Fingerprint Vectors Research Articles

An Approach to Comparative Analysis of Chromatographic Fingerprints for Assuring the Quality of Botanical Drugs.

The present study was focused on developing the chemometric methods for analysis of the chromatographic fingerprint to control the quality of botanical drugs, which has gained attention in Asia and other countries. We developed a novel approach to generate a set of fingerprint features, called Fisher components (FCs) that were extracted from the chromatographic fingerprint. The method greatly reduces the dimensionality of the fingerprint vector, and the resulting FCs still retain most discriminatory information of the original fingerprint. Choosing an example of relevance to contemporary botanical drugs, we applied the FCs to a set of Shenmai injection samples. We successfully identified the manufacturers of the samples using two classifiers, linear discriminant analysis (LDA) and k-Nearest Neighbor (k-NN) based on the FCs. We also applied a similarity assessment together with the visual analysis using the FCs to exam the products from different manufacturers. We found that the lot-to-lot consistency of p...

Clustering and similarity of chemical structures represented by binary substructure descriptors

A set of 1365 substructures has been created for the representation of organic chemical structures by binary substructure descriptors. Software SubMat calculates a matrix of binary fingerprint vectors for given sets of molecular structures and substructures (both in Molfile format). The distribution of the substructures in two spectral databases (IR, MS) has been investigated. Examples of structure similarity searches based on the Tanimoto index are presented. Cluster analyses of chemical structures by principal component analysis (PCA) have been performed for hitlists from spectral similarity searches.

Optimal Fingerprints for the Detection of Time-dependent Climate Change

An optimal linear filter (fingerprint) is derived for the detection of a given time-dependent, multivariate climate change signal in the presence of natural climate variability noise. Application of the fingerprint to the observed (or model simulated) climate data yields a climate change detection variable (detector) with maximal signal-to-noise ratio. The optimal fingerprint is given by the product of the assumed signal pattern and the inverse of the climate variability covariance matrix. The data can consist of any, not necessarily dynamically complete, climate dataset for which estimates of the natural variability covariance matrix exist. The single-pattern analysis readily generalizes to the multipattern case of a climate change signal lying in a prescribed (in practice relatively low dimensional) signal pattern space: the single-pattern result is simply applied separately to each individual base pattern spanning the signal pattern space. Multipattern detection methods can be applied either to test the statistical significance of individual components of a predicted multicomponent climate change response, using separate single-pattern detection tests, or to determine the statistical significance of the complete signal, using a multivariate test. Both detection modes make use of the same set of detectors. The difference in direction of the assumed signal pattern and computed optimal fingerprint vector allows alternative interpretations of the estimated signal associated with the set of optimal detectors. The present analysis yields an estimated signal lying in the assumed signal space, whereas an earlier analysis of the time-independent detection problem by Hasselmann yielded an estimated signal in the computed fingerprint space. The different interpretations can be explained by different choices of the metric used to relate the signal space to the fingerprint space (inverse covariance matrix versus standard Euclidean metric, respectively). Two simple natural variability models are considered: a space-time separability model, and an expansion in terms of P0Ps (principal oscillation patterns). For each model the application of the optimal fingerprint method is illustrated by an example.