Features for non-targeted cross-sample analysis with comprehensive two-dimensional chromatography

Abstract

This review surveys different approaches for generating features from comprehensive two-dimensional chromatography for non-targeted cross-sample analysis. The goal of non-targeted cross-sample analysis is to discover relevant chemical characteristics (such as compositional similarities or differences) from multiple samples. In non-targeted analysis, the relevant characteristics are unknown, so individual features for all chemical constituents should be analyzed, not just those for targeted or selected analytes. Cross-sample analysis requires matching the corresponding features that characterize each constituent across multiple samples so that relevant characteristics or patterns can be recognized. Non-targeted, cross-sample analysis requires generating and matching all features across all samples. Applications of non-targeted cross-sample analysis include sample classification, chemical fingerprinting, monitoring, sample clustering, and chemical marker discovery. Comprehensive two-dimensional chromatography is a powerful technology for separating complex samples and so is well suited for non-targeted cross-sample analysis. However, two-dimensional chromatographic data is typically large and complex, so the computational tasks of extracting and matching features for pattern recognition are challenging. This review examines five general approaches that researchers have applied to these difficult problems: visual image comparisons, datapoint feature analysis, peak feature analysis, region feature analysis, and peak-region feature analysis.