification
- Liquid chromatography
- Pattern matching
- Pattern recognition
- Two-dimensional chromatography