CINF 18 Maximizing chemical knowledge: New approaches in spectral data mining and search via the successful consolidation of multi-technique spectral data
Gregory M. Banik¹, Deborah Kernan², Kevin Scully³, and Marie Scandone³. (1) Bio-Rad Laboratories, Informatics Division, 3316 Spring Garden Street, Philadelphia, PA 19104, gregory_banik@bio-rad.com, (2) Bio-Rad Laboratories, Informatics Division, (3) Informatics Division, Bio-Rad Laboratories, Inc

It has become standard practice in multiple applications, such as compound verification or unknown sample identification, for scientists to run a sample and, using spectral search software, compare it to commercial and/or proprietary reference databases of spectra. The software mines the reference data and calculates a score or hit quality index (HQI) to describe the correlation or “closeness” of the match between the spectrum being examined and the spectra of known compounds in reference databases.

This paper describes a new approach to spectral searching which gives scientists who analyze samples using multiple spectral techniques the ability to simultaneously combine all spectral information available to yield a single search result. In a series of case studies, we will demonstrate how this approach enables the optimization of chemical similarity and maximizes chemical knowledge in order to identify several unknown samples.